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SPRING 2020

SCIENTIA THE BAYLOR UNDERGRADUATE RESEARCH JOURNAL OF SCIENCE AND TECHNOLOGY

How might gender affect sleep quality among college students?

What kind of emotional impact does bingewatching have?

To what extent are there race dispartities in cancer clinical trials?

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Editor-in-Chief Sean Ngo Student Editorial Board Shubhneet Warar, Arvind Muruganantham, Tiffany Luan, Sanjana Ade, Isha Thapar, Timothy Domashevich, Joshua George, Sinchana Basoor, and Tooba Haris Faculty Review Board Tamarah Adair, Ph.D., Patrick Farmer, Ph.D., Linda Olafsen, Ph.D., and Dennis Johnston, Ph.D. Publishing Advisor Rizalia Klausmeyer, Ph.D., Baylor Office of Undergraduate Research Design Team Arvind Muruganantham, Tiffany Luan, Sanjana Ade, Isha Thapar, and Sinchana Basoor Funding and Support Rizalia Klausmeyer, Ph.D., Baylor Office of Undergraduate Research About the Covers The visual on the front cover of Scientia is a Transmission Electron Microscopy image of a mouse cochlea. This image was taken by Preston Simpson as part of an experiment in Dr. Simmons’ lab, where they study aging brain function related to hearing, balance, neurodegeneration, and neuro-immune responses. The visual on the back cover is an immunofluorescence stain of mouse kidney glomeruli and tubules taken by Arvind Muruganantham. The staining was done primarily to quantify VEGFR2, an endothelial cell proliferation inducing receptor, in varying tissue samples. It was done as part of a larger project to understand the mechanisms of white blood cell recruitment in the kidney. Have a cool image from your research that you think would make a great cover for future editions of Scientia? Email it to baylor. scientia@gmail.com with a brief description and your image could be featured!


IN THIS ISSUE

4 5

A Letter From the Scientia Editorial Board COVID-19: An Interview with Dr. Kelli Barr Isha Thapar

Original Research

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Sleep, Social Media, and Stress: Gender Disparities in Sleep Quality Among College Students

28

The Impact of Social Media on the Short-Term Memory of Teenagers

34

Frequency of Binge Watching and its Emotional Impact

38

Effects of Polypropylene on Tetrahymena Cell Counts, Swim Speed, and Vacuole Formation

Daniel Zeter, Chenlu Gao, Michael K. Scullin, Ph.D.

11

Effect of Using CBD Placebo Under Stress: Measuring Heart Rate, Mean Arterial Pressure, and Reported Stress Level Evelynne Morris, Ireland Buckley, Gabbi Marchelli, Dena Quigley, Ph.D.

16

Proneness to Smartphone Addiction in Relation to Morningness and Eveningness

22

The Utilization of Magnets in Laparoscopic Uterine Prolapse Repairs

Abel Thomas, Samantha Hodges

Katie Nelson, Pamela Miller, PhD.

Katie Soudek, Shawn J. Latendresse, Ph.D.

Noah Mendoza, Madison Ambrose, Abel Thomas, Rithvik Bartham, Samantha Hodges, Tamarah Adair, Ph.D.

Review Articles

42 49

Endothelial Cell Diversity & Heterogeneity Arvind Muruganantham

Brain Regions Involved in Hypnosis: Clinical Implications Sarah Hale, Gary R. Elkins, Ph.D.

Alicia R. Chen, Daphne T. Simo, Megan K. Taylor, Marty Harvill, Ph.D., Mojgan Parizi-Robinson, Ph.D.

Scientia 2020 | 1


Original Abstracts

57

Biology

Adjusting Sample Concentration within Dynamic Range for Improved Data Analysis using Vacuum Ultraviolet Automated LibraryIntegrated Deconvolution Shubhneet Warar, Anna Arvidson, Meredith Ehlmann, Nick von Waaden, Ian G. M. Anthony, Touradj Solouki, Ph.D.

Mosquito Surveillance Techniques and Results in Waco, TX

Analysis of breast cancer profiles in TCGA by TNBC subgrouping reveals a novel microRNA-specific cluster distinguishing tumor subtypes Rebecca Modisette, Joseph H. Taube, Ph.D.

60

Kamerin Smith, Peyton Mizell, Lauren Hoogenakker, Marty Harvill, Ph.D.

Batool Unar Syed, Carolyn Carper, Henry Lyons, Deborah Olayinka, Jason Pitts, Ph.D.

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URSA Award Winning Abstracts Anthropology

59

Genetic suppressors of str-2 serotonin response defects

Disparity of Race Reporting and Representation in Clinical Trials Leading to Cancer Dug Approvals from 2008 to 2018 Jonathan M. Loree, Seerat Anand, Arvind Dasari, Joseph M. Unger, Anirudh Gothwal, Lee M. Ellis, Gauri Varadhachary, Scott Kopetz, Michael J. Overman, Kanwal Raghav

Descriptive analysis of blunt force trauma from variations of hammers on bovine bones Shawn Cleaver, Timothy L. Campbell

Observable Effects of Water Salinity on Bone Tammy Wake, Katie Binetti, Ph.D.

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The Effects of Exposure to Various Frequencies of Noise on the Reproductive Rates of Drosophila melanogaster

Amy Kumar, Shiv Gakhar, Julian Harris-Quanquin, Shelby Story, Bill Vo, Henry Vo, Myeongwoo Lee, Ph.D.

61

Anopheles species identification in Ethiopia: Comparison of morphology and molecular based techniques Joseph Spear, Sae Hee Choi, Tamar E. Carter, Ph.D.

Chemistry and Biochemistry Comparing Basis Sets and Methods in Density Functional Theory to Optimize the Electronic Structure of Sodium Adducted Carbohydrates Meg E. McCutcheon, Emily D. Ziperman, Srinivas Pulipaka, Emvia I. Calixte, Elyssia S. Gallagher, Ph.D.


Communication Science & Disorders

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Effects of Simulated Therapeutic Horse-Riding on Speech Therapy in Adult Brain Injury Callie Terrell, Clare Kuhlmann, Kat Delgado, Donna C. Powell, Paul T. Fillmore, Ph.D., Kathy Whipple, Ph.D.

Binaural Frequency Discrimination: Implication for Bimodal and Electric Acoustic Stimulation Users Sophie Suri, Yang-Soo Yoon, Ph.D.

Family & Consumer Sciences

63

Tailoring Fashion Therapy (FT) for Mental Health Patients and their Needs Victoria McKenty, Jay Yoo, Ph.D.

Physics N-Cadherin Dimerization Attenuated by Cadmium at Calcium Concentration in Neural Synapses Garrett Williams, Zhenrong Zhang, Ph.D.

Student Spotlights

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Roger Neuberger Faculty Mentor: Daniel Romo,

Ph.D.

Tina Li Faculty Mentor: Erich Baker,

Ph.D.

Scientia 2020 | 3


A Letter from the Editors Sean Ngo, Shubhneet Warar, Arvind Muruganantham, Tiffany Luan, Sanjana Ade, Isha Thapar, Timothy Domashevich, Joshua George, Sinchana Basoor, and Tooba Haris

On behalf of the Editors of Scientia, welcome! It is with great enthusiasm that we present to you Scientia 2020, Baylor University’s annually-published undergraduate research journal for science and technology. The start of this year has certainly brought with it a multitude of challenges for people around the world. During these times, we hope that you continue to be safe and healthy wherever you are. We are fortunate to have an online platform through which we can continue to publish Scientia this year. Our goal remains the same: to provide a professional platform upon which undergraduates of Baylor University are able to publish personally conducted and outstanding research in the areas of science and technology. In order to achieve this, Scientia accepts articles and abstracts spanning a wide variety of disciplines that are selected by the editorial board each year. We also feature the award-winning abstracts from the previous year’s URSA Scholars Week and highlight students who are doing extraordinary research. In line with Baylor’s most recent initiatives to achieve R1 research status, we believe that promoting research here at Baylor is quintessential for the growth and development of undergraduate students who will go on to make great contributions to the fields of science and technology. For this reason, we have committed ourselves to reviewing, editing, and publishing the research that has been conducted by Baylor undergraduates. Scientia is a publication of Baylor Undergraduate Research in Science and Technology (BURST). We believe Scientia encapsulates the mission of BURST by increasing awareness of the amazing research done by students on campus. Students whose research is published in Scientia display an outstanding passion for innovation, and it is our hope that this passion ignites a spark in other students to pursue their own research endeavors. This past year, the Scientia Editorial Board has been able to undertake several new initiatives that we hope will continue to enrich the research experience for Baylor undergraduates. The first of these is the Baylor Undergraduate Research Newsletter, which serves to spotlight high-value information concerning research opportunities/internships on campus and abroad, further highlight unique student research experiences, and promote deeper engagement in science. As we continue forward, we hope to develop this publication further in order to better serve you. We are also pleased to announce that we are currently developing a new website solely dedicated to Scientia. There, future authors will be able to submit their articles, and viewers will be able to read all past editions of Scientia and the Newsletter. We anticipate that this will allow us to better connect with you all and make the process of submission easier and more straightforward than ever. None of this, of course, could be done without the wonderful work being done by the students here at Baylor University. Our primary focus continues to be on highlighting and publishing the best research articles and abstracts that our students have to offer. In that effort, we have been able to expand our scope for this year’s edition and were able to reach students from the Anthropology department and the Robbins College of Health and Human Sciences. We greatly encourage you to take a look at these abstracts within Scientia 2020. As editors, it has certainly been a privilege to collaborate with phenomenal student researchers and faculty editors. The editorial process has given us as well as the authors invaluable insight into the intricacies of a research publication and enabled us to learn new information from a wide variety of fascinating fields. It is incredibly rewarding to work alongside these authors and share in their joy as their research comes to fruition. We hope that even though we are apart at this time, this publication can serve to connect us, the Baylor scientific community. Enjoy! Sincerely, The Scientia Editorial Board

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The Pandemic that Took the World by Surprise: An Interview with Dr. Kelli Barr Isha Thapar

It is surreal to live in a world where everything seems to be at a standstill and the future seems so uncertain. However, viral pandemics are not novel events. Deadly pandemics like the Spanish Flu, HIV/AIDS, and H1N1 have affected millions of people around the world. Viruses essentially consist of genetic material enclosed in a protein shell. Viruses don’t satisfy all the characteristics of a living organism, as they cannot reproduce outside a host body. When a virus enters a host cell, it tricks the hijacked cell to transcribe and translate its genetic material, thereby producing millions of copies. Coronaviruses are named for the crown-like spikes on their surface. Coronaviruses tend to primarily affect animals. There are only seven strains known to infect humans, including this new virus: COVID-19 (‘CO’ stands for ‘corona,’ ‘VI’ for ‘virus,’ ‘D’ for disease, and ‘19’ for 2019, the year it started). Four of the strains cause common colds and the other three strains, MERS-CoV, SARSCoV, and COVID-19, cause severe illnesses with acute respiratory symptoms. Like the H1N1 virus that emerged in 2009, COVID-19 is highly contagious, as humans have not previously encountered it and thus have no immunity. However, unlike H1N1, COVID-19 is disproportionately affecting older adults. Scientia interviewed Baylor professor and tropical disease biologist, Dr. Kelli Barr, to gain an insight into the implications of the COVID-19 pandemic for the future of research and healthcare. Currently, Dr. Barr is researching the epidemiology and pathogenesis of vector-borne and zoonotic viruses. Specifically, she studies the epidemiology of arboviruses in hyperendemic areas. According to Dr. Barr, viral pandemics like smallpox, measles, and polio have recurred regularly, as have several flu pandemics such as the H1N1 outbreak only ten years ago. During the H1N1 pandemic, widespread testing helped monitor the spread of the disease, and the implementation of self-isolation protocols prevented hospitals from being overwhelmed. Dr. Barr further elaborated that with the present pandemic, we are not conducting widespread testing, which is detrimental to proper surveillance. She believes that more labs must be approved for testing in order to prevent the spread of the disease. In addition to inadequate testing, the spread of the virus can also be attributed to lack of social distancing. In China, for example, a severe lockdown limited the spread of disease. Some countries implemented strict social isolation to the extent that only one person was allowed to leave the house once a week to get food. The lack of social distancing in the US is partially due to the absence of a community oriented mentality, as well as high population density in many cities, such as New York City. The restrictiveness of social distancing required to quell the spread of disease is one reason that Dr. Barr states vector-borne diseases are often easier to control. The control of vector-borne diseases can be achieved through the spray of insecticides and application of insect repellant, while contagious diseases spread by human contact are extremely difficult to deal with. Many wonder if we could have been better prepared for this pandemic, possibly through earlier investment in research and vaccine development. However, Dr. Barr states that increased research could not have prepared us for this particular pandemic because COVID-19 is a zoonotic disease, meaning it is transmissible from animals to humans. There is simply no way to predict if and when a virus will jump species. This is the case with many previous pandemics as well. H1N1 came from pigs, SARS came from bats, and MERS came from camels. Dr. Barr emphasizes that we must have a good administrative infrastructure in place in order to quickly control the spread of a pandemic once it arises. It is of interest to note how this pandemic will change the future of research and healthcare. According to Dr. Barr, research and healthcare “are already evolving.” Millions of dollars are being funneled towards coronavirus research. We do not yet have a human vaccine for any of the coronaviruses. This is due to the fact that these zoonotic viruses jump between humans and animals and mutate very quickly, making the development of a vaccine difficult. However, the severity of the illness caused by this virus is serving as an impetus for researching new antivirals and new vaccines. In addition to influencing research, this pandemic will fundamentally change an average person’s perspective on disease control and healthcare. Dr. Barr is of the opinion that “this pandemic is showing people how much they can contribute to the spread of disease.” The widespread nature of this disease emphasizes the importance of washing hands and staying home if ill. This pandemic will cause people to realize the extent to which behavioral changes impact the spread of illness. Dr. Barr’s parting advice to students is to reference clinical data from trustworthy sources such as the CDC and the WHO when seeking information about the coronavirus. She warns that social media can sometimes be a misleading source of false information and should not be solely relied on. Many Baylor students will go on to become healthcare workers, researchers, and policy makers. When another pandemic arises, which it inevitably will, we will be on the forefront. This pandemic will strongly influence how we behave in the future. Increased investment in the development of antiviral drugs, formation of policy to ensure effective administration, and better social distancing techniques will all aid in an improved response to a future pandemic. This pandemic was not the first to shock the world, and it certainly won’t be the last.

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Original Research

Sleep, Social Media, and Stress: Gender Disparities in Sleep Quality Among College Students Daniel Zeter, Chenlu Gao, Michael K. Scullin, Ph.D.

Abstract Sleep is a process essential to learning and memory and is therefore important to college students. However, many college students, especially female college students, report poor sleep quality which may be affecting their academic performance. Additionally, many of the factors affecting sleep quality—perceived stress, social media, and caffeine consumption—are common in the lives of college students. We recruited a convenience sample of 1,120 Baylor University undergraduate students (Mean age = 19.99; 66% female) to investigate whether gender differences in stress, caffeine consumption, and social media usage explain gender differences in sleep quality. The results showed that females reported worse sleep quality than males (p = .001). Additionally, relative to males, females reported higher levels of perceived stress (p < .001), spent more time on social media (p < .001), and consumed more caffeine (p < .001). Higher levels of perceived stress, more time spent on social media before bed, and greater caffeine consumption all predicted worse sleep quality in both male and female students (ps ≤ .02). Mediation analyses confirmed that the effect of gender on sleep quality was significantly mediated by social media use, caffeine consumption, and perceived stress (ps < .05). Identifying these gender differences in factors affecting sleep quality, along with further exploration into their effects on academic performance could lead to an understanding of how specifically sleep affects college students in the classroom.

Introduction Sleep is a process known for improving learning and memory (Maquet, 2001), making it essential for success as a student. College students often struggle to find the proper time for quality sleep while balancing academic coursework, a social life, employment, and extracurricular activities. Consequently, a majority of college students have poor sleeping habits, and their lack of quality sleep is associated with lower academic performance (Gilbert, 2010). Tsai and Li (2004) showed that while both male and female college students have poor sleep quality, the sleep quality was worse in females. Additionally, they found that gender differences in grade performance were primarily attributed to social jetlag, or differences in sleep quality during weekday and weekend nights. Smarr and Schirmer (2018) recently concluded that greater social jetlag correlated with lower academic performance in college students. While social jetlag is surely a component of the poor sleep quality among college students, our study aimed to address gender differences in specific factors common in the lives of college students—perceived stress, social media usage, and caffeine consumption. Kashani, Eliasson, and Vernalis (2012) researched the sleep-stress mechanism and identified that increased perceived stress was correlated with worse sleep quality, specifically in shortened sleep durations and sleepiness throughout the day. Lund, Reider, Whiting, and Prichard (2010) found that perceived stress was the strongest predictor of poor sleep quality in college students. In a national survey of young adults by Levenson et al.

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(2016), increased social media usage had a strong association with poor sleep quality. Additionally, social media can be used as a distraction from daily stressors, combining the effects of social media use and perceived stress (Leung, 2006). Alternatively, Orben and Przybylski (2019) found little evidence for an association between screen-time and psychological wellbeing, suggesting that more research in this area is needed. Caffeine consumption has also been identified as a contributing factor to sleep problems and daytime sleepiness (Roehrs, 2008). Caffeine, an adenosine receptor blocking stimulant, naturally leads to sleep problems, especially when people choose to have caffeine in the evening. The daytime sleepiness may be due to habitual caffeine users having poorer sleep efficiency at night, and then using caffeine throughout the day to attempt to rid themselves of sleepiness. Existing research, however, uncovers little about gender differences in the college student population, and how these differences could contribute to the disparities in sleep quality across male and female college students. The purpose of our study was to identify factors related to sleep quality, and college students specifically (e.g. perceived stress, social media usage, and caffeine consumption). We hypothesized that female students would have poorer sleep quality and greater social jetlag than male students. Additionally, we hypothesized that the gender differences in sleep quality may be explained by differences in social media use, caffeine consumption, and perceived stress.


Methods

Materials The survey included the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), the Perceived Stress Scale (PSS) and a 36-question demographics and daily activities questionnaire. The PSQI is a nine-item questionnaire that measures an individual’s sleep quality over the past month (Buysse et al., 1989). There are seven scored components— subjective sleep quality, sleep latency, habitual sleep efficiency, sleep duration, sleep disturbances, sleeping medication, and daytime dysfunction—that are summed to give a PSQI global score. Each component score ranges from 0 to 3 and the summed global score ranges from 0 to 21, with higher scores meaning worse sleep quality and scores greater than 5 representing clinically poor sleep quality. After the PSQI, the participants completed a demographics questionnaire to report their GPA, major, height, weight, gender, ethnicity, weekday and weekend sleep habits, social media usage during the day and before bed, and exercise habits. In addition, participants reported the number of caffeinated beverages (i.e., coffee, espresso, tea, energy drinks, soda, and caffeine pills) they consumed during the previous week. We aggregated their reports to produce an estimate of each participant’s total caffeine consumption (de Mejia & Ramirez-Mares, 2004). The final questionnaire used was the PSS, which quantifies a participant’s perceived stress levels (Cohen, Kamarck, & Mermelstein, 1983). The total PSS scores range from 0 to 40, with higher scores meaning higher levels of stress and scores greater than 14 meaning a moderate level of stress. Procedures The surveys were administered by undergraduate and graduate researchers throughout an entire academic year. During each semester, we gave three surveys spaced out evenly to the same courses. For each course, we administered surveys at the beginning of the semester, around the time of mid-terms, and a week before the final week. Surveys were either completed on paper or online (i.e., via Qualtrics). Participants were allowed approximately 10 minutes to complete the whole survey.

Original Research

Participants We surveyed a convenience sample of Baylor University undergraduate students aged 18 years or older (N = 1,120) at up to six time-points across the 2018-2019 academic year. We administered the survey in Organic Chemistry, Human Physiology, Human Anatomy, Cognition, Introduction to Biology, and First-Year Experience courses. This study was approved by the Baylor University Institutional Review Board and all participants provided informed consent before completing the survey. During the consent process, the course instructor left the room and was kept blinded to which students completed the survey, until after semester grades were finalized. The sample was primarily composed of students in STEMrelated courses, the students were 66% female, and participants averaged 19.99 years old (SD = 1.21). There were 146 freshmen, 339 sophomores, 353 juniors, and 259 senior students surveyed (23 students did not report their classification).

Note. Data are presented as mean (standard deviation) or frequency (%). Statistical Analysis We analyzed data from every participant’s first set of responses. If a participant completed multiple surveys, only their first survey was used for analysis. We used independent samples t-tests to compare sleep, stress, social media use, and caffeine consumption between female and male students. We used Pearson’s correlation to test whether stress, social media use, and caffeine consumption were related to sleep quality. Results with p ≤ 0.05 were considered statistically significant. To test caffeine consumption, social media use, and perceived stress as mediators of gender effects on sleep quality, we performed three sets of bootstrapping mediation analyses.

Results Gender Differences The demographic information for our male and female participants is presented in Table 1. Male and female participants did not differ in age (t(1093) = 1.48, p = .14) or year in college (χ² (3) = 0.15, p = 0.99). By contrast, females had significantly greater stress levels on the PSSs (M = 18.51, SD = 6.79) than males (M = 15.18, SD = 6.91), t(1073) = -7.52, p < .001. Females also consumed significantly more caffeine (M = 1099.68, SD = 1054.92 mg) than males (M = 864.24, SD = 974.05 mg), t(1080) = -3.53, p < .001. Furthermore, females reported using social media for more than two hours/day (M = 134.81 minutes, SD = 97.87 minutes), which was significantly more than male students (M = 104.96, SD = 83.42 minutes), t(1084) = -4.96, p <.001. At bedtime, females reported using social media for 40.12 minutes (SD = 40.26 minutes), approximately 29.4% more than males (M = 31.00, SD = 36.42 minutes), t(1088) = -3.63, p < .001. Sleep The female students had a significantly higher PSQI global score (M = 6.38, SD = 2.90) than the male students (M = 5.77,

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A.

B. r(339)= 0.01, p= 0.80

Original Research

r(342)= -.40, p< 0.001

D.

C. r(687)= -0.43, p< 0.001

r(682)= -0.11, p= 0.01

Figure 1. A) Sleep duration during the week in males predicts overall sleep quality. B) There is no association between weekend sleep duration and sleep quality in males. C) Sleep duration during the week in females predicts overall sleep quality. D) Weekend sleep duration in females is only weakly associated with sleep quality. SD = 2.78), t(1036) = -3.25, p = .001. Interestingly, these gender differences in overall sleep quality were dissociable from sleep quantity. On weekday nights, female students (M = 6.78 hours, SD = 1.05 hours) generally reduced their sleep durations below consensus recommended levels (7-9 hours; Watson et al., 2015), though the reductions were to a similar level as male students (M = 6.87 hours, SD = 1.06 hours; t(1094) = 1.37, p = .17). On weekend nights, females (M = 8.42 hours, SD = 1.49 hours) and males (M = 8.44 hours, SD = 1.35 hours) rebounded their sleep durations to similar levels (t(1086) = 0.17, p = .86). In both genders, shorter sleep duration during weekday nights predicted worse sleep quality on the PSQI [females: r(687) = -.43, p < .001; males: r(342) = -.40, p < .001]. Shorter sleep duration during weekend nights was only weakly associated with worse sleep quality in females, r(682) = -.11, p = .01, and no association was observed in males, r(339) = .01, p = .80 (Figure 1). Sleep, Stress, and Behavior Correlations Greater perceived stress was significantly associated with worse sleep quality in both female students, r(683) = .41, p < .001, and male students, r(340) = .38, p < .001 (Figure 2). Also, greater amounts of caffeine consumption was associated with worse sleep quality in males [r(337) = .13, p = .02] and females [r(683) = .12, p = .003]. Although daily social media use was not related to sleep quality in males, r(340) = .08, p = .14, it was significantly correlated with worse sleep quality in females,

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r(680) = .12, p = .002. Bedtime social media usage was a stronger predictor of poorer sleep quality, showing significant correlations in both males, r(342) = .21, p < .001, and females r(682) = .18, p < .001 (Figure 3). Furthermore, we conducted mediation analyses to test whether bedtime social media use, caffeine consumption, and perceived stress explained the gender difference in sleep quality, using a bootstrapping approach with 5,000 samples (Hayes, 2017). First, mediation analyses revealed that the indirect gender effect was significant, such that bedtime social media use mediated 21.27% of the total gender effect on sleep quality (indirect effect: B = 0.13, 95% CI: 0.06, 0.22; total effect: B = 0.60, p = .002; Figure 4a). However, after controlling for bedtime social media use, gender still predicted sleep quality (direct effect: B = 0.47, p = .01). Next, we tested caffeine consumption as a mediator. Caffeine consumption mediated 14.00% of the gender effect on sleep quality (indirect effect: B = 0.08, 95% CI: 0.03, 0.16; total effect: B = 0.59, p = .002; Figure 4b), though gender continued to predict sleep quality (direct effect: B = 0.51, p = .01). Last, we tested perceived stress as a mediator of gender effect on sleep quality. Perceived stress significantly mediated the gender effect on sleep quality and explained 87.72% of the total effect (indirect effect: B = 0.53, 95% CI: 0.37, 0.73; total effect: B = 0.61, p = .001; Figure 4c). After controlling for perceived stress, gender no longer predicted sleep quality (direct effect: B = 0.07, p = .68).


A.

B. Original Research

r(683)= 0.41, p< 0.001

r(340)= 0.38, p< 0.001

Figure 2. Perceived stress predicts sleep quality in males (A) and females (B).

A.

B. r(342)= 0.21, p< 0.001

r(682)= 0.18, p< 0.001

Figure 3. Bedtime social media usage predicts sleep quality in both males (A) and females (B).

A. 9.4112 p= 0.0003

Gender

B.

Bedtime Social Media (min)

0.0135 p< 0.0001

249.0794 p= 0.0003

Sleep Quality

0.4685 p= 0.0123

Gender

Caffeine (mg)

0.5092 p= 0.0076

0.0003 p= 0.0001

Sleep Quality

C. 3.2063 p< 0.0001

Gender

Perceived Stress

0.0748 p= 0.6752

0.1666 p< 0.0001

Sleep Quality

Figure 4. A) Bedtime social media (minutes) use partially mediated the gender effect on sleep quality; B); Caffeine consumption (mg) partially mediated the gender effect on sleep quality; C) Perceived stress completely mediated the gender effect on sleep quality. The values next to each path represent unstandardized regression coefficients (top) and p-values (bottom).

Scientia 2020 | 9


Original Research

Discussion

References

We found that female students had worse sleep quality than male students who were enrolled in the same difficult STEM courses. Bedtime social media and caffeine consumption partially explained the gender differences in sleep quality; the most important predictor of gender differences in sleep, however, were gender differences in perceived stress. We next consider these results in light of the existing literature on sleep and gender. Although Tsai and Li (2004) argued that social jetlag contributes to the gender differences in sleep quality, our results indicated that social jetlag does not differ between males and females. Yet, we identified three clear gender differences that play a role in the overall sleep quality of college students. First, female students perceived much more stress than male students. Prior studies have shown female students to be more apprehensive or nervous when attempting to major and study in STEM-related fields (Barr, 2008). Higher levels of stress, like those seen in our female students, can lead to shorter sleep durations and worse sleep quality overall (Kashani, 2012). The second mechanism for gender differences in sleep quality is through social media usage. After knowing the association between social media usage and poor sleep, it is not surprising that females used social media significantly more throughout the day than male students. Additionally, female students used more social media before bed, which seemingly compromised their sleep quality. Social media use before bed is detrimental to sleep quality because blue light from phones and tablets inhibits the release of melatonin, which helps to control sleep-wake cycles. Additionally, social media increases cognitive arousal, which is known to delay sleep onset (Wuyts, 2012). A third mechanism underlying gender differences in sleep quality is caffeine consumption. Female students were intaking much more caffeine throughout the week, which would negatively affect their overall sleep quality. Caffeine blocks adenosine receptors in the brain. When these receptors are activated, individuals feel sleepy and their neural activity begins to slow; thus caffeine reverses these effects. Future research should investigate gender differences in the time of day of caffeine is consumed to further clarify the connection between caffeine consumption and sleep quality in females and males. Sleep education programs can be developed to target a specific gender or group with unhealthy caffeine consumption behaviors. One limitation of this study was the correlational design. Our study identifies that sleep quality, gender, and daily behaviors are associated, but experimental designs will be required to determine whether these associations are causal. Nevertheless, this study served as a foundation for investigating why gender differences in sleep quality are prevalent in college students. At the moment, the influence of sleep quality on academic performance has not been analyzed; but our future work in compiling data sets of these studentsâ&#x20AC;&#x2122; course grades and grade point averages will allow further investigation as to whether poor sleep quality negatively affects academic performance in college students, and whether the effects are similar across males and females.

Barr, D. A., Gonzalez, M. E., & Wanat, S. F. (2008). The leaky pipeline: Factors associated with early decline in interest in premedical studies among underrepresented minority undergraduate students. Academic Medicine, 83(5), 503-511. Buysse, D. J., Reynolds III, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193-213. Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385-396. de Mejia, E. G., & Ramirez-Mares, M. V. (2014). Impact of caffeine and coffee on our health. Trends in Endocrinology and Metabolism, 25(10), 489-492. Gilbert, S. P., & Weaver, C. C. (2010). Sleep quality and academic performance in university students: A wakeup call for college psychologists. Journal of College Student Psychotherapy, 24(4), 295-306. Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York City, New York: Guilford publications. Kashani, M., Eliasson, A., & Vernalis, M. (2012). Perceived stress correlates with disturbed sleep: A link connecting stress and cardiovascular disease. Stress, 15(1), 45-51. Leung, L. (2006). Stressful life events, motives for Internet use, and social support among digital kids. CyberPsychology and Behavior, 10(2), 204-214. Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A. (2016). The association between social media use and sleep disturbance among young adults. Preventive Medicine, 85, 36-41. Lund, H. G., Reider, B. D., Whiting, A. B., & Prichard, J. R. (2010). Sleep patterns and predictors of disturbed sleep in a large population of college students. Journal of Adolescent Health, 46(2), 124-132. Maquet, P. (2001). The role of sleep in learning and memory. Science, 294(5544), 1048-1052. Orben, A., & Przybylski, A. K. (2019). Screens, teens, and psychological well-being: evidence from three time-usediary studies. Psychological Science, 30(5), 682-696. Roehrs, T., & Roth, T. (2008). Caffeine: Sleep and daytime sleepiness. Sleep Medicine Reviews, 12(2), 153-162. Smarr, B. L., & Schirmer, A. E. (2018). 3.4 million realworld learning management system logins reveal the majority of students experience social jet lag correlated with decreased performance. Scientific Reports, 8(1), 4793. Tsai, L. L., & Li, S. P. (2004). Sleep patterns in college students: Gender and grade differences. Journal of Psychosomatic Research, 56(2), 231-237. Wuyts, J., De Valck, E., Vandekerckhove, M., Pattyn, N., Bulckaert, A., Berckmans, D., ... & Cluydts, R. (2012). The influence of pre-sleep cognitive arousal on sleep onset processes. International Journal of Psychophysiology, 83(1), 8-15.

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Evelynne Morris, Ireland Buckley, Gabbi Marchelli, Dena Quigley, Ph.D.

Abstract This experiment studied whether a cannabidiol (CBD) placebo, coconut oil, plays a role in relieving stress. Acting as a stress-inducing stimulus, the Stroop test is a video that requires participants to say the color of the word rather than the word itself when projected on the screen. While administering the Stroop test to a group of college participants ranging from 19 to 21 years of age, change in heart rate and mean arterial pressure were measured using a blood pressure cuff and pulse plethysmograph. Coconut oil, the CBD placebo, was administered to the participant’s temples and neck before repeating the Stroop test. Mean arterial pressure was found to be significantly lower when the participant believed they were using CBD. Heart rate also decreased when the placebo was used. At various intervals during the study, personal surveys asked for the participant’s current stress level on a standardized scale from 1-10 and their view on the given drug. Recorded results displayed a general decrease in indicated stress levels after the CBD placebo was administered. The findings from this study are expected to spike interest in continuing to study the uses and effects of CBD.

Introduction Recent studies focusing on cannabidiol (CBD), a Cannabis sativa constituent, have shown the drug’s powerful effect on treating neuropsychiatric disorders. Specifically, CBD has been shown to possess anti-inflammatory and anti-anxiety properties (Aubrey, 2018). As cannabidiol has increasingly become well-known as a “magic” drug, it is unknown to what extent a psychological factor, the placebo effect, influences the decrease of stress-induced heart rate and blood pressure. By measuring the effect of a placebo in place of CBD, we will learn how influential the placebo effect is at mitigating stress-related symptoms. Evidence has shown that patient expectations about medicines influence how the patient feels after taking medication (American Cancer Society, 2015). The placebo effect can be verbally induced or result from conditioning, as well as prior experiences (Munnangi, 2019). Optimistic expectations lead to a positive response, and negative expectations lead to inhibition of the desired response (Benedetti, 2013). For instance, if someone expects CBD to work, there should be a higher chance that the given CBD placebo will show a decrease in the stress factors of heart rate and blood pressure on a person. This is the main hypothesis being tested in our experiment. We hypothesize that, as a psychological factor, the perception alone of using CBD oil will decrease blood pressure and heart rate in response to a stressful stimulus. In our research, the stressful stimulus is a Stroop test video. In this test, participants had to say aloud the color of the word that they saw flash before them on the screen. As the video progressed, the flashing of words on

the screen increased in speed. The final round (Round 4) projected words on the screen at such a high pace that made it difficult to process the word before the next appeared (MindfulThinks, 2017). Although it does stem from the marijuana plant, the removal of tetrahydrocannabinol (THC) leaves CBD oil with none of the psychoactive components. The use of CBD interacts with several receptors that regulate fear and anxiety, such as the cannabinoid type 1 receptor (CB1R), the serotonin 5-HT1A receptor, and the transient receptor potential vanilloid type 1 (TRPV1) (Blessing, 2015). As part of the endocannabinoid system, CB1R functions as an inhibitory Gi/o protein-coupled receptor that is densely distributed in the basal ganglia. The CB1R involves the cAMP signal transduction pathway. In this pathway, when the ligand binds to the G-protein coupled receptor, the alpha subunit migrates to adenylyl cyclase creating cAMP from ATP. When the CB1R is activated, Gi activates, which decreases intracellular cAMP concentration by inhibiting the production of adenylyl cyclase, the main enzyme used in this pathway (Blessing, 2015). Described as an anxiolytic, CBD has in the past few years been used as a therapeutic drug to combat anxiety disorders and PTSD. By administering CBD, acute increases in heart rate and blood pressure were typically reduced (Blessing, 2015). Regarding the myocardial physiology, heart rate is determined by the conductive autorhythmic cells in the heart which demonstrate pacemaker activity (Costanzo, 2018). The action potentials travel down along cardiac muscle membrane

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Effect of Using CBD Placebo Under Stress: Measuring Heart Rate, Mean Arterial Pressure, and Reported Stress Level


Original Research

and down into the T-tubule. In the T-tubule, a voltage gated channel opens in response to the action potential. The opening of this channel allows calcium to enter into the cardiac muscle cell. The calcium moves into the smooth endoplasmic reticulum and mediates calcium release back out of the smooth endoplasmic reticulum. In the autorhythmic cell, the action potential consists of sodium “funny” channels and T-type calcium channels. Sodium “funny” channels allow sodium into the cell causing the cell to depolarize. T-type calcium channels then open, which causes calcium to enter the cell and depolarizes it even more. The effects of these two different channels create the threshold potential. At threshold, voltage gated L-type calcium channels are activated and opened which results in rapid depolarization as more calcium enters the cell. Finally, the voltage gated potassium channels open and allow potassium to leave the cell resulting in repolarization. After repolarizing, the “funny” channels kick in again and the cycle starts over again. The voltage provided from the autorhythmic cells activate the sodium gated channels in contractile cells. This all leads to contraction of the heart at a regulated pace (Costanzo, 2018). For blood pressure, one needs to take into account the systolic and diastolic pressure. The systole event in the cardiac cycle marks the time where the cardiac muscle is contracting and the blood in the ventricle is ejecting (Costanzo, 2018). In contrast, the diastole event in the cardiac cycle is the time where the cardiac muscle is relaxing and the ventricle is filling with blood. Blood pressure is reported as the systolic pressure over the diastolic pressure. In other words, blood pressure is the peak pressure in the arteries over the minimum pressure in the arteries (Costanzo, 2018).

stress level and conducted a casual conversation with the experimenters. Before showing the Stroop test video for the second time, the participant was instructed to use CBD oil on their temples and back of the neck. No additional information about CBD in general was given. The video then played for one minute as heart rate was continuously measured. Right as the video ended, blood pressure and heart rate were measured for the third time. Afterwards, the participant then completed a final survey evaluating their overall experience and personal view on CBD. This procedure was performed on every individual on each data collection day. Approximately six participants were seen each day, over a span of four trial days. To control for confounding or outside variables, if a participant asked for more information on CBD, we answered with a generic reply stating that it is in a lot of products to avoid any confirmation bias. This allowed us to test each participants response based solely on their previous knowledge of CBD. Three bar graphs were created. The first graph presented data collected from the participants’ survey results on their current stress level. The second graph reported the differences in heart rate. Collected heart rates for all participants were then averaged every 10 seconds using (V1+V2)/2. Finally, the third graph reported the differences in mean arterial pressure (MAP). Collected blood pressure using systolic and diastolic pressure was then calculated using (SBP+ (2×DBP))/3. All three of the bar graphs are split up into the following sections: resting, without CBD placebo, and with CBD placebo. To further analyze the data, two paired t-tests were used in order to observe significance of the change in heart rate and blood pressure between using the placebo and not using the placebo.

Materials and Methods

Results

The objective of this experiment was to discern the differences in heart rate and blood pressure measurements in participants who watched a stressful video with and without using a CBD oil placebo. Coconut oil was given as the placebo CBD oil. The 24 subjects observed consisted of 17 females and 6 male Baylor University undergraduates with ages ranging from 19 to 21. Additionally, each individual was placed in both a control group and experimental group. First, the participants filled out a survey to evaluate their current stress levels and were asked if they had any allergies. A blood pressure cuff was wrapped around the participant’s left arm, a pulse plethysmograph was placed on the participant’s right index finger, and noise cancelling headphones were given to the participant in order to diminish ambient noise. Prior to watching the stressful video, the participant’s resting blood pressure and pulse were measured. A Stroop test video was chosen for the participants to watch. The video included three different levels that each increased in speed. Participants were given instructions to say aloud the color of the word that appeared on the screen rather than the word itself. For approximately one minute, they watched the YouTube video as their heart rate was continuously measured. As soon as the video ended, blood pressure and heart rate were measured for the second time. During the neutral time between video clips, the participants filled out a post-survey regarding their new

All figures are shown on the following page. In Figure 1, the second column (without CBD placebo) is the highest and depicts an increase in recorded stress level between the participants resting and post-Stroop test. At the moment in the experiment marked by the third column (with CBD placebo), participants reported a dramatic decrease in stress level in comparison to their stress level after watching the video for the first time. Unbeknownst to the participants, the “CBD” they applied to their temples was merely a placebo. The coconut oil should have had no effect on the participants, however, the participants still recorded a decrease in stress level. In Figure 2, there is an increase in heart rate in participants from resting to taking the stressful test without the CBD placebo. There was a decrease in heart rate with the CBD placebo, as opposed to without. Since this study is primarily focusing on the placebo effect on the body, we ran paired t-tests using the CBD placebo and without using the CBD placebo. This created an even field where the main differences were personal background and belief about how CBD would affect stress level while performing the same Stroop test. After conducting paired t-tests on heart rate with the CBD placebo and without the placebo, a t-stat value of 4.004 (p-value of 0.05) showed a significant difference between the two. This can also be seen in Figure 2, where using the CBD placebo decreased the average heart rate by about 6 beats per minute. An alternate paired t-test based on mean arterial

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Average Participant Surveyed Stress Levels

Original Research

5.8

Indicated Stress Level

5.6 5.4 5.2 5.0 4.8 4.6 4.4

4.75

5.42

5.00

Resting

Without CBD Placebo Participant State

With CBD Placebo

Figure 1. In each of the three surveys, we asked for the participant’s current stress level on a scale from 1 to 10. This bar graph represents three consecutive surveys: when resting before the Stroop test video, after watching the video without the CBD placebo, and after the second time watching the video when participants were told they were using CBD. All 24 of the participants’ scores were averaged together for each recorded stress level. Individual differences are indicated by the error bars given.

Average Heart Rate (bpm)

Average Differences in Heart Rate 100 90 80 70 60 50 40 30 20 10 0

76.625

87.965

82.083

Resting

Without CBD Placebo Participant State

With CBD Placebo

Figure 2. The heart rate, measured as beats per minute, for each participant was averaged together among the 24 total participants for each section of the experiment. When resting, heart rate was measured once. When watching the Stroop test video without the CBD placebo and when participants were told they were using CBD, heart rate was measured every 10 seconds and then averaged together. Individual differences are indicated by the error bars given.

Mean Arterial Pressure (mmHg)

Average Differences in MAP 93 92 91 90 89 88 87 86 85 84 83

90.679

87.212

90.379

Resting

Without CBD Placebo Participant State

With CBD Placebo

Figure 3. The mean arterial pressure (MAP), calculated from diastolic and systolic blood pressure for each participant, was averaged together among 21 of the participants for each section of the experiment. (3 participants’ blood pressures were excluded from data collection because of the blood pressure cuff not working). Blood pressure was measured once for each of the three states: when resting, after watching the Stroop test video without the CBD placebo, and after the second time watching the video using the CBD placebo. Individual differences are indicated by the error bars given.

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pressure (MAP) when told they were using CBD compared to without provided an insignificant t-stat of -0.966 (p-value of 0.05). This can also be seen in Figure 3, where MAP actually slightly increased the second time after watching the Stroop test video. 

Discussion Overall, the results support our original hypothesis. As a psychological factor, the perception of using CBD oil decreased heart rate in response to a stressful stimulus. From our statistical findings, there was a significant drop in heart rate once participants took the Stroop test after being told to apply “CBD” to their temples. On the other hand, the statistical data was insignificant regarding the change of blood pressure between taking the Stroop test with and without the CBD placebo. Because we did not see a consistent decrease in blood pressure similar to the decrease in heart rate, we can still conclude that most of our hypothesis was correct. This is because heart rate and blood pressure do not necessarily increase at the same time (American Heart Association, 2019). Healthy blood vessels respond to increased heart rate by dilating to maintain blood pressure. Since our participants reported no health complications, we can conclude that the insignificant change in blood pressure does not truly affect our results. Psychologically, participants felt a difference between taking the CBD placebo as well. From the psychological perception that the participants’ stress levels would decrease, their heart rates also decreased. As this study relied on the placebo effect of CBD, it was shocking to see how many of the college students had little to no knowledge about this substance. Approximately half of the responses given in the final survey were stated as “I do not know what CBD is,” and “I’m unsure of its effects.” Other responses given were “I believe CBD is medically beneficial,” and “I do not know if there is not a lot of data to back up the uses.” Although they had no information about how CBD would affect them, none of the participants refused to use the substance during the trial. One of the participants even believed CBD was the same as THC and weed. This shows the lack of educational programs given about marijuana. In the state of Texas, where laws regarding CBD use are continually passed, it is important that students are educated about this prevailing substance (McGaughy, 2019). Similar in reference to alcohol and other drugs, being uninformed can cause poor decision making. The responses from the final survey revealed that many people did not have any knowledge of CBD. All of the participants were willing to use the product in our experiment even if they knew little information on the substance. Many of the participants did not ask any additional information about the product when we asked them to rub CBD oil on their skin. After observing our participants’ body language and attitudes, every single participant did not even hesitate before administering it to their temples and neck. Only one participant asked a question to clarify if the product was an ingredient in marijuana as she was rubbing the CBD oil placebo into her skin. Because we did not want to affect her perception of the product before completing the experiment, we answered her question very vaguely by

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not disclosing any additional information. This trend was interesting because we noticed that this attitude towards unfamiliar substances is consistent with many things in society. Many teenagers and young adults try products without knowing the ingredients or the risk factors behind the substance. Using substances without knowledge of its makeup can be dangerous due to allergies, drug interactions, and other side effects. Throughout the experimental trials, we noticed several other trends in our observations. In the beginning, when we asked participants to complete the pre-survey, they seemed slightly anxious about not knowing the experiment. This anxiousness was shown in their preliminary heart rate. As the participant began to relax before we started the first part of the experiment, their resting heart rate became stable. An interesting observation we made was with each individual during the Stroop test video. When the Stroop test began to move at a quicker pace towards the end of the video in Round 4, all of the participants started to laugh. Some participants laughed consistently and did not even attempt Round 4 at all. The point in which everyone started laughing was also when we saw a trend in a rise in blood pressure among all of the participants. Before starting this study, we tried controlling any outstanding variables or confounding circumstance that would cause deviations in our experiment. Although we tried to prevent these causes, there were sources of error that might have been responsible for small differences in our results. To record each participant’s stress level, we required three surveys to be filled out throughout the experiment. Because we used a selfreporting survey, the variations in actual stress level could have been slightly off compared to what was reported in the survey results. Many times, surveys can cause biased or misleading information to be gathered because participants are not fully honest or fail to report all necessary information. Another source of error could have occurred using the headphones. We decided to use noise-cancelling headphones in our experiment to diminish outside conversations and distractions in the classroom during the stress stimulus, the Stroop test. For the first day, we conducted our experiment using Apple AirPods. Although these headphones kept some noise constant, they did not block out all background noise. For the remaining experimental days, we used Bose noise-cancelling headphones to help control the background noise we found in our first day of experimental study. Because we switched headphones from the first group of participants, there might be a slight variation in the results due to differences in distracting background noise. However, the main source of possible error is rooted in the Stroop test video. We decided to use the same stress stimulus to assure our stress stimuli for each trial were of the same caliber. Because the same video was viewed, there was a possibility that watching the video the second time was not as stressful as the first time because of the familiarity effect. We did, however, choose to continue using the same video because of the specific content in the Stroop test. Because the words in the video move rapidly, a viewer cannot easily remember their specific order to increase results like in other videos. After conducting this experiment, we discussed additional options for research in the future. To strengthen results, another study could be conducted in the same fashion but with a


StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih. gov/books/NBK513296/ Placebo Effect. (2015, April 10). Retrieved October 10, 2019, from https://www.cancer.org/treatment/treatments-and side-effects/clinical-trials/placebo-effect.html Placebo Genetics | Project CBD. (2019, May 6). Retrieved November 17, 2019, from https://www.projectcbd.org/ news/quick-hits/placebo-genetics Studies on CBD and Stress. (n.d.). Retrieved October 10, 2019, from https://www.projectcbd.org/cbd-for/stress What Are the Benefits of CBD? - The New York Times. (2019, October 17). Retrieved October 20, 2019, from https:// www.nytimes.com/2019/10/16/style/self-care/cbd-oil benefits.html

References Anxiety Relief Without The High? New Studies On CBD, A Cannabis Extract. (2018, April 23). NPR.Org. Retrieved October 10, 2019, from https://www.npr.org/sections/ health-shots/2018/04/23/604307015/anxiety-reliefwithout-the-high-new-studies-on-cbd-a-cannabis-extract Benedetti, F. (2013). Placebo and the new physiology of the doctor-patient relationship. Physiological Reviews, 93 (3), 1207–1246. Blessing, E. M., Steenkamp, M. M., Manzanares, J., & Marmar, C. R. (2015) Cannabidiol as a potential treatment for anxiety disorders. Neurotherapeutics, 12 (4), 825–836. Blood Pressure vs. Heart Rate (Pulse). (2016, Oct 31). Www. Heart.Org. Retrieved November 17, 2019, from https:// www.heart.org/en/health-topics/high-blood-pressure/thefacts-about-high-blood-pressure/blood-pressure-vs-heartrate-pulse Costanzo, Linda S. Physiology. Elsevier, 2018. Gov. Greg Abbott signs law legalizing hemp production, CBD products in Texas. (2019, June 11). Dallas News. https:// www.dallasnews.com/news/politics/2019/06/11/gov-gregabbott-signs-law-legalizing-hemp-production-cbdproducts-in-texas/ Haapanen, H. (2018, July 3). How to measure stress? Medium. https://medium.com/inmehealth/how-tomeasure-stress-d770da69152e How Fast Is Your Brain? The Stroop Test. (2017, April 11). Retrieved March 19, 2020, from https://www.youtube.com/ watch?v=gjesfzWozo4 Munnangi, S., & Angus, L. D. (2020). Placebo Effect. In

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different stress stimulus. This additional research could confirm the CBD placebo effect by stimulating the participants in a different way and coming to a similar conclusion. Another study could potentially use a different method to measure stress. With more advanced technology, cortisol levels can be compared in saliva testing. These levels of cortisol correlate relatively to the participant’s stress level. By comparing cortisol levels rather than self-reported stress levels, accuracy and precision of the experiment would increase. In accordance to real world applications of our experiment, we concluded that common effects found in CBD can be partially attributed to the patient’s belief in the products abilities. Because our results had some variations, we could not conclude that every participant had felt the CBD placebo’s effects solely from their belief in the treatment. This allowed our group to infer that CBD’s stress-reducing abilities are assisted by the patient’s prior thoughts, making it have a greater and more positive effect overall. Because there has been some controversy over CBD recently due to it becoming a prominent product in the media and in correlation to marijuana, this study is applicable to the topic at hand. Because CBD is relatively new, many people are searching for research that can serve as the foundation to their stance on the product itself. This experiment can serve as a spark for other studies to seek additional information on the overarching controversy surrounding CBD.


Original Research

Proneness to Smartphone Addiction in Relation to Morningness and Eveningness Abel Thomas, Samantha Hodges

Abstract With an influx of cellular phone use, questions have arisen over its impact on smartphone users. The purpose of the current study is to find out how many students have phone addictions, how severe these addictions are, and how the most severe cases relate to morningness and eveningness. One area in which phone addiction may negatively impact an individual’s quality of life is the amount of sleep he or she loses due to smartphone usage. Previous researchers investigated how smartphone addiction was linked to whether a person was a morning or evening person and found that evening oriented individuals were more likely to have a “severe” addiction than morning oriented individuals. In this previous study, a sample group of German students was given two questionnaires, one to determine whether a person was a morning or evening person based on their answers to the Composite Scale of Morningness (CSM) and another to assess phone addiction based on the Smartphone Addiction Proneness Scale (SAPS). This current study, which followed similar procedures with an American sample, found that there was no significant correlation between being evening oriented or morning oriented and smartphone addiction proneness. With the rise of technology and people’s everyday use of the Internet, the results from this study are important to help understand the consequences that an overuse of these electronic devices may entail.

Introduction Currently, over 150 million individuals worldwide use the Internet, with 70 to 80% of adults in the United States having online access (Woods & Scott, 2016). With this rapid growth of the availability of the Internet, the rise of smartphones was inevitable. The term “smartphone” was coined in 1997 when it became a part of people’s vernacular (Randler et al., 2016). With this influx of cellular phone use, questions have arisen over the impact that cell phones have on smartphone users. These include questions on how much of a hold cell phones have on people, why cellular devices have become so popular, and how much do people prioritize their devices over other forms of technology. Excessive use of mobile phones can affect mental and physical health status. In recent studies, correlations have been found between mobile phone dependency and loss of sleep (Toda & Ezoe, 2013; Toda et al., 2006). This study observes how this mobile phone dependency can be related to whether someone is an evening person or a morning person. One area of discussion on the topic is determining the level of phone addiction people have and the primary cause of this addiction. “Addiction” is defined in Webster’s Dictionary: a pathologic condition that one cannot tolerate without the continuous administration of a substance. Commonly handled by neuropsychiatric departments, addiction is a phenomenon that manifests tolerance, withdrawal symptoms, and dependence, and it is often accompanied by social problems. The term was once limited to drugs or other physical substances, but it is now also applied to the Internet, gaming, mobile-phone

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usage, and other behaviors (Lee et al., 2016). The components of smartphone addiction are tolerance, withdrawal, compulsive symptoms, and functional impairment (Lin et al., 2014). Smartphone addiction might be a type of non-substance addiction (Serenko & Turel, 2010). However, the term “addiction” may not be appropriate because studies showing behavioral and neurobiological similarities between mobile phone addiction and other types of addictive behaviors are lacking (Billieux et al., 2015). Therefore, these authors suggested the term “problematic usage” and labeled this behavior as “addiction proneness” (Kim et al., 2014). With the rise of smartphones, more teenagers are growing up with technology and its negative effects like phone addiction proneness (Lepp et al., 2014). The primary purpose of the current study is to find out if a sample of high school students have phone addictions, how severe they might be, and how the most severe cases could relate to morningness and eveningness. Morningness is defined as the characteristic of being most active and alert during the morning, and eveningness is the characteristic of being most active and alert during the evening (Randler et al., 2016). In order to conclude that a person may have addiction proneness, researchers must observe the implications of the addiction on the subject. For this study, it would be observing the correlation between sleep loss and the effect cellular phones have on the people using them. This is prudent as sleep is how people reenergize themselves, and, when reduced, has a slew of negative effects that have been well documented within the literature: increased Internet use is


Methods In this anonymous study, there was a cross sectional survey distributed to a sample from Lovejoy High School, a school of 1,500 students in the suburb of Dallas, TX. The 29-question survey contained 15 questions to evaluate the students against the Smartphone Addiction Proneness Scale (SAPS) and 14 questions to rank the students on their optimal time of

alertness by asking questions on when they go to bed, when they wake up, how often they use their cell phones, how much time they spend on their cell phones, and how much time they spend on homework on a daily basis. These last 14 questions were asked for both weekday and weekend scenarios. The first 15 questions used a Likert-scale of Strongly Disagree to Strongly Agree which was then converted into a scoring system from 1-5, respectively. The Institutional Review Board, the school administrators, the teachers, and the parents of the students were informed of the process. School district administration required parental consent for all student-participant studies. Those that took the survey were required to own a smartphone. Upon the conclusion of the study, the participants were categorized into groups with morning tendencies or evening tendencies and whether either group was more prone to smartphone addiction. This survey was sent to over 1,500 students, ranging from 13-year-old freshmen to 18-year-old seniors, and 194 students responded in the survey process. Nine student responses of the 194 were voided from the data collected due to improper answers and failure to complete each question from the survey. Thus, data was analyzed from 185 students. The SAPS measured smartphone addiction proneness in young adolescents. The SAPS helped determine which of these students, based on their scores, had a phone addiction proneness severe enough to be scientifically measured (which was a threshold of 60+ hours of phone time in a week). After determining which students had a phone addiction proneness, their scores were then compared to the Composite Scale of Morningness (CSM). The CSM, developed by Smith, Reily, and Midkiff in 1989, helped determine what kind of sleep patterns people have and when they are most alert. The CSM also showed the highest activity rate the individual had during the day. To classify evening, morning, or neither types, the 20th/80th percentiles were taken as cutoffs in this study (20% and lower = evening types; 80% and higher = morning types; 21-79% = neither types). The responses were then analyzed using a Q-Test, which finds outliers in a distribution, and an ANOVA test, which was used to compare the variance between the two groups.

Results Of the 185 student responses, 67 of these students qualified as phone addicted according to the SAPS. The scale determines an individual’s extremity of phone addiction proneness, but for this case, the individual had to have used their cellular device for more than half of their day on a regular basis, excluding time spent sleeping. In order to identify the qualifying study group, student wake-up and bedtimes were recorded for all days of the week. The survey included questions asking the students if they believed they had phone addictions. More than half of the students with a quantified phone addiction had a negative belief of their addiction. According to Figure 1, of all the responses, 29.73% agreed that there might be a possibility that they could have a problem.

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associated with shorter sleep duration (Anna & Westin, 2011), later bedtimes and rise times (Shochat et al., 2010), longer sleep latencies (Shochat et al., 2010), and increased daytime tiredness in adolescents (Billieux et al., 2008). The biggest factor in an individual’s loss of sleep is their chronotype which is the manifestation of their circadian rhythms (Randler et al., 2016). Slightly different than morningness and eveningness, chronotype refers to the preference for sleepwake timing: morning types experience peak alertness and performance earlier in the day. Evening types experience their most critical points of alertness at later times of the day (Carney et al., 2006; Suen et al., 2008). A series of studies have shown that morningness increases with age (Curcioa et al., 2006). Individual’s circadian preferences can be grouped into three categories: “morning type,” “neither type,” and “evening type,” but they can also be seen as a continuum (Natale & Cicogna, 2002). There are several variables that have an impact on one’s chronotype, including endogenous factors like one’s genetics, biology, age, and gender, as well as exogenous factors such as culture, society, and environment (Adan et al., 2012). A previous study in Germany examined reasons as to why teenagers became so prone to addiction to their cellular devices. In Germany, 25% of the surveyed 12 to 19-year-old adolescents owned a smartphone in 2011, and this number increased to 62% in 2013 (Medienpädagogischer et al., 2013). The researchers investigated how smartphone addiction can be linked to whether a person may be a morning or evening person. The researchers determined this by giving a sample group of 1,200 students aged 8-17 two questionnaires: one to determine whether a person was a morning or evening person based on their answers to the Composite Scale of Morningness (CSM) and another to assess phone addiction when compared to the Smartphone Addiction Proneness Scale (SAPS). This study found that evening-oriented students were more likely to fall in the most severe category of phone addiction proneness than morning-oriented students. Even though numerous studies have assessed what initiates phone addiction proneness, none have analyzed the relationship between chronotype and phone addiction in American high schoolers. 83% of American high schoolers have a smartphone, in comparison to the 62% of high schoolers in Germany (Lepp et al., 2014). Because the majority of American high school students are current cell phone users, I chose to examine these students to expand on the previous study done on German students. We hypothesize that, compared to morning-oriented American students, evening-oriented American students should score higher on the smartphone addiction proneness tests. This study will allow us to further explore the correlation between smartphone addiction proneness in relation to being an evening or morning oriented person.


Number of Students

70

59

60

49

44

50 40

27

30 20

6

10 0

Strongly Disagree

Disagree

Neutral

Agree

Strongly Agree

Scale Options Figure 1. This shows a bar graph based on the answers given from participants indicating their level of agreement to the question. The population n = 185. Percentage-wise out of 185, Strongly Disagree= 14.59%, Disagree= 31.89%, Neutral= 23.78%, Agree= 26.49%, and Strongly Agree = 3.24%.

“I have the habit of spending a lot of time on my smartphone” Strongly Agree Scale Options

Original Research

“Even if I think I should use my smartphone less, I still use it too much”

9

Agree

84

Neutral

41

Disagree

29 22

Strongly Disagree 0

10

20

30

40 50 Number of Students

60

70

80

90

Figure 2. This shows a bar graph based on the answers given from participants indicating their level of agreement to the question. The population n = 185. Percentage-wise, Strongly Agree= 4.86%, Agree= 45.41%, Neutral= 22.16%, Disagree= 15.68%, and Strongly Disagree= 11.89%.

Figure 2 demonstrates that most students at the high school level realize they spend too much time with their cell phones. This figure shows that 50.27% of the student responders agree or strongly agree that they are on their cellular devices too long. These questions asked in Figures 1 and 2 look at whether the individual sees a problem, but other questions in the survey asked if the people around them noticed their abundant phone use. This was asked to allow the participant to consider an objective viewpoint and eliminate personal bias. The question in Figure 3 itself gives a negative connotation towards smartphones, so the expected result was more students would not agree with this. However, contrary to the expected responses, 35.68% of students agreed. Sleep is an important factor that helps determine the

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strength of correlation between morning types and evening types with smartphone usage. Sleep was calculated using the midpoint of sleep, which is the halfway mark between going to bed and waking up. The whole sample size of 185 was used in calculations of the SAPS to determine the 67 students that use their phones for more than a quarter of the day. The 67 responses were only used when testing the relationship between phone addiction and morningness and eveningness. Figure 4 illustrates the range of hours of smartphone use by all the students, not including phone usage for work or school. The graph shows nearly half of the sample of students, 48.4%, fall between the two to three-hour range. When looking at the total results, sleep duration during the week showed to be negatively correlated to phone usage issues.


Scale Options

Strongly Agree

Original Research

“I could not imagine my life without a smartphone” 15

Agree

51 34

Neutral Disagree

51 34

Strongly Disagree 10

0

20

30 Number of Students

40

50

60

Figure 3. This shows a bar graph based on the answers given from participants indicating their level of agreement to the question. The population is n = 185. Percentage-wise, Strongly agree = 8.11%, Agree= 27.57%, Neutral= 18.38%, Disagree= 27.57%, Strongly Disagree= 18.38%.

Number of Students

“How often would you say you are on your phone for non-school-related activities? 50 45 40 35 30 25 20 15 10 5 0

45

45

33 25 16

[0,1]

(1,2]

(2,3]

(3,4]

Hours

(4,5]

13

(5,6]

3

5

(6,7]

(7,8]

Figure 4. This shows a histogram based on the answers given from participants to the question. The population is n = 185.

Discussion The study examined the relationship between phone usage in adolescents and the tendency to be a morning or evening person. The hypothesis before the survey and tests were taken was that compared to morning-oriented students, eveningoriented students should score higher on the smartphone addiction and addiction proneness tests as per the results from the German test. All three groups (morning= 0.93, evening= 0.94, neither= 0.97) showed relatively similar results on the Q-test when compared. All show a high relation to smartphone addiction proneness with bigger differences between the groups themselves. The Q test showed that the p-value between the morning and evening group was 0.996, between evening and

neither was 0.9499, and between morning and neither was 0.979. The ANCOVA test showed a p-value of 0.947 between the groups. These tests show that there is not a relationship between a person’s chronotype and their susceptibility to smartphone addictions. The relationship between smartphone addiction proneness and midpoint of sleep was more prevalent when using the midpoint of sleep on weekends than on weekdays. Because the midpoint of sleep on weekdays is restricted by the school schedule, the midpoint of sleep on weekends reflects the internal biological rhythm. Weekend sleep duration was not related to smartphone addiction proneness because recovery sleep played a role. Recovery sleep is when adolescents sleep longer on the

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weekends to make up their sleep debt which accumulated during the school week. High mobile phone usage was found to be related to later bedtimes (Lemola et al., 2014). Roenneberg (2004) suggested the prevalence of light-emitting electronic devices such as computers, tablets, and mobile phones late in the night is shifting people to a later chronotype (Roenneberg, 2014). These studies support the finding that screens and the light they give off (blue light) shift people to eveningness. Using the mobile phone before going to sleep leads to increased sleep problems (Van den Bulck, 2007). This hypothesis provides evidence that eveningness is associated with a higher potential for addictive behaviors. In line with this studyâ&#x20AC;&#x2122;s results, Barnes and Meldrum (2015) indicated those reporting sleeping fewer hours at night displayed lower levels of self-control. On the weekends, children and adolescents are able to regulate their sleep duration times on their own in comparison to school days when they must adhere to school times (Barnes and Meldrum, 2015). The results are more generalized in the Barnes and Meldrum study, primarily because only one scale was used to measure smartphone addiction (proneness), and chronotype by two measures (CSM scores, midpoint of sleep), as well as sleep duration. During adolescence (13-18 years), there is a higher tendency to eveningness. The contrasts indicated differences between the freshmen and seniors, and indicates that the SAPS can detect the changes in morningness and eveningness during adolescence (Pilcher et al., 1997). Limitations to the study include a self-report scale given to each student which showed the relationships between smartphone addiction and chronotypes which could be a problem due to students not taking the survey honestly. Another issue is this survey and study only look at cell phones and do not consider any other types of electronics the students might be using. Correlation does not prove causation and multiple other factors could explain phone addiction such as gender, age, mental age, psychological factors, etc. To overcome these effects, future research should add some physiological measures, such as actigraphy for sleep measurements, blood pressure, and pulse rate when using the smartphone to gather more forms of data. The small sample size could lead to a high margin of error along with the even smaller amount of people who met the requirements for having a phone addiction.

Conclusion The study presented the relationships between smartphone addiction and chronotype in adolescents. Chronotype was the best predictor of smartphone addiction proneness in adolescents and more important than age, sleep duration, and midpoint of sleep. Neither the morning or evening type was shown to be statistically significant compared to the other on being more prone to smartphone addiction. When sleep duration on weekdays was longer, smartphone addiction was lower. These trends should be considered in sleep education programs as smartphone usage can affect sleep habits. The results from this study can help the general public broaden their mindset to a misconception about phone addictions and provide a basis on

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which adolescents can reflect on their phone usage patterns and hopefully reduce future usage.

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Psychology and Learning, 5(2), 75-80. doi:10.4018/ ijcbpl.2015040106 Turel, O., & Serenko, A. (2010). Is mobile email addiction overlooked? Communications of the ACM, 53(5), 41-43. doi:10.1145/1735223.1735237 Urban, R., Magyarodi, T., & Rigo, A. (2011). Morningness-eveningness, chronotypes and health-impairing behaviors in adolescents. Chronobiology International, 28, 238-247. doi:10.3109/07420528.2010.549599 Van den Bulck, J. (2007). Adolescent use of mobile phones for calling and for sending text messages after lights out: Results from a prospective cohort study with a one-year follow-up. Sleep, 30(9), 1220-1223. Woods, H. C., & Scott, H. (2016). #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51, 41-49. doi:10.1016/j.adolescence.2016.05.008 Yen, C., Tang, T., Yen, J., Lin, H., Huang, C., Liu, S., & Ko, C. (2009). Symptoms of problematic cellular phone use, functional impairment and its association with depression among adolescents in Southern Taiwan. Journal of Adolescence, 32(4), 863-873. doi:10.1016/j.adolescence.2008.10.006

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J. F., & Grob, A. (2014). Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. Journal of Youth and Adolescence, 44(2), 405-418. doi:10.1007/s10964-014-0176-x Lepp, A., Barkley, J. E., & Karpinski, A. C. (2014). The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Computers in Human Behavior, 31, 343-350. doi:10.1016/j. chb.2013.10.049 Medienpadagogischer, Forschungsverbund, Sudwest. (2013). JIM 2013. Jugend, information, (multi-) media. Basisstudie zum Medienumgang 12- Bis 19-Jahriger in Deutschland [JIM Study 2013--Youth, information, (multi-) media. Basic studies for media handling of 12- to 19-year old Germans]. Stuttgart, Germany: Medienpadagogischer Forschungsverbund. Nakade, M., Takeuchi, H., Taniwaki, N., Noji, T., & Harada, T. (2009). An integrated effect of protein intake at breakfast and morning exposure to sunlight on the circadian typology in Japanese infants aged 2-6 years. Journal of Physiological Anthropology, 28, 239-245. doi:10.2114/jpa2.28.239 Natale, V., & Cicogna, P. (2002). Morningness--eveningness dimension: Is it really a continuum? Personality and Individual Differences, 32(5), 809-816. doi:10.1016/S01918869(01)00085-X Pilcher, J. J., Ginter, D. R., & Sadowsky, B. (1997). Sleep quality versus sleep quantity: Relationships between sleep and measures of health, well-being and sleepiness in college students. Journal of Psychosomatic Research, 42(6), 583596. doi:10.1016/s0022-3999(97)00004-4 Prat, G., & Adan, A. (2011). Influence of circadian typology on drug consumption, hazardous alcohol use, and hangover symptoms. Chronobiology International, 28, 248-257. doi:1 0.3109/07420528.2011.553018 Randler, C. (2008). Differences between smokers and nonsmokers in morningness--eveningness. Social Behavior and Personality: An International Journal, 36, 673-680. doi:10.2224/sbp.2008.36.5.673 Randler, C., Wolfgang, L., Matt, K., Demirhan, E., Horzum, M. B., & Beşoluk, Ş. (2016, 09). Smartphone addiction proneness in relation to sleep and morningness– eveningness in German adolescents. Journal of Behavioral Addictions, 5(3), 465-473. doi:10.1556/2006.5.2016.056 Roenneberg, T. (2004). The decline in human seasonality. Journal of Biological Rhythms, 19(3), 193-195. doi:10.1177/0748730404264863 Shochat, T., Cohen-Zion, M., & Tzischinsky, O. (2014, 02). Functional consequences of inadequate sleep in adolescents: A systematic review. Sleep Medicine Reviews, 18(1), 7587. doi:10.1016/j.smrv.2013.03.005 Suen, L. K., Ellis Hon, K. L., & Tam, W. W. (2008). Association between sleep behavior and sleep-related factors among university students in Hong Kong. Chronobiology International, 25(5), 760-775. doi:10.1080/07420520802397186 Toda, M., Nishio, N., Ezoe, S., & Takeshita, T. (2015, 04). Chronotype and Smartphone Use among Japanese Medical Students. International Journal of Cyber Behavior,


Original Research

The Utilization of Magnets in Laparoscopic Uterine Prolapse Repairs Alicia R. Chen, Daphne T. Simo, Megan K. Taylor, Marty Harvill, Ph.D., Mojgan Parizi-Robinson, Ph.D.

Abstract Uterine prolapse is characterized by the herniation of the uterus into or externally from the vagina, resulting from weakening ligamentous and fascia support (Dangal, 2005). Currently, the most effective treatment option for women experiencing uterine prolapse is through the laparoscopic fissure of the uterus and subsequent vaginal removal, referred to as a laparoscopic vaginal hysterectomy (Maher, 2001; Reich et al., 1989). The common issues in its practice, including potential infections at multiple port sites, coupled with limited field-of-view and depth perception, has led to the consideration of alternative and alleviated methodologies. Previous studies supporting the introduction of magnets in conjunction with other laparoscopic methods suggest possible alleviation of surgical healing time and invasiveness and therefore have prompted an investigation of using magnets to resolve issues concerning laparoscopic hysterectomies. Contrived to resemble a true uterine prolapse, the study design considerations included a laparoscopic box simulation that was constructed to combine the basic FLS tasks of cutting patterns and peg transfer (Ritter & Scott, 2007). Trials consisted of a controlled trial modeling the current standard of care with comparison to a magnetic-enhanced trial. A numerical score system was developed to account for speed and errors encountered by participants. Participants were composed of 22 undergraduate students enrolled in a beginnerâ&#x20AC;&#x2122;s laparoscopic course. Analysis of score values via a paired t-test illustrated a significant difference (p = 0.0008) between techniques, indicating that the use of magnets caused a decrease in the overall efficiency of the procedure. This comparative analysis of traditional laparoscopy and the integration of magnets serves to provide preliminary insight on novel applications in laparoscopic procedures and additional information surrounding patient outcomes following prolapse repairs.

Introduction Female pelvic floor dysfunction can manifest itself in a multitude of conditions, with general increases in prevalence following advancement in age, menopausal status, obesity, vaginal childbirth, connective tissue disorders, and chronic straining (Milsom & Gyhagen, 2014; Smith et al., 2014). Previous studies suggest the global prevalence of pelvic organ prolapse is estimated to be around 60% and between 2-20% for all parous women and premenopausal women, respectively (Dangal, 2005; Smith et al., 2014). These dysfunctions are often encountered in the form of uterine prolapse, commonly described as the descent of the uterus toward or through the opening of the vaginal canal following a defect in the connective tissues and pelvic musculature upholding the uterus (Uterine prolapse, 2019). According to a study conducted by the Department of Urogynecology at the University of Queensland, the preferred surgical treatment for uterine prolapse is a vaginal hysterectomy (Maher et al., 2001). In most cases, a laparoscopic approach is utilized to conduct the hysterectomy procedure. The general philosophy behind the laparoscopic technique was first demonstrated by Dr. Hans Christian Jacobaeus in 1901 and later reintroduced in the late 1980s by Dr. Hans Troidl

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(Kelley, 2008). The invention of laparoscopy has arguably transformed the field of surgery, with its proven ability to improve overall postoperative complications, such as decreased recovery time and preservation of the natural state of the human body with minimally invasive incisions (Best & Cadeddu, 2010; Park et al., 2007). Each incision made in previously standard operative procedures has generally been proven to ultimately lengthen the recovery time for the patient and suggests the rise of comorbid complications later in life, in comparison to laparoscopic methods. Differing from the historical lateral hysterectomies, a laparoscopic hysterectomy is a surgical procedure involving the use of minimally invasive instruments to effectively ligate the ovarian arteries and veins to then prepare the removal of the uterus vaginally (Lange et al., 2019). This procedure is initiated with a small incision site, or port, that is made in the navel region of the abdomen where a laparoscope (a thin fiber-optic tool characterized by its high-intensity light and high-resolution camera) is inserted into the abdominal wall for internal visualization of the pelvic region for surgical viewing (Shiel Jr, 2018). Two subsequent incisions are made in the lower


Materials and Methods Preparation of Mock Laparoscopic Box: The simulation was fashioned from a plastic box (STERILITE, 2018) with a camera, mimicking a body cavity and laparoscopic instruments, similar in design to previously established simulations of the Fundamentals of Laparoscopic Surgery (FLS) curriculum, provided in collaboration with the American College of Surgeons (Ritter & Scott, 2007). To constitute a similar environment to the abdomen in vitro, a balloon filled with superabsorbent water polymer beads was

Original Research

abdominal region, where metal tubes referred to as trocars are inserted as a passageway and mode of mobility for the laparoscope and laparoscopic tools. The uterus is eventually detached from its supporting connective tissues and ligaments and removed via the vaginal canal. Postoperative outcomes following this particular procedure include faster recovery time, enhanced cosmesis of the preoperative state of the body, and decreased risk of complications such as significant blood loss, post-operative pain, and scarring (Laparoscopy Laproscopic Hysterectemy, 2005). Recently, other modern surgical interventions have incorporated the use of magnets in vivo to facilitate fewer incisions made in the body cavity (Rice, 2018). With the potential expansion in the use of magnets within surgical procedures, surgeons would be able to take advantage of the magnetic forces and attach a magnet internally for surgical use (Best & Cadeddu, 2010). In theory, after the trocars are inserted in the abdomen, the magnet would be introduced internally through a detachable magnetic forceps grasper and attached to the target organ. The magnetic detachable grasper will be temporarily attached to the organ and an external magnet would be introduced, allowing a magnetic force to be created, with only the abdomen in between the two magnets. It is postulated that the force between the two magnets would be strong enough to allow manipulation of the organ, but not forceful enough to puncture the skin or any underlying membrane layers. Once the magnets are connected, the surgeon would be able to use the external magnet to move the organ to its desired location without having to create another incision. This study was tasked with the introduction of magnets in laparoscopic procedures to deduce if the number of incisions made in the procedure could be decreased by at least one incision site. In reference to Image 1, a diagram depicting incisions for a laparoscopic hysterectomy, it is hypothesized that incision number three, normally used for an additional forceps, could be eliminated in favor of an alternative magnetic manipulation. It is also postulated that the introduction of magnets would allow enhanced visualization of the intraabdominal region and would provide additional, enhanced mobility for other inserted instruments to precisely access organs and avoid inadvertent punctures to surrounding organs during the procedure. Placing importance on minimizing the invasiveness of laparoscopic surgery with the use of magnets could serve to maintain the integrity of the preoperative state of the body via the decreased amount of incisions capable of producing scarring and coincidentally increase overall patient satisfaction following surgery.

UTERUS VAGINAL CANAL

Image 1. An image depicting incision sites in a traditional laparoscopic hysterectomy used to treat a prolapsed uterus. LAVH (Laparoscopic-assisted vaginal hysterectomy) wrapped in gauze to represent a membranous-like uterus. The balloon was suspended in the laparoscopic box by multiple rubber bands that were attached to each corner of the box to simulate similar resistance and support provided by the ligaments surrounding the uterus in vivo. A piece of Styrofoam was sculpted into a pelvic-like shape with an anatomically designated area for the balloon, or uterus, to fit into. Ambipolar N52 Neodymium Disc magnets were attached under the gauze surrounding the balloon to resemble the internal attachment of the magnet to the uterus. An external magnet structure was created by attaching two magnets, of the same grade, between four sticks of bamboo to allow the participants to move the magnets outside the box. Ambipolar N52 magnets are a type of rare-earth, permanent magnet composed from an alloy of the elements neodymium, iron, and boron. The magnets were chosen due to their strength and ability to hold and support the weight of the balloon, roughly half a pound. Participants: The participants were composed of 22 pre-medical freshmen enrolled in BIO 1V90, the Laparoscopic 1 and 2 courses at Baylor University (Waco, TX). The students were taught beginner level laparoscopic skills over five months and were tested for proficiency in skills such as pegboard exercises, cutting a paper circle, and tying a loop suture. After achieving appropriate proficiency, the participants acquired a sufficient level of skill necessary for the completion of the simulation. Data Collection: The simulation was designed to have the participants pull the balloon (uterus) above the carved Styrofoam (pelvis), cut the specific rubber bands (ligaments) suspending it, and releasing the balloon safely into the designated area of the Styrofoam

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These error points were assigned based on the severity of the error that they would account for in actual laparoscopic surgery. For instance, puncturing the balloon or ripping the gauze was worth the most points due to the postoperative repercussions of puncturing the uterus or ripping its tissue. A “score” range, totaled out of 300 points, was formulated as follows: a base number of 300 minus the time (seconds) the trial took minus each error encountered in the trial

Image 2. An opened sample laparoscopic box depicting the pelvic region in which a laparoscopic uterine prolapse repair is to occur. (a general setup of the task is exhibited in Image 2). For proper insertion and use of laparoscopic tools in the simulation, each box included portholes, stabilized with trocars, indicative of incision sites that would be made in the abdomen for the procedure (exhibited in Image 1). For the controlled test, the participants used both ports for the insertion and use of laparoscopic instrumentation, a forceps and a pair of scissors, as traditionally utilized in laparoscopic hysterectomy procedures. For the variable simulation, the participants were only allowed to use one port for the insertion of the scissor-modified forceps; in place of the grasping forceps, a magnetic-like joystick was placed outside of the box to maneuver the balloon, already enhanced with an inner magnet, to the desired position for the simulation. The scoring range was designed similar to the FLS scoring metrics the participants used in previous proficiency training and tests (Ritter & Scott, 2007). A normalization score of 300 was used as a base starting number to account for and properly analyze the proficiency of each participant regarding the task given. The successful completion of the task was timed and subtracted as a measurement of efficiency and precision; procedural error points were accounted for by subtraction using the following scale shown in Table 1: Description of Error

Points Accounted For

Missing the Styrofoam target

-5

Cutting the bands in the wrong spot

-10

Puncturing the balloon or ripping the gauze

-20

Similar to the FLS scoring system (Ritter & Scott, 2007), a high score indicated superior rating, with factored-in considerations of both the efficiency and error component to the overall scoring. A total of five trials were run for both the control and variable experiments. The trials were collected back-to-back with an expectation that the score would improve as time proceeded. The control trials were collected first followed by the variable trials to account for the interference of the false appearance of better scores in the variable trials due to experience with the exercise. The two varying tests were collected one week apart, with the participants not allowed to practice laparoscopic skills in between. All twenty-two students performed a total of ten trials, five of each test, providing a total of 110 trials for each test.

Results For the best, unbiased statistical analysis, the “scored” data values for each trial of both the control and treatment experiments were averaged for each participant. The “average score” observation for both experiments was then paired together by participant and ran in RStudio Version 1.2.5033 as a paired student t-test1 . A significant t-statistic value = 3.9242 and a reported p-value = 0.00083 (Figure 1, Table 2) were recorded. With assumptions of the model holding4 (Figure 2, Table 3), the hypothesis was rejected that there was no difference between the two laparoscopic techniques. To determine which method was faster, summary statistics (Table 4) were compared between 1

“A paired t-test is used to compare two population means where you have two samples in which observations in one sample can be paired with observations in the other sample. Examples of where this might occur are before and after observations” (Shier, 2004); contrary to a z-test, this test is more robust for assumption of normality of populations. 2

The t-statistic is used in conjunction with a Gaussian curve to formulate a p-value for confirmation/rejection of the null hypothesis 3

“The null hypothesis postulates the absence of an effect, such as no difference between two groups, or the absence of a relationship between a factor and an outcome. The smaller the p-value, the greater the statistical incompatibility of the data with the null hypothesis, if the underlying assumptions used to calculate the p-value hold” (Wasserstain & Lazar, 2016). 4

Table 1. A table representing descriptions of errors accounted for in the numerical scoring system and their corresponding score values.

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Assumptions such as independence and normality between the residuals used for calculation of the t-test (Paired samples t-test in R, n.d)


Histogram with Normal Curve

Frequency

Table 3: A Shapiro Wilkes’ Test for Normality8, indicating a wellmodeled distribution of the paired difference in scores for the modeling of time efficiency and errors within the uterine prolapse repair simulation (n=22).

3 2 1 0 -50

0 50 100 Control-Magnet Difference between Means

Figure 1. 2-dimensional graph showcasing the distribution of the frequency (binwidth=5) of each difference in mean score values of the control experiment in comparison to the treatment experiment. The central tendency is roughly observed around 23, with 95% of differences lying under the normal distribution between 10 and 36; this is in accordance with the difference and CI intervals calculated through the paired means statistical analysis.

Mean of t-statistic Degrees of Freedom Difference

μ=22.956

t=3.924

21

P-value

95% Confidence Interval9

0.0007791* [10.79015, 35.12318]

Table 2: A table representing the t-test results of using each participants’ score values. A t-test conducts a measure of a significant difference between the mean values of the control and treatment experimental data.

Plot of Quantile Distributions

Score

250

200

Experimentation Grouping Control Magnet Variable

150

Figure 2. A one-dimensional box plot distribution of each treatment population score values (with n=22 for each treatment). The boxes are indicative of the interquartile ranges (of 25 and 75% respectively), and the median (50%) by the black lines within the boxes. The tails extending outwards from the box indicate accepted maximum and minimum values of the model.6 Stars indicate outlier values of each population.7

Group

Variable of Analysis

Population Count

Mean

Standard Deviation

Control

Score

n=22

264.104

20.494

MagneticManipulation

Score

n=22

241.147

30.548

Table 4. Descriptive statistics depicting the mean and standard deviation of the control and treatment values, using data from the developed score system.

the mean score values of each treatment. It was determined that mean of differences observed between the recorded “scores” of each population5 was μ=22.956, with the control experiment exhibiting a mean (average score) of μ=264.104 and the variable experiment had a mean of μ= 241.147. It was concluded that the more effective laparoscopic measure, within this design study, was the control method— for both efficiency and error reduction.

Discussion This study was conducted to heighten understanding of the incorporation of magnets in abdominal and pelvic laparoscopic procedures. With the underlying conjecture that the introduction of magnets to laparoscopic surgery could provide an alternative method for hysterectomies to treat prolapsed uteri, it was presumed that this procedure would allow for better outcomes for women who suffer from uterine prolapse. The incorporation of magnets would provide better postoperative outcomes through the efficiency in both surgery and recovery time through the minimization of incision sites and possible negative outcomes due to surgical cutting and puncturing errors. The results yielded through this design study were opposite of the hypothesized outcome of the introduction of magnets 5

Assuming difference calculated by control(avg)- magnetic (variable, avg)

6

See, (Statistics, n.d.)

7

After careful review of each outlier (according to the methods prescribed by Joshua Patrick, PhD Baylor University (Patrick, 2019)), no outliers were deemed necessary for removal from the results. 8

See, (Paired samples t-test in r, n.d)

9

We can be 95% sure that the true mean decrease lies somewhere between these; for more on CI, see (Paired samples t-test in r, n.d)

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7 6 5 4


Original Research

to an abdominal laparoscopic surgical procedure. Incorporating magnets led to a negative outcome in the completion of a uterine prolapse repair. Specifically, the reported p-value (p = 0.0008) from the paired student t-test indicated there was a significant difference in the scores between use and no use of magnets. The simulationâ&#x20AC;&#x2122;s control treatment, modeled off of the current standard of care in laparoscopic hysterectomies, yielded a higher mean average score, at 264.104; conversely, integrating magnets produced a mean average score of 241.147. After careful consideration and analysis of the experimental method used, methods of simulation, and materials available, many confounders surrounding the study design were proposed to possibly counteract the results obtained in this experiment and are outlined below:

optimal way to collect data. The current scoring guide was one dimensional in its variable analyses content, as it only looked at the comparison in the overall score a participant received, with little to no consideration or weight of if the score was achieved primarily by efficiency or by lack of error terms. Since the purpose of this study was multifaceted, with an aim for a reduction of incisions site, overall efficiency, and reduction of procedural error, it could be proposed that a new scoring guide is devised for similar experiments in the future that allows for statistical consideration and analyses of each component separately to get a well-rounded conclusion for each component.

Selection of Participant Population: The test subjects utilized in this experiment may have not been accurate representatives of the target population, as they were amateur college freshman students with minimal knowledge and training in the laparoscopic procedure within the areas of instrument manipulation and depth perception, in comparison to actual surgeons with an FLS training certification. The students, therefore, may have adversely been inadequate in their performance of a simulation procedure.

Studying the integration of magnets into laparoscopic procedures identifies and infers its potential use in hysterectomies for uterine prolapse repairs for women. Ultimately, it must be recognized that magnets in surgery are still highly experimental and thus require further investigation on their functionality and applicability for optimized study designs that allow for appropriate mimicking of similar laparoscopic procedures. This experiment functioned primarily as a preliminary study to indicate how magnets could be used in a laparoscopic procedure with regards to the anatomical region of the pelvis and associated dysfunction and disease. With regard to this study design, many errors were reported. Some errors, if present in both studies, were natural blockers of their own results and may, therefore, have minimized their weight as overall confounders to these results achieved. The errors unique to the variable test could be proposed as the biggest confounders leading to unexpected results, calling for optimization in the future. Although the reports of this study were found to satisfy all statistical assumptions, deeming them valid results, it is still an opinion that this particular experimental method would be tested on experienced, trained surgeons with proper surgicalgrade instruments. The prediction of the outcomes may not reproduce similar results reported in this study but rather results in favor of the magnetic manipulation technique. Overall, this study still offers some possible insight into magnetic laparoscopy and is worth consideration. Even with the time increase reported in more than 75% of the participants, all were able to successfully complete the task only using one port. Regardless of the results achieved in this study, additional trials must be conducted on more accurate models such as a cadaver or true-to-size uterus model, along with FLS certified surgeons to simulate similar clinical environments before considering the adoption of magnets as an additional technology for use in laparoscopic settings.

Design Study-Physical Setup and Associated Mechanical Errors:

To keep uniformity between test subjects, all boxes were constructed in the same manner; these boxesâ&#x20AC;&#x2122; dimensions, however, were not similar in dimension to the human body, which is less rigid in form. This made the spacing and placement of the pelvis and prolapsed uterus quite difficult to manipulate, possibly leading to slower times. Another source of design error was found in the physical design of the magnetic joystick itself. During the experiment, many of the subjects expressed issues in the use of the magnetic joystick for manipulation; in fact, these mechanical errors reduced the overall participation size, as some boxes had to be swapped out and trials discontinued or thrown out due to this error. This decision was decided upon to avoid the risk of confounding data values, due to the time and experience component that comes with having to learn and replicate a task over many trials, and thereby contradicting the unbiased measures set aside in the original study design. In addition, the laparoscopic forceps, scissors, and magnets were of a lower grade quality than those used in real surgery and may have also led to time and error complications in both treatments. For example, many test subjects had great difficulty cutting within the simulation furthering their complications with the task, leading to possible discrepancies with their performance in the experimental trial. Optimization of Scoring Methodology: Timing considerations were measured to be indicative of the efficiency of the surgeon completing the assigned task in surgery. In addition, error points were assigned based upon the severity of consequences for the type of error committed. While the overall scoring of this experiment was modeled in accordance with current FLS scoring guides (Ritter & Scott, 2007), it is recognized that this may have not been the most

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Conclusion

Acknowledgements We would like to thank Alexis Ford, Rianna Larios, Katie Dearth, Kyle Langston, Taylor Weber, Hank Chen, Madison Ambrose, and Earle Hall Living and Learning Center for their continuous help and support. We would also like to thank the BIO 1V90 student participants for their cooperation and willingness to participate in our study.


References

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Best, S. L., & Cadeddu, J. A. (2010). Use of magnetic anchoring and guidance systems to facilitate single trocar laparoscopy. Current urology reports, 11(1), 29-32. Dangal, G. (2005). A brief overview of genital prolapse in women. Nepal journal of science and technology, 6(1). Doshani, A., Teo, R. E., Mayne, C. J., & Tincello, D. G. (2007). Uterine prolapse. Bmj, 335(7624), 819-823. https://www. bmj.com/content/335/7624/819.short Kelley W. E., Jr (2008). The evolution of laparoscopy and the revolution in surgery in the decade of the 1990s. JSLS : Journal of the Society of Laparoendoscopic Surgeons, 12(4), 351–357. Lange, S., Higgins, R., Carter, J., & Rivlin, M. (2019). Laparoscopic Hysterectomy. Retrieved from https:// emedicine.medscape.com/article/1839957-overview#a1 Laparoscopy Laparoscopic Hysterectomy - Kaiser Permanente East Bay. (2005). Retrieved from https://thrive. kaiserpermanente.org/care-near-you/northern-california/ eastbay/departments/laparoscopy/ob-gyn/laparoscopichysterectomy/ Maher, C. F., et al. (2001). Uterine preservation or hysterectomy at sacrospinous colpopexy for uterovaginal prolapse?. International urogynecology journal, 12(6), 381-385. Milsom, I., & Gyhagen, M. (2014, September). The epidemiology, natural history and prevention of pelvic floor disorders. The Global Library of Women’s Medicine. Nucleus Medical Media (2019). Supraventricular laparascopic hysterectomy [Digital image]. Retrieved from https:// ebsco.smartimagebase.com/supraventricularlaparascopic-hysterectomy/view-item?ItemID=31370 Paired samples t-test in r. (n.d.). Retrieved April 05, 2020, from http://www.sthda.com/english/wiki/paired-samples-t-testin-r#visualize-your-data-using-box-plots Park, S., Bergs, R. A., Eberhart, R., Baker, L., Fernandez, R., & Cadeddu, J. A. (2007). Trocar-less instrumentation for laparoscopy: magnetic positioning of intra-abdominal camera and retractor. Annals of surgery, 245(3), 379–384. doi:10.1097/01.sla.0000232518.01447.c7 Patrick, J., Baylor University. (2019). Module 3.4 Checking for Outliers. Retrieved April 05, 2020, from http://www. jpstats.org/Regression/ch_03_04.html Reich, H., DeCaprio, J., & McGlynn, F. (1989). Laparoscopic hysterectomy. Journal of Gynecologic Surgery, 5(2), 213-216. Rice, L. (2018, September 12). How A Texas Physician Uses Magnets To Make Surgery Less Invasive. Retrieved from www.tpr.org/post/how-texas-physician-uses-magnetsmake-surgery-less-invasive Ritter M., Scott, J. (2007). Design of a Proficiency-Based Skills Training Curriculum for the Fundamentals of Laparoscopic Surgery. Surgical Innovation, 14(2), 107-111. Shiel Jr., W. C. (2018, December 27). Definition of Laparoscope. Retrieved from https://www.medicinenet.com/script/ main/art.asp?articlekey=9931 Shier, R. (2004). PDF. UK: Mathematics Learning Support Centre (StatsTutor). Retrieved from http://www.statstutor. ac.uk/resources/uploaded/paired-t-test.pdf

Smith, T. A., Poteat, T. A., & Shobeiri, S. A. (2014). Pelvic organ prolapse: an overview. Journal of the American Academy of PAs, 27(3), 20-24. Statistics, How To (Ed.). (n.d.). Box plot (box And whiskers): How to read one & how to make one in Excel, TI-83, SPSS. Retrieved April 05, 2020, from https://www. statisticshowto.com/probability-and-statistics/descriptivestatistics/box-plot/ STERILITE. (2018). STERILITE 19849806 18 Quart/17 Liter Ultra Latch Box, Clear with a White Lid and Black Latches. Retrieved from https://www.amazon.com/ Sterilite-19849806-Quart-Latches-6-Pack/dp/ B002BA5F52/ Uterine prolapse. (2019, June 1). Retrieved from http:// www.mayoclinic.org/diseases-conditions/uterineprolapse/symptoms-causes/syc-20353458. Wasserstein, R. L., & Lazar, N. A. (2016, March). The ASA statement ON p-Values: CONTEXT, process, and purpose. Retrieved April 05, 2020, from https://amstat.tandfonline. com/doi/full/10.1080/00031305.2016.1154108#. Xoi714hKhPY


Original Research

The Impact of Social Media on the Short-Term Memory of Teenagers Katie Nelson¹, Pamela Miller, PhD.² ¹Department of Psychology & Neuroscience, Baylor University, Waco, TX ²Department of Psychology, University of Denver, Denver, CO

Abstract Social media sites such as Instagram have increased dramatically in popularity, especially with teenagers. However, there is still research to be done on how social media may influence important psychological growth or aspects of ordinary brain function, such as short-term memory. This experiment collected fifty voluntary participants and measured the impact that social media has on short-term memory in teenagers. Each volunteer was placed into a control (without social media) or experimental group (with social media). The participants were given two minutes to memorize as many words as possible from a 51-word list. Then, they sat in silence or used social media for five minutes. Finally, the participant has two minutes to write down as many words as they could recall. The hypothesis that social media would negatively influence the short-term memory in teenagers was proven correct. The teenagers from the control group recalled more words than those from the experimental group. A possible conclusion drawn from this experiment is that using social media will negatively influence short-term memory and impair recall, while learning or studying.

Introduction Social media platforms are used everywhere by practically everyone. Whether it is a mom waiting to pick up her child or a college student sitting in class, people are occupying themselves through social media. Every upcoming generation seeks for their attention to be captured all the time and social media is that medium. With more people using social media everyday, it is vital to research how this new media affects humans and more specifically their memory. Memory Professors Richard Atkinson and Richard Shiffrin has established that memory is divided into three general stages: sensory, short-term and long-term. This idea was first proposed in 1968 and was the subject of both professors’ ongoing studies until 2016. They revealed a new understanding that short-term memory includes the concept of conscious working memory (Shiffrin, 1977). Absorbing most information is done without intention, unconsciously, and the working memory is used to pick up stimuli to remember. The memory process begins with sensory memory when immediate information is first recorded by senses. It quickly flows to the short-term (or working) memory, which is activated memory that holds information briefly. The working memory specifically is not just a temporary shelf for holding new information, but instead is an active “scratchpad” where the brain actively processes information by making sense of new inputs and connecting them to long-term memories. Long-term memory is a somewhat permanent and limitless storehouse for all information, including skills, knowledge, andexperiences (Myers & DeWall, 2018). Every human records information

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through this memory-process, but interference in any stage can change the way memories are recorded and stored. This threestep process is widely accepted and used to understand how humans document outside input. Short-term memory usually requires information to be continually repeated to be retrieved later. Typically, information is held for about 20-30 seconds and then begins to fade. Effortful processing is used to encode new information but requires conscious effort and attention (Loftus, 2018) to keep information in the brain and later stored as long-term memory. These two stages of memory work together to help process and save new information. A research experiment run by Alan D. Baddeley, British psychologist, and professor of psychology at the University of York explored how memory span is related to the length and occurrence of an English word. Participants in his experiment read aloud different words that had one of two lengths: one syllable and five syllables. He found that recall with the onesyllable length word set was more likely since the short-term memory has a limited life and the more information it is asked to hold, the less likely it will be remembered (Baddeley et al., 1975). It makes logical sense that Baddeley would find that words of shorter length were recalled more than longer ones. The ability to recall correlates with the length of the word in such a way that future experiments should use a variety of words to get a balanced experiment. Though different aspects of human memory have been studied throughout the years, one thing is clear. Memory can be easily changed, and no memory recalled is a correct permanent representation of the prior event. With this, it is important to


see how memory could be impacted by newer technology, such as social media.

Methods To test the effects of social media on short-term memory in teenagers, a group of fifty high school teenagers (ages fourteen to eighteen) were collected. The researcher publicly advertised for volunteers to participate in the study and participants contacted the researcher through email or in person. The participants gave informed consent to participate and have their data reported anonymously. Each participant studied a list of fifty-one words for two minutes, consisting of seventeen nouns, seventeen verbs, and seventeen adjectives. The nouns were listed on the left in one column, the verbs were listed in the middle column and the adjectives were listed in the right column (refer to Figure 1).

Figure 1. List of words given to study participants.

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Original Research

Social Media Humans are social creatures by nature. In the recent past, people have developed a new way to connect with others without actually being in the same physical location. Social media is a communication tool whereby people use technology to connect with others, share content and network. More importantly, social networking sites create a database for sharing information with others (Hanson, 2018). Examples of social media sites are platforms like Instagram and Facebook. Social media can be used to advertise products, learn new information, check-up on friends and much more. As technology expands, social media is becoming more prevalent around the world. When social media was created, it was intended to be used for giving access to content to those who wouldn’t usually have it, connecting scholars from around the world, helping create new ideas to build the future, and increasing collaboration between different people (Redecker et al., 2010). Depending on the user, social media can be positive. However, today more people are using social media for different reasons. With the increase of social media users, the world is facing new problems. Distraction is a large problem for students in college and secondary school. Jessica Mendoza, a psychology professor at the University of Alabama, experimented with her colleagues to test how cell phones or social media may influence their learning abilities in the context of a lecture. There was a group of students who kept their phones and one that didn’t. The students who had the cellphones were sent distracting messages during the lecture and then both groups were tested over the lecture at the end. The researchers found that the group that was distracted by the technology performed worse on average than the control group, specifically over the material review at the end of the lecture (Mendoza et al., 2018). This is not a rare occurrence, though. Researchers in another study run by the University of North Texas found that social media interruptions and distractions during medical learning activities (live and recorded lectures) can have an important impact on learning outcomes. The students who were distracted by social media received lower test scores in medical school than their peers (Zureick, 2018). Social media has increased the use of cell phones in class, meetings, social interactions and almost everywhere else. Social media connects people but it should not be abused: evidently, social media can negatively impact people’s lives. Today, numerous people are wondering what impact social media may have on memory. A study by Scott T. Frein, head of the department of psychology at Virginia Military Institution and his colleagues proved that the frequency usage of Facebook was correlated with memory recall. The researchers used 44 participants. By asking the average amount of hours a day the participant used social media, they were put into two groups: the low-frequency group and the high-frequency group. Then, they gave each person a recall test and compared data. Their results showed that on average, people in the high-frequency social media group had worse vocabulary recall than the other group. The high-frequency group recalled on average about 15% of the words and the low-frequency group recalled on average 22% (Frein et al., 2013). This study showed a correlation

between lower recall ability and high usage of Facebook. Their correlation of social media and memory was based on the general previous usage that the participant noted. Even though this experiment was a general introduction, findings like these support more reason to research social media’s impact on human life, especially memory. Social media becomes more popular with each generation. Teenagers use social media, especially Instagram, more than any other age group (Smith & Anderson, 2018). However, there are extremely few studies involving social media that use teenagers as subjects, instead prioritizing those that are 18 or older. There is a need for studies particularly involving teenagers and social media regarding to short-term memory. To evaluate social media on teenagers, a specific demographic was used for this study: participants ages 14-18 from one high school. It is presumed that social media will negatively impact short-term memory in teenagers. The researcher hypothesizes that the experiment group using social media during the “study break” will recall fewer words overall. It is expected that the short-term recall of the control group will be higher than the experiment group using social media.


Original Research

The participants were separated into two groups: the control and experimental group. After studying the list of words for two minutes, the control group participant sat in silence for five minutes, while the experimental group participant scrolled through an Instagram feed, using an account designed by the researcher (Appendix A). The Instagram account followed four accounts to create a feed: a Do-It-Yourself account (@ diy.learning), an account posting nature’s best (@nature), Instagrams official account (@instagram) and the account for the school’s public announcements. After the participant sat in silence or scrolled through the feed, a blank lined piece of paper was given to the participant to write as many words as they could recall for two minutes. If the word was on the list then it was considered correct when counting for total correct words. The participant was not marked down for words they wrote that were not listed or close misspellings. If the participant wrote a word that they had already written, it was only counted correctly once. After the two minutes had passed, the researcher thanked the participant and offered to sign off on one hour of volunteer service for their participation. To justify this experimental research method, multiple sources regarding social media and memory was analyzed in relation to their method. Because it was observed that the average number of participants in studies of this caliber was around forty to eighty, this study used fifty participants (Frein et. al, 2013; Savill et al., 2018). By advertising publicly and requesting all students to participate, the sample bias (of choosing participants) was eliminated. The researcher chose fifty-one words only consisting of verbs, nouns, and adjectives because other memory tests given in previous studies used these grammatical categories (Baddeley, et al., 1975). This mitigated individual bias toward certain parts of speech. All the words on the list were chosen because of their non-triggering tendencies, varying syllable count and the simplicity of each word considering their neutral connotations. Participants alternated from the experiment to control group to further guarantee random assignment. The participant received two minutes to study the words and another two minutes to write down the recalled words. This is so the words would stay in the short-term working memory and not linger towards long term memory (Craik & Watkins, 1973). To recreate real study habits and circumstances, the break was five minutes, the average time for a teenage “study break” (Epstein et al., 2016) The Instagram account followed four accounts that guaranteed to not show provoking or triggering content, but offered a feed that still captured the audience’s attention. It was important for the Instagram feed to be engaging to resemble the real distractions social media may offer. The accounts used were verified through Instagram, meaning that the accounts had to be neutral and could not be controversial. Towards the end of the experiment, the researcher gave a blank piece of paper instead of a multiplechoice or fill-in-the-blank test to initiate a recall, not retrieval or review, of what the participant remembered. How does the use of social media, specifically Instagram, influence short-term memory in teenagers? This experiment directly tested social media’s effect on teenagers’ shortterm memory by incorporating Instagram into the test. The time intervals chosen for the studying, break, and recall are complimentary to how long information remains in short-term

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memory. With the break being only five minutes, the brain kept the words in the working short-term memory for this short time. The study session was two minutes and the recall time was made equivalent to allow enough time for each participant to go over each word but not store into long-term memory. By only using teenagers ages fourteen to eighteen in this study, the method clearly connects to the targeted high school population. This study followed the theoretical experiment method because it was testing the direct effect of one variable on another. Many studies referenced above were correlational studies, not specific cause and effect. This experiment was not a correlational study because a correlational study can only show whether two variables have a relationship but do not measure the extent to which they affect each other. By using social media during the test, the quantitative results will prove whether social media has a direct impact on the number of words recalled. This study was a one-time experiment because data was collected and analyzed to provide a clear conclusion. One important limitation of this study is the population size because similar experiments usually utilize a larger sample size, which gives a better estimate of the targeted population. A larger sample size would give more accurate results if this study was being applied to various populations. The ages of the participants of this study range from fourteen to eighteen because few high school teenagers are thirteen or nineteen. A small limitation to the method was that even though each experimental group participant saw content from the four accounts, that doesn’t mean every participant viewed all the same posts. The followed accounts posted videos and photos frequently so depending on the day, each participant saw similar posts but not necessarily the same photos as the other participants. This was only a small limitation because in the real world, depending on the day and time someone uses social media, they would see different posts no matter how often they use social media. So even though the posts themselves varied, the content was still similar among all participants because they only used social media through the four accounts during this experiment. In future studies, this method could use alarger sample size to apply to different or larger populations. Due to the availability of resources used and close following of the scientific method, this study is replicable but can be altered in future experiments.

Results Out of fifty participants, there were thirty-four females, fifteen males, and one unidentified tested. Regarding the specific ages of the teenagers, Figure 2 gives a visual demographic. The total correct words recalled for each individual from each group were compared for the control and experimental group (Appendix B). A two-tailed two-sample t-test was used to see if the data collected was statistically significant, meaning the two groups have a relationship that is caused by something other than chance. For the data to be statistically significant, the p-value had to be less than the alpha-level of 0.05. The p-value for this test was 0.0297, showing the data was statistically significant. To detect the extent of the effect of social media on memory, a one-tailed two-sample t-test found the p-value to


Age Demographics

Average Correct Different Parts of Speech Recalled Average Correct Words Recalled

Number of Participants

10

5

7 6

Experiment Control

6.80 5.96

5 4.44

4 3.72

3

2.92

3.12

2 1 0

Nouns

Verbs

Adjectives

Part of Speech 0

14

15

16

17

18

Age of Participants

Figure 2. Age demographic of the participants in this study.

Number of Correct Words Recalled

Average Correct Words Recalled 25

Experiment Control

20

or either of these terms when writing words on the paper. These patterns of words recalled between the different groups does not pertain to the exact research the question addressed— how does social media impact short-term memory, in teenagers. However, it does provide insight into why the results may have produced this data.

Discussion

15

10

5

Figure 4. Number of average correct parts of speech recalled for each respective group.

11.92

14.96

Group

Figure 3. Number of average correct words recalled for each respective group. Error bars depict one standard deviation. be 0.0148 (also significantly less than 0.05). The average correct words recalled and standard deviations for each group are shown in Figure 3. The standard deviation for both groups differs largely as well, as shown in Figure 3. For the experiment group (social media usage) the standard deviation was 3.3655 and for the control group, it was 5.8344. The total average words recalled accounts for how many words the participant wrote down and is not dependent on whether the words are correct or not. The total average words recalled for the experimental group were 17.68 and for the control group, it was 15.28. Regarding the actual words, nouns were the part of speech most likely recalled, next to adjectives and then verbs, on average. Refer to Figure 4 for the exact averages of the different parts of speech. Another observation between both groups was that many of the words recalled on average, had double letters such as in green or mattress. A final observation about the types of words recalled often between both groups was that the participant would usually recall either the first or last term in the list. The first term was ‘star’ (noun) and the last term was ‘busy’ (adjective). Thirty-four out of the fifty participants recalled both

Interpretation and Significance of Results As stated in the results, the standard deviation had a p-value ≤ 0.05, concluding the results to be statistically significant. The one-tailed t-test showed that the control group had less of an effect on short term memory compared to the experimental group. When analyzing the data collected, the existing hypothesis proved to be correct: social media would negatively impact short-term memory in teenagers. Comparing the data sets more closely, the total average words recalled for the control group was 17.68 and for the experimental group, it was 15.28. These are all the words written on the piece of paper and counted whether they are correct or not. The total correct average words recalled for the control group were 14.96 and the experimental group was 11.92. The control group had a smaller difference between the correct and total average words recalled than the experimental group (2.72 vs 3.36). It was hypothesized that the neural networks required for short term memory (for students using social media in the experimental group) may have been overwhelmed and would not commit the correct words to memory. The students in the control group sat in silence during the five-minute break and could keep the words in their short-term memory without as much distraction. Regarding the total correct words recalled, the standard deviation, or the extent of variance from the mean, for the control group was 5.8344 and for the experimental group was 3.3655. Even though both groups had 25 participants, there was a large difference in the standard deviations. A possible hypothesis for the differing standard deviations was that the sample from the control group had a large variance of distractions not controlled in this experiment. This might be because the participants in the control group could have had extremely varying internal

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distractions (such as their emotions or thoughts while taking the test) and the experimental group had a similar variance in confounding distractions. If the sample size had been larger, the standard deviations would most likely be smaller. The standard deviations for the control and experimental group may have not been so different with a larger sample size. When the actual correct words recalled were further examined, a few patterns were observed. By separating the nouns, verbs, and adjectives into three separate columns, it was clear which words were recalled more often. On average, nouns were the most correctly recalled words, then adjectives and lastly verbs. In multiple studies, nouns were recalled more often than any other part of speech because they are more specific than other parts of speech and can usually produce a clear image of the object upon reading (Earles & Kersten, 2000). This also explained why verbs were recalled less often than adjectives because verbs describe a general action that can be recalled with concentration but adjectives can bring a specific image to the brain when reading. Another pattern noticed was that words with double letters (such as mattress, see or pretty) were also recalled more often. A finding by two psychologists, Donna-Marie Wright and Linnea C. Ehri, who study word comprehension in children, found this to be common because people’s knowledge was constrained by certain word structures (Wright & Ehri, 2007). The most recurring pattern, however, was the high frequency of the serial-position effect. This term refers to the tendency to recall information that was presented first and last in a list better than information presented in the middle (Murdock, 1962). The serial-position effect is widely recognized among many studies and explains why thirty-four out of the fifty participants recalled either the first (star) or last (busy) word in the list. No matter which group the participant was in, they most likely exhibited the serial-position effect or most often recalled words with double letters.

confounding variables; however the testing environments were very similar in noise level and light exposure. The researcher deemed it more important for the testing environments to be similar but didn’t require the participants to all be tested on the same day or at the same time. This study can draw many implications. Short-term memory was negatively impacted when teenagers used social media during their study break. In 2018, a study found that 89% of students describe their social media use to be almost constant or several times a day (Anderson et al., 2018). It can be inferred that since so many students use social media frequently, they may use it while studying, as a distraction, or simply use it in between study sessions. The information received while studying goes through the short-term memory, into working memory, and usually stored in long-term memory. This experiment clearly shows that using social media may negatively impact the way information travels through shortterm memory and eventually affects the amount of information stored in long-term memory. In turn, this could affect the recall of information and future test scores. This study does not show that bad test scores are caused by social media use, but future research could show a correlation.

Limitations and Implications The most prominent limitation of the research results was the sample size. It was predicted to be a limitation in the method but was emphasized by the large standard deviations. A larger sample size would have provided more accurate results and a smaller variance from the mean, making the conclusions more applicable to wide general populations. This experiment used volunteer participants from one high school, which also limited the extent to which the study’s results may be applied to other populations. Because the participants were voluntary, there may have been a certain uncontrolled bias because the researcher could not control which students would participate. If the study was to be repeated, the list of words presented in the three separate columns could be randomly assorted instead of separation by part of speech. This might show that the nouns were not significantly recalled more than the verbs and adjectives because of their positioning on the page. It would not have affected the results pertaining to the question of how social media impacts short term memory, but it could bring differing results involving the words’ certain parts of speech if this data was used for future experiments. The participants were not tested all on the same day or at the same time of day. This slightly impacted the results or the sample demographic makeup. The date and time were

Future Research This experiment could be changed in multiple ways and the data could be used for different research questions. First, if keeping the original question, the sample size could be larger and/or gather participants from different places. By doing this, the results may be applied to a more general population. Using different words, such as those that have a pattern in their structure or sound, could produce different results (Wright & Ehri, 2007). The words used in this experiment were random, neutral, and were not connected through meaning. This experiment could also use other social media sites like Facebook or Snapchat. Instagram was used as the social media platform for this study because it is the most commonly used platform today for this age group (DeMers, 2017). Different aspects of the raw data could be more closely studied. For example, the effect of gender and age differences in participants could be analyzed and correlated to short term memory. To analyze deeper, the actual words of an experiment could be examined more closely for patterns. For instance, a researcher could evaluate how many syllables are in the words most frequently recalled or determine which vowels are most frequently recalled. There are a few related questions that could be asked based on these findings. What would happen if the sample population

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Conclusion Social media interfered with the recall of the words presented in this experiment, leading to the conclusion that social media negatively impacted the short-term memory in these participants. The hypothesis that predicted this result aligns with research that indicates short-term memory is not limitless. Social media use may have overwhelmed short-term memory. With the conclusion being that social media negatively impacted short-term memory in teenagers, further research could be done to investigate the universality of this conclusion.


memory and reading aloud : semantic compensation for weak phonological processing across tasks. White Rose Research Online. Shiffrin, R. (1977). Human memory: a proposed system and its control processes. Human Memory, 1-5. Smith, A., & Anderson, M. (2018, September 19). Social media use 2018: demographics and statistics. Pew Research Center Wright, D., & Ehri, L. (2007). Beginners remember orthography when they learn to read words: The case of doubled letters. Applied Psycholinguistics, 28(1), 115-133. Zureick, A. H., Burk-Rafel, J., Purkiss, J. A., & Hortsch, M. (2018). The interrupted learner: How distractions during live and video lectures influence learning outcomes: Study interruptions and histology performance. Anatomical Sciences Education, 11(4), 366-376.

References Anderson, M., Jiang, J., Anderson, M., & Jiang, J. (2018, November 30). Teens, social media & technology 2018. Pew Research Center Baddeley, A. D., Thomson, N., & Buchanan, M. (1975). Word length and the structure of short-term memory. Journal of Verbal Learning and Verbal Behavior, 14(6), 575-589. Craik, F. I., & Watkins, M. J. (1973). The role of rehearsal in short-term memory. Journal of Verbal Learning and Verbal Behavior, 12(6), 599-607. DeMers, J. (2017, March 28). Why instagram is the top social platform for engagement. Earles, J. L., & Kersten, A. W. (2000). Adult age differences in memory for verbs and nouns. Aging, Neuropsychology, and Cognition, 7(2), 130–139. Epstein, D. A., Avrahami, D., & Biehl, J. T. (2016). Taking 5: work-breaks, productivity, and opportunities for personal informatics for knowledge workers. Frein, S. T., Jones, S. L., & Gerow, J. E. (2013). When it comes to Facebook there may be more to bad memory than just multitasking. Computers in Human Behavior, 29(6), 21792182. Globerson, S., Levin, N., & Shtub, A. (1989). The impact of breaks on forgetting when performing a repetitive task. IIE Transactions, 21(4), 376-381. Hanson, J. (2018). Social media. World Book Student. Loftus, E.F. (2018). Memory. World Book Student. Mendoza, J. S., Pody, B. C., Lee, S., Kim, M., & Mcdonough, I. M. (2018). The effect of cellphones on attention and learning: The influences of time, distraction, and nomophobia. Computers in Human Behavior, 86, 52-60. Murdock, B. B., Jr. (1962). The serial position effect of free recall. Journal of Experimental Psychology, 64(5), 482-488. Myers, D. G., & DeWall, C. N. (2018). Myers’ Psychology for the AP® Course (3rd ed.). New York, NY: Worth. Redecker, C., Punie, Y., & Ala-Mutka, K. (2010). Learning 2.0 promoting innovation in formal education and training in europe. Sustaining TEL: From Innovation to Learning and Practice Lecture Notes in Computer Science, 308-323. Savill, N., Cornelissen, P., Whiteley, J., Woollams, A., & Jefferies, E. (2018). Individual differences in verbal short-term

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was increased to all ages, not just teenagers? Since teenagers use social media more than any other age group, (Smith & Anderson, 2018) the results may be vastly different for someone older or younger. However, by limiting the sample size to just teenagers, this experiment could be focused on the group most affected by the distraction of social media. This research could be expanded to the correlation between study habits and test scores. If a student uses social media frequently while studying, their memory will be weakened, and the information may not be retained. When it comes time for the test, would the average student that studied with no social media distraction perform better than the average student that did use social media while studying? With further research, repeating experiments like this one, bigger implications and connections could be drawn to answer the real-world effects of social media on memory.


Original Research

Frequency of Binge Watching and its Emotional Impact Katie Soudek, Shawn J. Latendresse, Ph.D.

Abstract This study examined whether there was an association between participants’ frequency of binge watching and the emotions they associate with it. An online survey was administered to 66 college students and assessed emotions commonly associated with binge watching, such as loss of control, dependency, and positive emotions. A correlation analysis and three simple regression analyses were conducted on the data. There was a significant association between frequency and all three subcategories of the questionnaire, with negative emotions having a higher association than positive emotions. These results suggest that higher frequencies of binge watching have predominantly negative effects on viewers, and implications for future research are discussed.

Introduction There are many activities available on the internet, and the amount of content accessible to users grows every day. Watching video content is the fourth most prominent activity that Americans engage in, following only email, text/instant messaging, and social networking (National Telecommunications and Information Administration, 2017). TV shows have doubled in number in the last seven years with 487 shows premiering in America in 2017 (Shaw, 2018). Historically, TV watching was on a schedule dictated by the network that premiered the content. New platforms such as Netflix and Hulu, however, began releasing entire seasons of TV shows at one time to allow viewers to decide their own viewing schedule, encouraging a new viewing method called binge watching, which allows consumers to watch concurrent episodes without interruption (Jenner, 2016). Netflix (2013) defines binge watching as viewing two to six episodes of a TV show in one sitting. For this study, watching the appropriate amount of episodes (as mentioned in Netflix’s definition) in a single sitting qualifies as a binge, no matter what device or content provider the subjects use. Binging video content is an individualized activity, and it is difficult to determine the point at which TV watching becomes problematic (Jenner, 2017). In some cases, binging can have positive effects on viewers. One study researched effects of uplifting content on viewers’ emotions and overall outlook on life, finding that viewing uplifting content more often than others caused viewers to be more benevolent in their everyday lives (Neubaum, Kramer, & Alt, 2019). Also, Rubenking and Campanella (2018) found that binge watching was used to aid in viewers’ emotion regulation, allowing viewers to indulge in whatever emotions they preferred depending on what content they decided to watch. The ability to choose from so many types of content escalates instant gratification for the viewer, and instant gratification has been identified as a motivating

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mechanism for binge watching. According to Roberts (2014), in an impulsive society, we are inclined to amplify gratification in any given moment and ignore future repercussions because we constantly progress from one plane of gratification to another. Because gratification is now at our fingertips, it is more difficult than ever to delay satisfaction. Vast amounts of content on different platforms and ease of gratification are likely to cause a decline in consumers’ self-control, which leads to negative effects of binging. Reinecke and Hofmann (2016) suggest that media consumption falls into two categories: recreational and procrastinatory. Those with less self-control are more likely to use media to procrastinate their responsibilities, while recreational users are more goal oriented and are able to delay gratification. Panek (2014) suggests that one’s self-control concerning amount of media usage for leisure is affected by how many options are available. Because there are so many platforms of content available, viewers are more likely to give in to temptation despite having more important tasks to complete (Reinecke & Hofmann, 2016). As a result of instant gratification and lack of self-control, those who binge watch were more likely to impede their own goals (Walton-Pattinson, Dombrowski, & Presseau, 2016). One study found that TV watching was negatively associated with academic performance, which could be because many viewers report watching video content while attempting to finish important tasks simultaneously (Jacobsen & Forste, 2011). Procrastination and declines in productivity due to binge watching often result in the viewer experiencing guilt (Mehra & Gujral, 2018). In some cases, resulting guilt from binge watching causes the consumer to engage in more binging to postpone their responsibilities even longer (Panda & Pandey, 2017). Riddle, Peebles, Davis, and Xu (2018) suggest that at a certain point, binging could reveal characteristics of addiction


Methods Participants Participants consisted of 66 students (58 women, 8 men, Mage = 19.54 years, age range: 18-23 years) enrolled at a private Baptist university. They were recruited through snowball sampling. There were 9 freshmen, 29 sophomores, 13 juniors, and 15 seniors. Measures The scale used to measure emotional effects of binge watching was the Binge Watching Engagement and Symptoms Questionnaire (BWESQ Billieux et al., 2019). Frequency of binge watching is measured by the number of days the participant binge watches per week, scaled from 0 to 7. Binge watching engagement and symptoms questionnaire The Binge Watching Engagement and Symptoms Questionnaire (Billieux et al., 2019) is a measure of the emotions and symptoms associated with binge watching. It uses a 7-factor model that evaluates engagement, positive emotions, desiresavoring, pleasure preservation, binge-watching, dependency, and loss of control. These were developed from the literature on addiction, impulse control, and emotion regulation. Items include “I sometimes fail to accomplish my daily tasks so I can spend more time watching TV series” (Billieux et al., 2019, p. 2). Responses are recorded on a 4-point Likert scale that range from 1 (strongly disagree) to 4 (strongly agree). The questionnaire presented with an alpha reliability coefficient of α = 0.62-0.83, which is sufficient. Procedure Each participant completed a Qualtrics survey that consisted of the BWESQ, one question to obtain the frequency of each subject’s binges, and another to assess how long their binges last on average. Demographic information was included

at the end of the survey, asking age, classification, major, and gender. The survey then thanked the participants and provided the researchers’ contact information before being submitted.

Results Analyses focused on the participants’ scores in frequency of binge watching and three subcategories of the BWESQ: loss of control, dependency, and positive emotions. Frequency of binge watching had the highest correlation with loss of control, followed by positive emotions and dependency (see Table 1). A simple regression analysis was conducted using R software to determine if there was an association between frequency of binge watching and the three categories of emotions (see Tables 2-4). Frequency of binge watching and loss of control resulted in a significant association with R2 = 0.51, df = 68.82, p < .001. Frequency accounted for 51% of variance in loss of control. Next, the simple regression analysis for frequency and dependency resulted in an association with R2 = 0.23, F(1, 64) = 20.47, p < .001. Frequency accounted for 23% of variance in dependency. The final regression was conducted on frequency and positive emotions, resulting in an association of R2 = 0.32, F(1, 64) = 31.4, p < .001. Frequency accounted for 32% of variance in positive emotions. These results partly support our hypothesis because, as hypothesized, frequency accounted for some increase in loss of control and dependency. However, frequency also accounted for an increase in positive emotions, which does not support the hypothesis. The results of these tests suggest that the frequency of one’s binging is associated with emotions often connected to binge watching. Loss of control had the highest association, followed by positive emotions, then dependency. Due to the BWESQ subcategories’ significant associations with frequency of binge watching, it is suggested that one’s frequency of binge watching has an effect on their emotions toward it.

Discussion The aim of this study was to find associations between the frequency of one’s binge watching and various emotions. It was hypothesized that those who binge watch more frequently would experience more feelings of loss of control and

Table 1

Table 1. Correlation coefficients between frequency of binge watching and participant scores in three subcategories of the BWESQ

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in viewers. This specific point where binging is problematic was researched in a study that suggested there was a possible threshold at which binging goes from enjoyable to guiltinducing, with three to four episodes being the optimal binge (Feijter, Khan, & Gisbergen, 2016). Once viewers passed the threshold, guilt increased and the viewing experience was less enjoyable. The addictive potential and decreased satisfaction found in longer binges emphasize the importance of frequency and duration of binge watching. Binge watching video content has become the new norm in video consumption (Matrix, 2014), and while research has been conducted on its impact on daily activities and productivity, few studies have investigated the emotional outcomes of various frequencies of binging. This study aimed to explore how one’s frequency of binge watching is associated with their emotions toward it. It was hypothesized that there would be a positive association between frequency of binge watching and emotions. Specifically, we hypothesized that a higher frequency of binge watching would be associated with both an increase in loss of control and dependency and a decrease in positive emotions.


Original Research

C.

Tables 2-4. Regression analyses of frequency of binge watching and three subcategories of the BWESQ dependence and less positive emotions. The statistical analyses conducted supported part of our hypothesis, with positive associations between frequency and loss of control, frequency and dependency, and frequency and positive emotions. Loss of control had the largest association with frequency, followed by dependence and positive emotions. Although positive emotions also had a positive correlation with frequency, the association was smaller than the loss of control. This suggests that those with higher frequencies of binge watching can experience more negative emotions than positive ones. As a result of the higher association between frequency and negative emotions like loss of control and dependency, binging is more likely to have more negative impacts on viewers as their frequency increases. These findings are consistent with other conclusions in research concerning the negative effects of binge watching such as Mehra and Gujral’s binge watching study (2018). Loss of control and frequency presented with the strongest association, which could be related to the negative correlation between selfcontrol and use of instant media (Panek, 2014). The association between frequency and dependency supports the idea that binge watching can have addictive potential (Riddle et al., 2017). Also, while the association between frequency and positive emotions was not as hypothesized, it could support the suggestion from Gujral and Mehra (2018) that instant gratification offered by binge watching can sometimes overpower the viewer’s guilt over postponing more important tasks and result in an increase of positive emotion.

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The current study encountered a few limitations. First was the sample size and demographics. With only 66 participants, a larger sample would increase confidence in the results. The sample also consisted solely of college students and may not generalize to older samples. Lastly, race was not gathered in the demographics, and future research could include race to account for any racial differences that may be present. Another limitation of this research is that only linear regression was analyzed for the variables. A nonlinear analysis on positive emotions and frequency could reveal a nonlinear association to support the idea of a threshold for gratification as mentioned in the study done by Feijter and Gisbergen (2016). The survey methodology was a slight limitation in this study. Participants were asked to answer the questionnaire as accurately as possible based on the memory of their feelings concerning binge watching. Future studies could employ an observational or experimental approach to obtain information from participants immediately after they binge watch. This would provide more accurate responses because the participants would be reporting the emotions they are feeling in real time rather than recalling how they felt after previous binges. Additionally, our measure for frequency of binging (ranging from never to every day) did not consider the duration of the subjects’ binges. Further research could go beyond frequency to find effects on different durations and time spent binging. Finally, this study’s questionnaire had multiple subcategories for negative emotions, but only one for positive emotions. Future research might benefit from using another measure of positive emotions to provide more equal information on varieties of emotions. While this study was one of the first to use frequency as a predictor for the emotions often associated with binge watching, the results reflect those of past research, suggesting that higher frequency of binge watching has a predominantly negative emotional impact on viewers. Also, a major implication of past research on binge watching is its effect on sleep. Those who binge watch at a higher frequency tend to have more trouble sleeping, which suggests that not only can binging have negative emotional impacts but also negative physical impacts (Exelmans & Van den Bulck, 2017). Future research done on binge watching could investigate the addictive potential of binge watching in more detail. Also, because binge watching is such a new activity, current research is limited to analyzing short term effects. In the future, researchers will be able to observe long term effects that are not yet present in viewers.

Acknowledgements I would like to acknowledge Victoria Anozie for her substantial support and patience in completing this study and her willingness to think outside of the box.

References Exelmans, L. & Van den Bulck. (2017). Binge viewing, sleep, and the role of pre-sleep arousal. Journal of Clinical Sleep Medicine, 13(8), 1001-1008. doi:10.5664/jcsm.6704


Reports, 35(5), 381-391. doi:10.1080/08824096.2018.15253 46 Shaw, L. (2018). There are more new TV shows than ever in America. Retrieved from https://www.bloomberg.com/ news/articles/2018-01-05/netflix-propels-tv-productionsurge-dethrones-hbo-with-critics Walton-Pattinson, E., Dombrowski, S., & Presseau, J. (2018). ‘Just one more episode’: Frequency and theoretical correlates of television binge watching. Journal of Health and Psychology, 23(1), 17-24. doi:10.1177/1359105316643379

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Feijter, D., Khan, V., & Gisbergen, M. (2016). Confessions of a ‘guilty’ couch potato: Understanding and using context to optimize binge-watching behavior. Proceedings of the ACM International Conference of Interactive Experiences for TV and Online Video, 59-67. doi:10.1145/2932206.2932216 Jacobsen, W., & Forste, R. (2011). The wired generation: Academic and social outcomes of electronic media use among university students. Cyberpsychology, Behavior, and Social Networking, 14(5), 275-280. doi:10.1089/ cyber.2010.0135 Jenner, M. (2016). Is this TVIV? On Netflix, TVIII and binge watching. New Media and Society, 18(2), 257-273. doi:10.1177 Jenner, M. (2017). Binge-watching: Video-on-demand, quality TV and mainstreaming fandom.International Journal of Cultural Studies, 20(3), 304-320. doi. org/10.1177/1367877915606485 Matrix, S. (2014). The Netflix effect: Teens, binge watching, and on demand digital media trends. The Centre for Research in Young People’s Texts and Cultures, 6(1), 119-138. doi:10.1353/jeu.2014.0002 Mehra, A. & Gujral, A. (2018). Binge watching: A road to pleasure or pain? Indian Journal of School Health, 4(1), 2-13. Retrieved from http://expressionsindia.org/cbse_ programs/health_journal/2018/jan_apr18.pdf#page=10 National Telecommunications and Information Administration. (2018). Most popular online activities of adult internet users in the United States as of November 2017. Retrieved from https://www.statista.com/statistics/183910/internetactivities-of-us-users/ Netflix. (2013). Netflix declares binge watching is the new normal. Retrieved from Netflix.com: https://pr.netflix.com/ WebClient/getNewsSummary.do?newsId=496  Neubaum, G., Kramer, N., & Alt, K. (2019). Psychological effects of repeated exposure to elevating entertainment: An experiment over the period of 6 weeks. Psychology of Popular Media Culture, 8(1). doi:10.1037/ppm0000235 Panda, S. & Pandey, S. (2017). Binge watching and college students: Motivations and outcomes. Young Consumers, 18(4), 425-438. doi:10.1108.yc-07-2017-00707 Panek, E. (2014). Left to their own devices: College students’ ‘guilty pleasure’ media use and time management. Communication Research, 41(4), 561-577. doi:10.1177/0093650213499657 Reinecke, L. & Hofmann, W. (2016). Slacking off or winding down? An experience sampling study on the drivers and consequences of media use for recovery versus procrastination. Human Communication Research, 42(3), 441-461. doi:10.1111/hcre.12082 Riddle, K., Peebles, A., Davis, C., & Xu, F. (2018). The addictive potential of television binge watching: Comparing intentional and unintentional binges. Psychology of Popular Media Culture, 7(4), 589-604. doi:10.1037/ppm0000167 Roberts, P. (2014). Instant Gratification. The American Scholar. Retrieved from https://theamericanscholar.org/instantgratification/#.XJQLk6fMzu0 Rubenking, B. & Campanella, C. (2018). Binge-Watching: A suspenseful, emotional habit. Communication Research


Original Research

Effects of Polypropylene on Tetrahymena Cell Counts, Swim Speed, and Vacuole Formation Noah Mendoza, Madison Ambrose, Abel Thomas, Rithvik Bartham, Samantha Hodges, Tamarah Adair, Ph.D.

Abstract Nanoplastics, plastic fragments smaller than 1 μm, are prevalent throughout the environment. The effect of nanoplastics on terrestrial ecosystems is largely unknown. Tetrahymena pyriformis is a single-cell model organism frequently used for analyzing toxicity which also represents a major group of predators (ciliates) in the soil ecosystem. The goal of this study is to shed light on the impact of polypropylene (PP) nanoplastics on eukaryotic cells by examining the effects of polypropylene treatments on Tetrahymena pyriformis. To generate potential breakdown products from the polypropylene, treatment solutions were made by shredding and adding heat to polypropylene baling twine in proteose-peptone-tryptone (PPT) media. To test the null hypotheses that polypropylene nanoplastics do not impact reproduction, swim speed, and vacuole formation of Tetrahymena pyriformis, assays were conducted in both the control and the polypropylene treatment cultures after 48 hours. The results disprove the null hypotheses and indicated that polypropylene had a significant impact indicated by an increase in all three variables. These results indicate that nanoplastics do have an effect on single-cell organisms in culture. Further studies will focus on the effect of nanoplastics on terrestrial ecosystems. The soil ecosystem is the foundation for a healthy biotic environment and investing in research within this scope will help determine the system effects of plastics on the overall ecosystem.

Introduction Plastics have found their way into many aspects of our lives, and with an ever-growing population, the presence of plastics and more specifically, nanoplastics, in our lives will continue to grow. The increasing presence of nanoplastics has gained attention over recent decades and is currently gaining the attention of many media outlets, such as the National Geographic article We Depend On Plastic. Now, We’re Drowning in It (Parker, 2018). The term “nanoplastics” refers to particles of plastic that are smaller than 1 μm in diameter. Currently, humans are producing 245 million tons of plastics annually to keep up with the growing population, and this number will only continue to rise (Andrady, 2011). The increase in plastic usage requires an increase in the disposal and recycling of plastics currently consumed. This is problematic because, when not recycled, plastics may take decades or even centuries to fully decompose, leaving an abundance of nanoplastics in the environment. Without an effective way to decompose nanoplastics, this could eventually bring forth negative health effects on humans and organisms in the environment. Nanoplastics are often ingested by organisms in the environment and become embedded in their intestines, leading to digestive blockages and appetite suppression (Galloway & Thompson, 2013). Unfortunately, not all plastics are disposed of properly, resulting in plastics being incorporated into agriculture through mulch and fertilizers. Many fields, however, are being impacted

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by a type of baling twine that is often left behind to be decomposed, resulting in the introduction of nanoplastics directly into the soil and indirectly into agricultural goods. This polypropylene (PP)-based twine is often treated with various chemicals that enhance its ability to withstand weather and UV light. With this being said, limited research has been done regarding the impact of plastics on terrestrial ecosystems and organisms (Rillig, 2012). In order to understand the impact of nanoplastics within ecosystems, Tetrahymena pyriformis was exposed to polypropylene (PP) that was derived from baling twine.  In order to study its effects on terrestrial organisms, PP was administered to Tetrahymena pyriformis that was cultured within the lab. Tetrahymena pyriformis was specifically chosen due to its credibility as a model organism in laboratory studies and its presence within various terrestrial ecosystems.  The effects of PP on the Tetrahymena pyriformis were examined through asexual reproduction rate (cell counts), swim speed, and food vacuole formation. Tetrahymena pyriformis has been used in countless research studies due to its consistency and easily identifiable developmental traits (Cole et al., 2012; Sauvant et al., 1999). Tetrahymena pyriformis uses phagocytosis to engulf food, in a non-discriminatory manner. This characteristic is observed when feeding on laboratory substances, such as India ink,


General Experimental Design The following experiment was designed to test the effect of nanoplastics, specifically polypropylene from baling twine, on Tetrahymena pyriformis. Black baling twine, which has added chemicals to provide UV protection, longevity, and resistance to moisture, was used in the experiments. Preparation of Polypropylene  To prepare the polypropylene, baling twine was cut into small pieces. After shredding the baling twine into small pieces, 0.5 g of twine was combined with 50 mL of protease-peptonetryptone (PPT) media in a sterile glass jar. The contents of the glass were then placed into a microwave for approximately an hour to simulate environmental breakdown. The solution was kept overnight in a 55-degree Celsius water bath before filtering, so that the solution contained nanoplastics less than 5 µm in diameter. PPT media was added so that the concentration of filtered solution was 0.5 g/50 mL or 1%. The liquid was autoclaved for 15 minutes to eliminate any remaining bacteria and microbes in the solution.  Preparation of Tetrahymena pyriformis Culture After the solution was sterilized in the autoclave, 5 mL of a 6.1 x 104 cells/mL Tetrahymena pyriformis culture was added to either 45 mL of PPT media for the control solution or 45 mL of the filtered Polypropylene (PP) solution for the treatment solution, resulting in a 1:10 dilution ratio for both solutions. The Effect of Polypropylene Treatment on Tetrahymena pyriformis Cell Counts 5 mL samples of the treatment and control were aseptically transferred from the 50 mL flask, which was swirled before pipetting, to separate sterile test tubes. To examine cell counts following exposure of PP to Tetrahymena pyriformis, 2 µL of either the control or treatment culture of Tetrahymena pyriformis was combined with 1 mL of iodine. The iodine was used to stain the Tetrahymena pyriformis and pictures were captured of cells to provide an accurate count. The counts were performed in triplicate for both the control and treatment solutions to ensure accuracy. The Effect of Polypropylene Treatment on the Swim Speed of Tetrahymena pyriformis To conduct the swim speed assay, a 20 µL sample of either the control or treatment media was placed onto a flat slide. A millimeter marked ruler was placed under the slide to measure the distance traveled by Tetrahymena pyriformis. Only Tetrahymena pyriformis swimming in a straight line during the entire length of their swimming path was recorded. Swim speed

The Effect of Polypropylene Treatment on the Vacuole Formation of Tetrahymena pyriformis A 10 µL sample of either the treatment or control media were placed on concavity slides. A 2 µL sample of India ink, which helped to identify food vacuoles, was mixed into the media. A 2 µL sample of methylcellulose was added to slow down the speed of Tetrahymena pyriformis. A compound microscope was then used to observe the number of food vacuoles formed after 5 minutes and 15 minutes. This was repeated for 10 cells. Statistical Analysis Statistical analyses for the three assays (cell count, vacuole formation, and swim speed) were conducted using the “Data Analysis” ToolPak program in Excel. The data analysis was used to conduct descriptive statistics, which calculated the means and standard errors of each of the data sets. An F-test was conducted to compare the variances of the control and the treatment group and determine the type of t-test that should be conducted to examine the differences in the control and treatment means, a t-test was conducted to test the null hypothesis.

Results The following discussion reports the results from the reproduction, swim speed, and food vacuole formation assays, overall displaying the impact that the PP had on the Tetrahymena pyriformis by comparing the control group to the treatment group. Figure 1 compares the average cell count values for the control and treatment medias of Tetrahymena pyriformis. The control media showed a mean value of 20,274 cells per milliliter and a standard error of 1,351 cells per milliliter. The treatment

60000 Cell Count per mL

Materials and Methods

was expressed as millimeter (mm)/ seconds (s). This was repeated 10 times for both the control and the treatment cultures. While collecting this data, the samples were videotaped to ensure the correct data was recorded.

Average Cell Count of Control and Treatment Groups

50000 40000 30000 20000

10000 0

Control Media

Treatment Media

Sample Condition Figure 1: Polypropylene significantly increased the Tetrahymena pyriformis cell counts . Control: n = 171, Treatment: n = 174. (* = p < 0.01.) e presented as mean (standard deviation) or frequency (%).

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allowing researchers to measure consumption rates of various food sources (Aijaz & Koudelka, 2017; Kaminskaya et al., 2007). In this experiment nanoplastics found in baling twine will be used to test the hypothesis that nanoplastics affect the reproduction, swim speed, and vacuole formation of Tetrahymena pyriformis.


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Number of Food Vacuole Formed

Average Food Vacuole Formation: 5 Minutes 4 3.5 3 2.5 2 1.5 1 0.5 0 Control Media

Treatment Media

Sample Condition

Figure 2. Polypropylene significantly increased the Tetrahymena pyriformis swim speed. Control: n = 175, Treatment: n = 194. (* = p < 0.05)

Speed (mm/s)

Average Swim Speed of Control and Treatment 0.42 0.41 0.4 0.39 0.38 0.37 0.36 0.35 0.34 0.33 0.32

Treatment Media Sample Condition

Control Media

Figure 3. Polypropylene significantly increased the Tetrahymena pyriformis vacuole formation over 5 minutes. For control at 5 minutes: n = 156. For treatment at 5 minutes: n = 120. (* = p < 0.01) Average Food Vacuole Formation: 15 Minutes Number of Food Vacuole Formed

Original Research

showed a mean value of 44,704 cells per milliliter and a standard error of 3,212 cells per milliliter. An F-test was used to determine the presence of any significant difference in variance between the cell count of the control and treatment medias. The results indicated that the F-critical value was larger than the F-value, allowing for a t-test assuming equal variance to be conducted. The t-test concluded that there was a significant difference in cell counts between the control and treatment medias following the exposure to polypropylene (p = 1.648 x 10-11, p < 0.05). More specifically, the polypropylene treatment led to a significant increase in the cell counts of Tetrahymena pyriformis. Figure 2 demonstrates the effect polypropylene had on the swimming speed of Tetrahymena pyriformis. The control media swim speed assay showed that the cells traveled an average of 0.366 millimeters per second with a standard error of 0.012, calculated using the “Data Analysis” ToolPak. The treatment media swim speed assay showed that the cells traveled an average of 0.401 millimeters per second with a standard error of 0.013. A F-test was used to determine the presence of any significant difference in variance between the swimming speed of the control and treatment medias. The results indicated that  the F-critical value was larger than the F-value, allowing for a t-test assuming equal variance to be conducted. The t-test concluded that there was a significant difference in swim speed between the control and treatment medias (p = 0.038, p < 0.05). More specifically, the polypropylene treatment led to a significant increase in the swimming speed of Tetrahymena pyriformis. Figure 3 compares the average food vacuole formation of the control and treatment media of Tetrahymena pyriformis after 5 minutes. The average number of food vacuoles formed at 5 minutes for the control media was 2.323 with a standard error of 0.135. The average number of food vacuoles formed at 5 minutes for the treatment media was 3.516 with a standard error of 0.194. An F-test was used to determine the presence of any significant difference in variance between the swimming speed of the control and treatment medias. The results indicated that  the F-critical value was larger than the F-value, allowing for a t-test assuming equal variance to be conducted. The t-test concluded that there was a significant difference in food vacuole formation between the control and treatment medias (p = 7.025 x 10-7, p < 0.05). Specifically, there was a significant increase in food vacuole formation in the treatment media after 5 minutes. Figure 4 compares the average food vacuole formation of the control and treatment media of Tetrahymena pyriformis, with standard error after 15 minutes. The average number of food vacuoles formed at 15 minutes for the control media was 3.609 with a standard error of 0.195. The average number of food vacuoles formed at 15 minutes for the treatment media was 5.317 with a standard error of 0.246. An F-test was used to determine the presence of any significant difference in variance between the swimming speed of the control and treatment medias. The results indicated that the F-critical value was larger than the F-value, allowing for a t-test assuming equal variance to be conducted. The t-test concluded that there was a significant difference in food vacuole formation between the control and treatment medias (p =1.149 x 10-7, p < 0.05). Specifically, there was a significant increase in food vacuole formation in the treatment media after 15 minutes.

6 5 4 3 2 1 0

Control Media

Treatment Media

Sample Condition

Figure 4. Polypropylene significantly increased the Tetrahymena pyriformis vacuole formation in 15-minute interval. For control at 15 minutes: n= 161, and treatment for 15 minutes n=85. (* = p < 0.05.)


Discussion

References Aijaz, I., & Koudelka, G. B. (2017, March 3). Tetrahymena phagocytic vesicles as ecological micro-niches of phage transfer. Federation of European Microbiological Societies, 93(4), 1-8. Retrieved from https://searchproquest. com.ezproxy.baylor.edu/docview/1992002353?accountid=7014&pq-origsite=summon Andrady, A. L. (2011). Microplastics in the Marine Environment. Marine Pollution Bulletin, 62(8), 1596-1605. doi:https://doi.org/10.1016/j.marpolbul.2011.05.030 Cole, E., & Sugai, T. (2012, March 20). Developmental progression of Tetrahymena through the cell cycle and conjugation. Science Direct, 109, 177-236. Retrieved from https://www.sciencedirect.com/science/article/pii/ B9780123859679000074?via=ihub Galloway, T. S., Thompson, R. C., & Wright, S. L. (2013). The Physical Impacts of Microplastics on Marine Organisms: A Review. Environmental Pollution, 178, 483-492. doi:https://doi.org/10.1016/j.envpol.2013.02.031 Kaminskaya, A., Pushkareva, V., Moisenovich, M., Stepanova, T., Volkova, N., Romanova, J., Litvin, V., Gintsburg, A., &Ermolaeva, S. (2007, December). Stimulation of biofilm formation by insertion of Tetrahymena pyriformis wells within Burkholderia cenocepacia biofilms. Pleiades Publishing, 22, 186-194. Retrieved from https:// link-springer-com.ezproxy.baylor.edu/article/10.3103/ S0891416807040088 Lynn, D. H. (2009). Ciliates. Encyclopedia of Microbiology (Third Edition), 578-592. doi:https://doi.org/10.1016/ B978-012373944-5.00248-0 Parker, L. (2018, May 16). We Depend On Plastic. Now, We’re Drowning in It. Retrieved from https://www.nationalgeographic.com/magazine/2018/06/plastic-planet-waste-pollution-trash-crisis/ Rillig, M. C. (2012, May 31). Microplastic in Terrestrial Ecosystems and the Soil. Environmental Science and Technology, 46, 6453-6454. Retrieved from https://pubs.acs.org/doi/ pdfplus/10.1021/es302011r Sauvant, M. P., Pepin, D., & Piccinni, E. (1999, March 23). Tetrahymena pyriformis: A tool for toxicological studies. Pergamon, 38(7), 1631-1669. Retrieved from https://www.sciencedirect.com/science/article/pii/ S0045653598003816?via=ihub

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Original Research

This experiment examined the effect of polypropylene, a frequently used plastic that is found in baling twine, on Tetrahymena pyriformis, a soil microorganism. Two separate cultures, one containing a control media (PPT) and the other with polypropylene (PP), were used to test the effect of polypropylene on Tetrahymena pyriformis cell counts, swim speeds, and food vacuole formation. Overall, the results disprove the null hypothesis, that nanoplastics do not have an effect on Tetrahymena pyriformis cell counts, swim speed, and food vacuole formation. This study showed an increase in cell counts of the ciliate Tetrahymena pyriformis when the nanoplastic polypropylene was added into the media. The increase in cell counts could be due to Tetrahymena pyriformis consuming bacteria in the polypropylene mixture or through absorption of additional unknown nutrients. This increase in nutrients could have provided the energy for the increase in swim speed and reproduction for Tetrahymena pyriformis. Vacuole formation at 5 and 15 minutes was increased by the presence of PP, which could be an indicator that nanoplastics increase the feeding behavior of Tetrahymena pyriformis. But this could also indicate that the solution that we placed Tetrahymena pyriformis in was nutrient rich and allowed for more frequent feeding. This experiment could be repeated using purified nanoplastic which would eliminate the variable of additional nutrients that may have been present in the solution. A recent study performed by Aijaz & Koudelka indicates that Tetrahymena pyriformis’ use of phagocytosis when engulfing substances, like bacteria and other foreign material, allows them to have limited discrimination between the substances they consume (Aijaz & Koudelka, 2017). In return, this ability could help limit the impact that a particular substance may have on the ecosystem by using them to create a barrier between the foreign material and the environment (Aijaz & Koudelka, 2017). The results from our study indicate that one of the potential substances that Tetrahymena pyriformis could consume includes nanoplastics. Additionally, Tetrahymena pyriformis’ ability to withstand the strains of the bacteria within Aijaz and Koudelka’s study displays their high tolerance to unfavorable environments. Future field studies involving terrestrial ciliates could be used to determine if ciliate have the ability to remove nanoplastics from the environment.                   Future experiments could examine different sources of polypropylene that are common in items that humans use daily. This would make the findings of this study more applicable to humans, as they could directly see how their practices affect microorganisms and their broader impact on the ecosystem. Another direction that could be explored is the effect of nanoplastics on protein production, or also whether there are correlations present between cellular processes such as vacuole formation and increasing concentrations of polypropylene nanoplastics. Since our findings show an increase in ciliate population coinciding with added polypropylene in the ciliate environment, this could cause soil ecosystems to become unbalanced. Since some ciliates are hydrogenosomes and have the ability to produce methane, increases in these organisms in

the presence of nanoplastics could lead to an increase in greenhouse gases, such as methane (Lynn, 2009). Overall, the results of the experiment found that polypropylene significantly increased Tetrahymena pyriformis cell counts, swim speed, and vacuole formation. 


Endothelial Cell Diversity & Heterogeneity Arvind Muruganantham¹ ¹Department of Biology, Baylor University, Waco, TX

Review Article

Abstract Given the extensive functional, phenotypic, and genotypic diversity of the endothelium, endothelial cell biology remains a poorly investigated area of research in relation to its relative importance to human health. As the lining of the body’s vasculature, the endothelium heavily mediates the coagulation cascade by controlling the activation of platelets through vWF presentation and formation of the fibrin-platelet matrix through TF secretion. Apart from maintaining proper hemostasis, the endothelium is also largely responsible for mediating immune response via leukocyte trafficking. Endothelium response to local cytokine signaling prompts leukocyte extravasation through chemoattraction (via cytokines), rolling adhesion (via CAMs), tight adhesion (via integrins), and transmigration (via PECAM). Furthermore, tumor angiogenesis, a process highly investigated due to its importance to tumor cell proliferation and metastasis,is also endothelium-mediated. By hijacking proangiogenic pathways, such as VEGF and cytokine signaling, cancer cells promote sprouting angiogenesis to the site of the tumor to obtain a steady supply of nutrients and growth factors. In order to carry out the endothelium’s wide-ranging functions, vascular endothelial cell phenotypes are influenced by a cocktail of developmental influences arising as early as amniotic development and environmental cues from the surrounding tissue. Adding another layer of heterogeneity, recent studies have shown phenotypically equivalent endothelial cells engaging in noise-mediated stochastic phenotype switching to create mosaic heterogeneity in endothelial cells arising from the same vascular bed. Further understanding the diverse and complex functions of endothelial cells, at both the cellular and population level, will provide insight into drug development for a myriad of diseases.

Introduction The endothelium, the single layer of cells that lines the cavities of the body and its vasculature, is involved in many critical biological processes and disease pathologies. Endothelial cells are products of mesoderm-derived hemangioblasts that form cell clusters, known as blood islands, during amniotic development. The outer cells of the blood islands further flatten to differentiate into vascular endothelia while the core of the blood cells become hematopoietic cells (Dyer & Patterson, 2010). As the vascular endothelium progresses in the developmental process, it gains exposure to signals that results in the cell type’s widespread diversity and heterogeneity via environmental cues. Through a combination of paracrine signaling and cellular sensors, cells modulate their gene expression patterns to match that of their respective environments (Zhang & Friedman, 2013). Given the endothelium’s multifaceted roles throughout the body, the ability of endothelial cells to become highly specialized for their environment is crucial to maintaining proper vascular physiology.

Endothelium Mediated Coagulation Serving as the lining of the body’s vasculature, naturally, the endothelium is heavily involved in many of the hemostatic

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pathways triggered by vascular injury. Typically, coagulation is thought to occur in three distinct phases (Versteeg, Heemskerk, Levi, & Reitsma, 2013). First, sub-endothelial collagen is exposed at the site of injury and mediates the initial round of platelet adhesion to the collagen surface via von Willebrand factor (vWF). Activated endothelial cells then secrete tissue factor (TF) to combine with activated circulating coagulation factor VII (fVIIa) to form the TF/fVIIa complex, which in turn increases thrombin levels to form activated platelet aggregates. Finally, thrombin proteolytically cleaves various coagulation factors and fibronectin to interlock fibrin chains into a cohesive fibrin matrix, thus creating an effective barricade to protect against infection and rapid blood loss (Figure 1). Endothelial cells serve as master regulators of the coagulation cascade through a two-fold mechanism: presentation of vWF on the luminal surface of vasculature to prompt activated platelet adhesion to the collagen surface and TF secretion to create a cross-linked fibrin matrix upon which activated platelets can form aggregates (Yau, Teoh, & Verma, 2015). After the injury has been healed, endothelial cells also contribute to the breakdown of the dense platelet-fibrin matrix through the release of fibrinolytic enzyme activators, such as tissue plasminogen activator (t-PA)(Rajendran et al., 2013). In unison with other serine proteases, t-PA catalyzes the


Endothelium Mediated Leukocyte Trafficking

conversion of inactive plasminogen to its clot degrading isoform, plasmin (Tsurupa & Medved, 2001). Endothelial cell mediated t-PA release from internal Weibel-Palade bodies is essential to restoring tissue health and maintaining hemostasis (Hubel et al., 2002). Dense platelet aggregates bound to endothelial cell presented vWF are unlinked through the metalloprotease, ADAMTS13 (Turner, Nolasco, Tao, Dong, & Moake, 2006). In addition, constitutive ADAMTS13 secretion by endothelial cells remains an important source of circulating plasma ADAMTS13 during the post-injury repair response.

Role of the Endothelium in Cancer Perhaps one of the most important and therapeutically

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Review Article

Figure 1. Summary of the endothelium-mediated coagulation cascade. Endothelium-secreted tissue factor (TF) and circulating activated coagulation factor VII (fVIIa) form a complex to upregulate thrombin expression.

However, the function of the endothelium stretches far beyond simply serving as a lining for blood vessels and as a central mediator of the coagulation cascade. The endothelium’s role in inflammation has been a heavily investigated area of research in the past decade. Moving away from the notion that the endothelium serves only a menial role in the immune response, recent studies have shown that the endothelium plays a crucial role in regulating leukocyte transmigration and homing (Muller, 2013). Using the vasculature as a ‘highway’ for transport to sites of interest, circulating leukocytes are able to swiftly counter pathogenic threats present in the plasma. However, luminal membrane protein presentation by endothelial cells is responsible for the movement of leukocyte infiltration beyond the endothelial basement membrane to mitigate pathogenic threats past the endothelium (Sans et al., 1999). Immune response-mediated cellular adhesion molecule (CAM) presentation is controlled by paracrine cytokine signaling (such as interleukin-1 and tumor necrosis factoralpha) from the site of inflammation or infection. In response to cytokine signaling, endothelial cells increase the presentation of leukocyte ligand CAMs, such as P-selectin, to prompt rolling adhesion (Jung, Norman, Scharffetter-Kochanek, Beaudet, & Ley, 1998). Rolling adhesion is characterized by the slowing down of leukocytes on the endothelium as attractions between endothelium presented CAMs and leukocyte ligands are rapidly formed and broken. Once rolling leukocytes have decreased their movement considerably, tight adhesion takes place. During this stage, chemokines secreted by activated macrophages cause leukocyte surface integrins to switch from their rested low-affinity conformation to their activated highaffinity conformation (Sun et al., 2014). High-affinity integrinreceptor interactions lock the leukocyte in a tight adhesion, rendering the leukocyte immobile. Once tight adhesion has been achieved, leukocytes extend their pseudopodia and recruit platelet endothelial cell adhesion molecule (PECAM) to pull the leukocyte through gaps between endothelial cells (Vaporciyan, 1993). When it encounters the endothelial basement membrane, a combination of mechanical force and proteolysis is used to force the leukocyte deeper into the surrounding tissue. Since the recruitment of leukocytes past the endothelium is heavily mediated by presentation of leukocyte ligands on the luminal endothelial surface, various endothelial disorders can impact leukocyte movement throughout the body. For instance, in diabetic kidney disease, where high blood glucose levels diminish endothelial nitric oxide synthase (eNOS) levels, blood vessels are vasoconstricted in response to reduced nitric oxide production (Garcia-Garcia, 2014). As a result, in the glomeruli of the kidney, miniscule blood vessels are destroyed through sustained damage to the glomerular basement membrane via mechanical force induced by leukocyte infiltration. Thus, this greatly hinders the kidney’s ability to effectively filter blood.


Review Article

relevant functions the endothelium commands is angiogenesis. Angiogenesis is the process by which cells signal the endothelium in response to changing tissue requirements to sprout new blood vessels (Walsh, 1997). This process is relatively common in the body, taking place during development and during periods of rapid tissue growth. However, during cancer, tumor cells hijack the angiogenic pathway to siphon nutrients and blood via the vasculature (Jahroudi & Greenberger, 1995). Once the tumor has grown larger than a few millimeters in diameter, it begins to experience hypoxia and nutrient deprivation, as the existing blood supply is insufficient to satisfy the rapidly proliferating tumorâ&#x20AC;&#x2122;s needs. Thus, the tumor begins to release proangiogenic signals to the surrounding tissue in an effort to recruit vasculature to the area (Hida, Maishi, Annan, & Hida, 2018). Along with vascular endothelial growth factor (VEGF), the key growth factor responsible to recruiting endothelial sprouting and growth, tumor cells also secrete other cytokines and guidance factors to further remodel the surrounding vasculature such that the tumor is adequately nourished (Figure 2).

Figure 2. Tumor cells directly adjacent to vasculature are adequately nourished while the core of the tumor is hypoxic and nutrient-deficient. The tumor releases proangiogenic growth factors (such as VEGF, PDGF, FGF) to increase nutrient availability. VEGF, vascular endothelial growth factor; PDGF, platelet derived growth factor; FGF, fibroblast growth factor. However, despite the elaborate signaling efforts of the tumor, tumor cell-recruited vasculature is often disorganized and prone to hemorrhaging due to the internal competition among tumor cells to obtain nutrients (Weis & Cheresh, 2011). As a result of this, tumor cell-recruited vasculature is organized only according to the intensity of proangiogenic signal released by cells that lie in certain areas of the tumor, partially explaining why cells at the core of tumors are hypoxic in nature (Muz, Puente, Azab & Azab, 2015). In response to this leaky new vasculature, the immune system prompts its wound healing response and deploys immune cells to the area (Weis & Cheresh, 2011). The accompanying inflammatory response further contributes to tumor angiogenesis via immune cell secreted

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cytokines. In unison with VEGF and chemokine signaling, tumor cells also release proteolytic enzymes that cleave segments of the extracellular matrix (ECM), exposing proangiogenic integrins and allowing cellular mobility for metastasis. After loss of cellular adhesion (primarily through VE-cadherin loss, ECM cleavage, and connexin loss), cancer cells that are able to infiltrate the blood supply often migrate to metastatic sites in a non-random fashion (Ben-Baruch, 2009). For example, breast cancers tend to metastasize to the bone and brain while colon cancers tend to metastasize to the lung and liver. In order to survive and thrive at the metastatic site, the cancer cell must be able to adapt to the difference in microenvironment between the site of origin and the metastatic site. Thus, while cancer cells are often able reach diverse areas of the body, only a small subset of those extravasions result in successful growth of the cancer cell colony due to microenvironment compatibilities. Conventional forms of chemotherapy routinely used in clinical settings tend to lack specificity against cancer cells. As a result, cancer treatment is extremely demanding and greatly diminishespatient quality of life (Bhugwandass, Pijnenborg, Pijlman, & Ezendam, 2016). However, modern cancer therapies are attempting to inhibit tumor angiogenesis instead of directly targeting cancer cells that can easily be evaded by the host immune system and gain drug resistance. This approach seems particularly attractive since it serves as a method of conferring specificity on cancer treatment that has never been accomplished before (Weis & Cheresh, 2011). Since intense angiogenesis observed at the site of tumors is not highly typical in the body, targeting neovascularization in the body may be more effective at destroying tumors than conventional chemotherapy. However, there are limitations to this approach. Targeting proangiogenic pathways, such as through VEGF inhibition, is highly feasible, although consequences sustained through inhibition of non-canonical pathways must also be considered. In addition, cancer cells are often able to adapt and compensate for subtle changes in their microenvironment; for instance, an anti-VEGF therapy might prompt the tumor to upregulate chemokines and proangiogenic integrins to maintain a net proangiogenic status (Jin & Mu, 2015). Thus, targeting the endothelial cells responsible for angiogenesis may be a highly effective strategy for starving tumors of their nutrient supply, although conferring specificity exclusively to tumor-induced vasculature proves to be challenging.

Phenotypic Diversity of the Endothelium Given the endothelium spans all areas of the human body, it must be able to cater to a wide variety of demands dependent on the tissue surrounding the vasculature. Morphological changes induced by the cellular microenvironment prompts a compensatory response from individual cells of the endothelium (Sumpio, 1991). To this extent, each cell reacts to its microenvironment in a unique way, largely independent to the influences of neighboring cells. However, since neighboring cells are exposed to roughly the same environmental conditions, inevitably they tend to exhibit similar gene expression profiles. Exemplary of the highly dynamic state of endothelial cells, individual segments of the vascular endothelium are able to


Figure 3. The three types of endothelia. The pink layer below the endothelial cells is the basement membrane. Continuous endothelium is the most common type found in nearly all vasculature. As its name suggests, it is characterized by a continuous array of endothelial cells held together firmly

by tight junctions. The continuous endothelium scarcely participates in adsorption and secretion due to its lowcell permeability. Water, ions, and other small molecules travel past the continuous endothelium through tiny gaps in the tight junctions between endothelial cells. The blood-brain barrier, a complex system of miniscule filtering capillaries that allows selective permeability for neuronal tissue, contains continuous capillaries supported by astrocytes and a thick basement membrane (Abbott, 2013). As a result, very few substances are able to cross the blood-brain barrier. Thus, the extreme selectivity of the blood-brain barrier poses numerous therapeutic challenges, as low blood-brain barrier permeability drastically limits the repertoire of drugs that can be used for neuronal diseases. Contrastingly, in areas where extremely high permeability is necessary, such as the kidney and small intestine, fenestrated endothelia exist (Betts, 2013). Fenestrated endothelia feature porous endothelial cells in addition to the gaps between tight junctions found in continuous endothelia. These 60-80 nm diameter fenestrations serve to increase the degree of permeability from the vasculature to the surrounding tissue by creating a route for molecular transport that does not involve intercellular travel. To aid in size-based sorting, fenestrations are supplemented by a network of radially aligned fibrils that allow small molecules and moderate amounts of protein to diffuse through the endothelial lining (Pavelka & Roth, 2010). In renal glomeruli though, specialized cells called podocytes extend foot processes through the endothelial fenestrations to confer solute specificity in the absence of fibrils (Pavenstadt, 2000). The negatively charged basement membrane is also able to selectively discourage the diffusion of negatively charged solutes such as albumin (Betts, 2013). Moreover, fenestrated endothelia are able to modulate their degree of permeability given their respective microenvironment by controlling the number of fenestrations present. The third type of endothelium, and the least common, is the sinusoid endothelium. Sinusoid endothelia are characterized by flattened rows of endothelial cells with large intercellular gaps. These large intercellular gaps are present in the basement membrane as well, allowing for the easy passage of large proteins or even cells. Compared to the other types of endothelia, sinusoid endothelia have the slowest blood flow, allowing for copious exchange of nutrients, waste, and other molecules. Sinusoid endothelia are extremely useful in organs that secrete very large biomolecules, such as the liver and spleen. In the bone marrow, sinusoid endothelia are crucial since new blood cells can only enter the blood supply through the massive intercellular gaps characteristic of sinusoid endothelia.

Developmental Origins of the Endothelium & Morphogen-induced Vascular Specialization Given the huge amount of diversity among endothelial cells, it is important to understand how these differences arise. Following mature cell differentiation and vascular remodeling via Indian hedgehog (IHH), vascular specialization, that is the differentiation into arterial or venous endothelial cells, is mediated almost solely by a (VEGF) gradient present in the

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Review Article

gauge shear stress and prompt the activation of transcription factors (such as Kruppel-like factor-2, KLF-2) to express proteins to ameliorate shear stress (Hierck, 2008). For instance, regions of heavy laminar stress, such as arterial vasculature where blood pressure is high, contain endothelial cells that heavily express VE-cadherin, a junctional endothelial-specific adhesion molecule that packs endothelial cells tightly and elongates cells to reduce shear stress (Kondapalli, Flozak, & Albuquerque, 2003). In contrast, regions of less strenuous shear stress, such as venous vasculature where blood pressure is lower, contain endothelial cells that heavily express P-selectin and vWF. Although dynamic gene expression is by no means exclusive to endothelial cells, it is unique in its magnitude of plasticity; in making gene expression highly plastic, endothelial cells are able to display a wide range of phenotypic diversity specifically suited for their respective cellular microenvironments (Dejana, Hirschi, & Simmons, 2017). For instance, the degree of compactness between endothelial cells in capillaries has three levels (Betts, 2013) (Figure 3).


Review Article

mesoderm (Dyer & Patterson, 2010). However, it is important to note that although the presence of VEGF and its receptor, VEGFR2, is correlated with arterial cell fate, VEGF signaling may increase endothelial cell survival and propagation but not directly influence differentiation (Atkins, Jain, & Hamik, 2011). Although VEGF-VEGFR1 binding has weaker kinase activity than VEGF-VEGFR2 binding, VEGF-VEGFR1 binding is crucial to maintaining proper vascular morphology by acting as a VEGF sink, mitigating excessive proliferation caused by VEGF-VEGFR2 binding. Acting upstream of VEGF, Sonic hedgehog (SHH) binds to the Smoothened receptor to further induce arterial cell fate. In addition to VEGF activity, vascular specialization is also mediated by ephrinB2 and its receptor tyrosine kinase, EphB4, during vascular plexus remodeling. Interestingly, ephrinB2 secretion is largely exclusive to the arterial endothelium while EphB4 secretion is largely exclusive to the venous endothelium. Thus, this complementary relationship suggests a genetic influence on vascular specialization prior to systemic blood flow. Furthermore, VEGF binding to neuropilin-1 (Nrp-1) previous to systemic blood flow has been shown to trigger Notch signaling, promoting ephrinB2 and subsequently the VEGF-induced arterial phenotype (Dyer & Patterson, 2010). Venous cell differentiation is mediated by suppression of the Notch signaling pathway and arterial identity via chicken ovalbumin upstream-promotor-transcription factor II (COUP-TFII). Ablation of COUP-TFII reverts venous endothelial cells to the arterial phenotype and restores Notch signaling, suggesting COUP-TFII suppresses the default arterial phenotype under venous differentiation (Atkins, Jain, & Hamik, 2011). From arterial and venous specialization, venous endothelial cells undergo further differentiation to create lymphatic endothelial cells. The lymphatic system serves as the migratory channel by which immune cells carry out their duties and surveil the body. Thus, lymphatic endothelial cells require a distinctive set of differentiation factors separate from those of the arterial and venous endothelia. During development, a subset of cardinal vein cells begins expressing prospero homeobox transcription factor 1 (Prox1) (Atkins, Jain, & Hamik, 2011). Eventually, as the subset of Prox1-positive cells grows in number, a balloon cluster known as the lymph sac arises. The lymph sac then thins and branches to form lymphatic vessels. Formation of Prox1-positive clusters solely at the anterioposterior axis of the cardinal vein suggests that Prox1 expression is closely regulated in venous endothelial cells. SRY-family transcription factor Sox18 and COUP-TFII co-expression has been shown to be necessary and sufficient for Prox1 induction and the subsequent lymphatic endothelial cell phenotype (Atkins, Jain, & Hamik, 2011).

Genotypic Diversity & Mosaic Heterogeneity of the Endothelium In addition to the astonishing diversity of endothelial cell phenotypes, endothelial cells of the same phenotype also share genotypic differences. For instance, when vWF expression for heart, lung, liver and kidney endothelia was measured through immunofluorescence, vWF expression varied drastically across

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various organs despite being of the same endothelial type (i.e. continuous, fenestrated, sinusoid) (Marcu et al., 2018). Furthermore, when whole tissue from these organs were stained against VE-cadherin and CD45 and subject to flow cytometry, distinct expression profile clusters were identified among organ-specific tissue; this gene expression heterogeneity was then confirmed once again through RNAseq analysis, revealing unique gene expression profiles for organ-specific endothelial cells. It is no surprise that organ-specific endothelial cells have distinct gene expression profiles warranted that the endothelium fulfills different functions given the organin which it resides. Gene expression for any cell type, including endothelial cells, lies on a spectrum upon which the most typical ‘pool’ of biologically stable cells is designated as a cell type. However, although they are not given distinction as individual cell types, smaller biologically stable ‘pools’ of cells exist among cell types that can be accessed by noise-mediated changes in DNA methylation (Yuan et al., 2016). Furthermore, using Cre/loxP cell fate mapping, these stochastic switches in gene expression were revealed to be more common in organ tissues requiring more dynamic gene expression (such as the lung and heart) while these switches were shown to be less common in organ tissues requiring static gene expression (such as the liver and kidney) mediated via biologically unstable intermediary states the cell must cross to revert to an alternative gene expression profile. Taken together, these studies suggest a wide range of genotypic diversity among organ-specific endothelial cells as well as endothelial cells arising from the same vascular bed.

Conclusion Given the multifaceted role of the endothelium in various disease pathologies, understanding endothelial heterogeneity will help confer high levels of specificity when designing therapeutics. Since endothelial cells exhibit heterogeneity at the cellular microenvironment level, they hold promise for high specificity early detection and effective treatment of disease. Further research into mosaic heterogeneity of endothelial cells subject to identical microenvironments will provide great insights into the role of stochastic phenotype switching in disease predispositions such as variable metastatic potential among cells of a tumor. In addition, investigating whether these mosaic gene expression profiles fulfill a higher homeostatic role in their respective global populations will be an interesting line of inquiry. Considering the crucial role of the endothelium in maintaining hemostasis and overall body homeostasis, further investigation into its varied functions across tissue types will provide more insight into how the endothelium works in unison with the kidney to regulate circulating solute levels in the blood. This opens doors for new therapeutic development in vascular disorders, such as hypertension, where new standards of care aside from Losartan are crucially needed. By further elucidating all the molecular effectors responsible for the coagulation response, researchers can engineer better hemostatic agents for use in the clinic and the field. Understanding the dynamism of the endothelium will reveal fundamental new ways to combat a plethora of diseases—from atherosclerosis to cancer.


Acknowledgements I’d like to thank my former Principal Investigator, Dr. Vivek Bhalla MD, FASN, FAHA, and the Bhalla Lab at the Stanford University School of Medicine, where my previous research on diabetic kidney disease and Esm-1 inspired my interest in endothelial cell and vascular biology.

References

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Brain Regions Involved in Hypnosis: Clinical Implications Sarah Hale, Gary R. Elkins, Ph.D.

Abstract

Introduction Hypnosis and hypnotherapy have a history predating modern psychology and psychotherapy. Due to many efforts of researchers and practitioners “during the latter half of the 20th century and leading up to the present time, there has been an increasing amount of empirical testing the effectiveness of hypnotic interventions” (Elkins, 2014). Because of this, hypnosis has been shown to have applications in both the medical and psychotherapy fields. In the medical field, hypnosis has been used to treat various physical conditions, including chronic pain (Artimon, 2015; Mazzola et al., 2017), irritable bowel syndrome (Palsson, 2015), and management of hot flashes (Elkins, Fisher, Johnson, Carpenter, & Keith, 2013; Sliwinski & Elkins, 2017). Hypnosis has also been used to improve palliative care for chronic illness (Brugnoli et al., 2018). In conjunction with psychotherapy, hypnosis has been used to treat psychological disorders, such as Post-Traumatic Stress Disorder (PTSD) (Lynn, Malakataris, Condon, Maxwell, & Cleere, 2012), depression (Alladin & Alibhai, 2007; Kirsch & Low, 2013), and nicotine addictions (Green & Lynn, 2017; Pekala, 2017). According to Landry & Raz (2017), “the hypnotic response is located at the confluence of three central factors: interindividual variability in hypnotizability, the induction procedure, and the content of hypnotic suggestions” (See Figure 1). This paper discusses the brain regions involved in each of the three central factors during hypnotic response. In most of the reviewed articles addressing hypnotizability and/or induction, induction and hypnotizability were primarily studied together and their contributions to the hypnotic response were found to be correlated (Cardeña, Jönsson, Terhune, & MarcussonClavertz, 2013; Deeley et al., 2012; Hoeft et al., 2012; Jiang, White, Greicius, Waelde, & Spiegel, 2017; Lipari et al., 2012; William J. McGeown, Mazzoni, Venneri, & Kirsch, 2009; Picerni et al., 2019). This observation illustrates that the more

hypnotizable someone is, it is probable that they would have a higher response to induction. Multiple sources found patterns of modulation in three principle neural networks when investigating hypnotizability and induction. These attentional neural networks include the Executive Control Network (ECN), the Salience Network (SN), and the Default Mode Network (DMN). The ECN is a neural network in the brain that is associated with the control of intentionality. This system is also referred to as the Central

Induction

Suggestion Hypnotic Responses

Hypnotizability

Figure 1. Note. Adapted from “Neurophysiology of Hypnosis”, by Landry, M., & Raz, A., 2017, In G. R. Elkins (Ed.), Handbook of medical and psychological hypnosis: Foundations, applications, and professional issues, p.20

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Hypnosis is described as a “state of consciousness involving focused attention and reduced peripheral awareness characterized by an enhanced capacity for response to suggestion” (Elkins, Barabasz, Council, & Spiegel, 2015). The clinical uses of hypnosis include medical and psychological applications. However, it is not yet known whether the “state of consciousness” is an alteration of waking consciousness, similar to other states (i.e. meditation, mindfulness, yoga), or unique to hypnosis. This paper reviews the relevant literature on hypnosis to identify the brain regions that research has suggested may be most likely associated with hypnosis. Studies utilizing electroencephalogram (EEG), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) scanning were reviewed. Because of the results presented in the studies examined in this paper, I hypothesize that hypnosis can affect certain regions of the brain. Furthermore, targeting and altering activity in those brain regions could enable benefits to be achieved more quickly through hypnotherapy and similar mind-body practices.


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Executive Network in some sources. The main brain region associated with the ECN is the dorsolateral prefrontal cortex (DLPFC) (Deeley et al., 2012; Hoeft et al., 2012; Jiang et al., 2017; Lipari et al., 2012; William Jonathan McGeown, Mazzoni, Vannucci, & Venneri, 2015; Terhune, Cardeña, & Lindgren, 2011). The SN, composed of the dorsal anterior cingulate cortex (dACC), anterior insula, amygdala, and ventral striatum, is “involved in detecting, integrating, and filtering relevant somatic, autonomic, and emotional information using independent component analysis” (Hoeft et al., 2012). The DMN, the most recently discovered of the three networks, is described as a “network of brain regions more active during low-demand compared to high-demand task conditions and has been linked to processes such as task-independent thinking, episodic memory, semantic processing, and selfawareness” (Deeley et al., 2012). Brain regions associated with the DMN include the “posterior cingulate cortex (PCC) and other midline brain structures including the medial prefrontal cortex (mPFC)” (Jiang et al., 2017). While these networks do work independently, the SN and DMN also are connected to the ECN and have been shown to work with the ECN under some circumstances. The third factor contributing to the hypnotic response is suggestion, which can produce one of three categories of effects: perceptive, cognitive, or motor (Halligan & Oakley, 2014). Perceptive suggestions are intended to alter visual or sensory experiences. Cognitive suggestions are intended to alter performance of relevant tasks. Motor suggestions are intended to “alter preparation, execution, and monitoring of actions” (Cojan et al., 2009; Landry & Raz, 2017). In this paper, I review relevant literature on these three types of suggestions. By investigating the brain regions or networks implicated in each of the individual factors contributing to the hypnotic response, one can achieve a more thorough understanding of the neural processes occurring during hypnosis. Considering the various psychological and physical applications of hypnosis that have been identified, a more thorough understanding of the neural underpinnings behind the response could lead to a refinement of the approach to inducing a hypnotic response as well as an expansion of potential ameliorating applications of hypnosis as a clinical tool.

Method In order to find relevant sources for this review, I used the following search engines: PsychINFO, Pubmed, and Google Scholar. Keywords for searches included “hypnosis”, “brain imaging”, “neuroimaging”, and “brain correlates”. Studies that utilized different brain imaging techniques, such as EEG, MRI, fMRI, and PET were included. Since a variety of imaging techniques allows for many possible types of visualizations of the nervous system, this renders more information. Most studies analyzed healthy subjects and level of hypnotizability was reported to be low hypnotizability, medium hypnotizability, or high hypnotizability. Some studies included subjects from only one hypnotizability level, while others include subjects from all levels. Studies with subjects that had any history of psychological disorders were excluded. Seventeen studies will

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be reviewed in this paper.

Results Hypnotizability and Induction Three neural networks, the ECN, SN, and DMN, commonly showed increased activity in research involving the neural correlates associated with hypnotizability and induction. The most common finding among studies discussing induction was a reduction in DMN activity, especially in individuals who are of high hypnotic ability (Deeley et al., 2012; Lipari et al., 2012; William J. McGeown et al., 2009). To explore modulation of the DMN during induction, Deeley et al. (2012) used fMRI to observe medium hypnotizable individuals (MHIs) and highly hypnotizable individuals (HHIs). They found that there was a negative correlation between self-reported hypnotic depth, and therefore absorption, and activity within the DMN. They also found increased activity in other prefrontal regions of the brain involved in attention. Lipari, et al. (2012) found similar results when investigating “pure hypnosis” (a state in hypnosis in which there are no further suggestions after induction) with a “hypnotic virtuoso”. This subject scored the highest possible score (12) on the Stanford Hypnotic Susceptibility Scale (Weitzenhoffer, Hilgard, & Kihlstrom, 1962). Using fMRI and EEG, this study found a significant decrease in activity within the DMN, as well as “peculiar activations of non-DMN areas and hemispheric Kihlstrom, 1962). Using fMRI and EEG, this study found a significant decrease in activity within the DMN, as well as “peculiar activations of non-DMN areas and hemispheric asymmetries of frontal lobe connectivity” (Lipari et al., 2012). Another study by McGeown et al. (2009) utilized fMRI to analyze differences in neural correlates among LHIs and HHIs during visual tasks both in and out of hypnosis with the goal of understanding induction. While McGeown et al. (2009) also found that there was a significant decrease in anterior DMN activity among HHIs, their study suggests that there was no other increase in any other cortical regions, which was not observed in the two previously mentioned studies. The decrease in DMN activity in HHIs suggests that HHIs are experiencing a higher degree of attention to the administered suggestions. HHIs are likely integrating the suggestions, resulting in an increased behavioral response behaviorally to the suggestions. Although various findings showed different brains regions also involved in hypnotizability, it appears that that reduced activity in DMN are common across all studies. Another interesting finding involving the attentional neural networks is a modulation of connectivity between the ECN, SN and DMN (Jiang et al., 2017). In this study, the left and right dorsolateral prefrontal cortex (ECN), dorsal anterior cingulate cortex (dACC; SN), and posterior cingulate cortex (PCC; DMN), were observed using fMRI in order to study connectivity between the three networks during hypnosis. Among HHIs, there was a “decoupling” or decreased level of connectivity between the DLPFC and posterior cingulate cortex (PCC), a region of the DMN, explaining a deeper level of absorption and possibility of “hypnotic loss of self-consciousness and amnesia”. During induction, there was not only decreased activity in the dorso-anterior cingulate cortex (dACC), a region of the SN, but


connectivity to the DLPFC, the executive attentional center of the prefrontal cortex responsible for the allocation of attentional resources. With higher connections between these two regions, HHIs more readily identify suggestions as relevant and important stimuli to attend to, leading to an increased focus and attention to the suggestion. This ultimately leads to an increase in individuals’ susceptibility to hypnotic suggestions. Terhune, Cardeña, & Lindgren (2011) did not look for any of the aforementioned networks; instead they used EEG and self-reports to compare frontal-parietal phase synchrony in LHIs and HHIs. They found that HHIs showed “lower frontalparietal alpha 2 synchronization during hypnosis” than LHIs (Terhune et al., 2011). Therefore, they concluded that HHIs have a frontal-parietal network that is more easily modulated, hence allowing them to be more hypnotizable. The researchers also acknowledge, however, that an “alternative interpretation” of the results is a reflection of “group differences in the DMN”, including the medial prefrontal and lateral parietal cortices (Terhune et al., 2011). In addition to the attentional neural networks, other studies have found additional brain regions to be involved in the hypnotic response. Cardeña, Jönsson, Terhune, & MarcussonClavertz (2013) utilized EEG in order to observe neutral hypnosis among 37 individuals with varied hypnotizability. They found that global functional connectivity was decreased following hypnotic induction. Subjective reported hypnotic depth was related to global connectivity, suggesting that reduced functional connectivity could be associated with higher degrees of hypnotizability. (Cardeña et al., 2013) Another study by Picerni et al. (2019) investigated “the association between cerebellar macro- or micro-structural variations…and hypnotic susceptibility”. They found that HHIs exhibited lower levels of gray matter volumes in various brain regions such as the right inferior temporal gyrus, insula, superior orbitofrontal cortex, etc. This suggests that increased hypnotizability is associated with lower levels of cell body presence (Picerni et al., 2019). Contrary to the association between higher levels of

Table 1. Summary of the Studies Focusing on Induction and Hypnotizability

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also a higher level of connectivity between (the insula—another region of the SN) and the DLPFC (ECN). The authors suggest these actions correlate with “reduced context comparison” and “decreased attention to external environment”. Considering that the dACC has been implicated in playing a role in finding solutions to detected conflicts, it is particularly interesting that the DLPFC, the central executive area responsible for allocating attentional resources, exhibits higher levels of connectivity with this region in HHIs. This suggests that HHIs more readily detect suggestions as a conflict of interest, leading to increased allocation of attentional resources to the verbal suggestion. This, thereby, could result in an increased susceptibility to the suggestion (Jiang et al., 2017). When comparing HHIs and LHIs, Jiang et al. (2017) found that HHIs had increased connectivity between the DLPFC and insula. The insula plays a role in self-awareness (being aware of oneself) and perception of the self (how one sees oneself). Considering that the insula is additionally implicated in the salient network which has connections to the default mode network, the insula may be involved in the allocation of attentional resources that are relevant to self-perception. It is noted that HHIs showed increased connectivity between the DLPFC and the insula. This is consistent with the function of the insula and the potential role of functional connections between the SN and the DMN. In regard to induction, the insula appears to be involved in the isolation of the self from the external environment, resulting in possibly increased attention allocated to the verbal suggestions being administered. Another study (Hoeft et al., 2012) comparing neural correlates of hypnotizability found similar results, showing HHIs having greater connectivity between the left DLPFC and SN. Subjects including twelve LHIs and HHIs were observed under fMRI and T1 MRI scans in order to compare the neural phenomena under hypnosis between the two groups. The study also found that HHIs have a greater connectivity between the dACC and DLPFC. Considering that the dACC is involved in conflict detection, it is interesting that HHIs exhibit increased levels of


Review Article

hypnotizability and lower levels of gray matter volume, white matter volume appears to be increased in HHIs. This is consistent with previous research that suggests that there are increased functional connections between neural networks during the hypnotic response. Suggestion Suggestions may be considered powerful tools for modulating perceptual experiences. Among the various perceptual experiences that can be induced or modulated through suggestion, the most common throughout the literature include visual and nociceptive experiences (Aleksandrowicz, Binder, & Urbanik, 2007; Koivisto, Kirjanen, Revonsuo, & Kallio, 2013; William J. McGeown et al., 2012; Valentini, Betti, Hu, & Aglioti, 2013). In regard to potential modulations of the experience of pain through suggestion, there are various applications that have been explored in previous studies. In a study by Aleksandrowicz, Binder, & Urbanik (2007), the researchers utilized fMRI to observe neural activity relative to a control as well as a hypnotic condition receiving a pain stimulus. Each subject underwent every condition: pain stimulus, pain stimulus following an analgesic suggestion, pain stimulus under neutral hypnosis (a state in which hypnosis is induced, but no suggestion is given), pain stimulus under hypnosis following analgesic suggestion, then finally “focusing and de-focusing of attention, in an alternate fashion”. Overall, the study found decreased thalamic activity, suggesting that sensory pathways, prior to reaching areas of higher-order processing, may become modulated as a result of hypnotic suggestions. Furthermore, nociceptive processing centers exhibit attenuated levels of activity, suggesting that suggestion can modify the experience of pain at the neurophysiological level. Although reduced levels of activity are observed in some nociceptive neural processing areas as a result of suggestion, some areas have been found to exhibit increased activity. The right anterior cingulate cortex (R-ACG), for instance, exhibits increased activity as a result of analgesic suggestions, suggesting that the reception of verbal suggestions, including those relating to nociceptive experiences, are actively and not passively integrated by the subject. Further research is necessary to expand these findings in analgesic suggestions to other categories of hypnotic suggestion (Aleksandrowicz et al., 2007). Furthemore, hypnotic suggestions related to experiences of analgesia affect specific aspects of the nociceptive experience. In a study by Valentini et al. (2013), through the use of a noxious laser pain stimulus, HHIs and LHIs were tested to observe “whether hypnotic suggestions of sensory and affective hypoalgesia or hyperalgesia differentially affected subjective ratings of laser-induced pain and nociceptive-related brain activity”. Subjects experienced an alteration of the affective domain of pain. Therefore, the pain experience was enhanced through the activation of neuroaffective processing centers. In particular, this study found modulated activity in the cingulate cortex as well as the somatosensory cortex. This phenomena indicates that conflict identification along with allocation of attentional resources results in modulation of somatosensory cortices, including those related to that of the experience of nociceptive stimuli (Valentini et al., 2013). In addition to modulating the subjective experience of

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pain or nociceptive stimuli, hypnotic suggestions are capable of modulating visual perception. In a study conducted by McGeown et al. (2012), the effects of hypnosis on color perception were explored in individuals of varying degrees of hypnotizability. In order to do this, researchers utilized fMRI in order to observe responses to two visual hallucinations (i.e., seeing color when looking at a grey-scale image and seeing greyscale when looking at a color image). These hallucinations were induced via suggestions with and without a hypnotic induction. The results of this study were consistent with other studies regarding nociceptive perception. HHIs experienced altered color perception under the experience of hypnotic suggestion. Strikingly, however, this study also found that HHIs are able to experience an altered color perception regardless of whether they are being administered a hypnotic suggestion or not. Furthermore, this study found evidence of default mode network alterations in activity during hypnotic responses, indicating that attentional networks are involved in attending to suggestions whether the subject is being exposed to a hypnotic stimulus or not (William J. McGeown et al., 2012). Visual perception can be modulated in considerably specific ways. In a study conducted by Koivisto et al. (2013), HHIs were tested in how much suggestions could alter the perceived color that they observed in briefly presented shapes. In order to investigate this, the researchers utilized EEG, then observed their two subjects’ responses as they were “briefly presented visual shapes under posthypnotic color alternation suggestions such as ‘all the squares are blue’”. Overall, there was a significant difference found in beta activity over the posterior cortex when presenting hypnotic suggestions and when not presenting suggestions. Beta-band oscillation was not observed during hypnotic suggestion, suggesting that activity levels in certain brain regions are modulated in the same way that color perception appears to be modulated in response to hypnotic suggestions (Koivisto et al., 2013). In addition to having the capacity to modulate perceptual experiences, hypnotic suggestion has been found to be able to induce altered motor activity states, particularly in HHIs. In a study conducted by Cojan et al. (2009), the effects of hypnotic suggestion on inducing motor paralysis were explored. Subjects underwent fMRI in three conditions: “normal state, hypnotic left-hand paralysis, and feigned paralysis”. Although increased activity in areas relevant to the intention of movement were observed, such as in the right motor cortex, precuneus activity additionally increased. This increase indicates that mental imagery, a largely implicated component of the hypnotic response, began to increase self-monitoring in movement. Overall, this study found that hypnotic suggestions act through internal representations and not directly through motor inhibition. This is consistent with the previous study that found that hypnotic suggestions act through top-down influences and not necessarily through bottom-up influences (Cojan et al., 2009). Extending the findings that hypnotic suggestion has been implicated in the alteration of motor capacities during the hypnotic response, a study by Pyka, et al (2011) aimed to investigate hypnotic paralysis by means of two sessions of fMRI. Subjects performed two sessions of fMRI, one session consisting of hypnotic suggestion of a left-arm paralysis and the other


Review Article

Table 2. Summary of the Studies Focusing on Suggestion

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session consisting of normal-state observations. The researchers found that specific neural networks are involved in the alteration of motor behavior through top-down influences. In particular, attentional neural networks such as the default mode network were found to have direct connections with areas such as the primary motor cortex in the frontal lobe. Furthermore, the precuneus is, again, implicated to have a significant role in mediating the role of self-referential mental imagery in maintaining the hypnotic state of heightened suggestibility. According to this study, not only does this extend to visual perception, but furthermore to the induction of altered motor behavior through hypnotic suggestion (Pyka et al., 2011). In order to observe whether or not bottom-up influences affect the hypnotic response in relation to elicited movement, Burgmer, et al. (2013) investigated the influence of hypnotically induced paralysis of the left arm on subjects during an imitation or mimicking task. Researchers utilized fMRI in order to assess movement imitation and observation while subjects were under two conditions: hypnotic suggestion of left-hand paralysis and without. The study found heightened activity levels in higherorder areas related to conflict detection (anterior cingulate cortex), and self-representation (insula), among other cortical regions. Consistent with previous studies that found that the effect of hypnotic suggestion seems to be restricted to areas starting from the thalamus and beyond, this paper further implicates that hypnotic suggestions appear to act through topdown influences and not through direct alteration of peripheral motor regions (Burgmer et al., 2013). In addition to being able to alter perception as well as being able to elicit movement, hypnotic suggestion has been found to be able to alter other cognitive processes. For instance, Ulrich, et al. (2015) investigated the effects of hypnotic suggestion on semantic processing. This study was centered on the effects of suggestion on how individuals extract information from verbal language. In order to observe this, subjects “performed the task once at normal wakefulness and once after the administration of hypnotic suggestions to perceive” an actual word “as a meaningless symbol of a foreign language.” Through the use of primes as forms of suggestions, in order to influence subjects during a discrimination task between pseudowords and target words, the study found that prime words had reduced effects when coupled with a hypnotic suggestion. Overall, this study suggests that semantic priming can be reduced as a result of a reduction of activity in automatic and attentive semantic processing centers, as induced by hypnotic suggestions (Ulrich, Kiefer, Bongartz, Grön, & Hoenig, 2015). As the previously discussed study suggests, there are various other cognitive abilities, aside from perception, that can be modulated through hypnotic suggestion. Another study conducted by Facco, et al. (2014) observed nonmusician subjects who “underwent MMN recording before and during a hypnotic suggestion for amusia. MMN amplitude was recorded using a 19-channel montage and then processed using the lowresolution electromagnetic tomography (LORETA) to localize its sources”. The researchers found that the recognition of music stimuli can be altered through hypnotic suggestion. More specifically, the study found that decreased amplitudes of activity were detected in the left temporal lobe. Overall, these findings suggest that, in addition to preceptual experiences and

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the elicitation of movements, cognitive experiences such as language processing and aesthetic experiences such as those of music perception can be altered through hypnotic suggestion (Facco et al., 2014).

Conclusions In summary, the central finding of this literature review is that there are specific neural networks that are implicated in the hypnotic response. Among these neural networks, the three, principle attentional neural networks—the salience network, the default mode network, and the executive control network— appear to be especially important in mediating the attentional component of hypnotizability, in which subjects pay increased attention to suggestions and, as a result are more susceptible to the given message. Through the interactions between these three neural networks as well as brain regions associated with selfconcept, self-movement, and perception, hypnotic suggestions can elicit altered perceptual, motor, and cognitive experiences. Considering these extensive capabilities of hypnotic suggestions in eliciting altered perceptual, motor, and cognitive activity, the potential applications of hypnosis to ameliorate different conditions are profound. For instance, psychological conditions associated with impaired thoughts or behaviors can be ameliorated through hypnotic suggestion. Similarly, conditions involving impaired cognitive abilities can be improved through hypnotic suggestion. Overall, the potential applications of hypnotic suggestion to clinical settings are considerable. Further research is critical in continuing to expand our knowledge on the nuanced components of hypnotic responses in order better comprehend and thereby apply the technique to relevant circumstances.

Acknowledgements I would like to thank Dr. Gary Elkins, my mentor, and Whitney Williams for guiding and supporting me in my research. Finally, I would like to thank the Baylor McNair Scholars Program for expanding opportunities for me, both in research and in life.

References Aleksandrowicz, J. W., Binder, M., & Urbanik, A. (2007). Hypnosis and analgesic suggestions in fMRI. Archives of Psychiatry and Psychotherapy, 9(3), 25–33. Alladin, A., & Alibhai, A. (2007). Cognitive hypnotherapy for depression: An empirical investigation. International Journal of Clinical and Experimental Hypnosis, 55(2), 147– 166. https://doi.org/10.1080/00207140601177897 Artimon, H. M. (2015). Hypnotherapy of a pain disorder: A clinical case study. International Journal of Clinical and Experimental Hypnosis, 63(2), 236–246. https://doi.org/10. 1080/00207144.2015.1002704 Brugnoli, M. P., Pesce, G., Pasin, E., Basile, M. F., Tamburin, S., & Polati, E. (2018). The role of clinical hypnosis and selfhypnosis to relief pain and anxiety in severe chronic diseases in palliative care: a 2-year long-term follow-up of treatment


4083–4093. https://doi.org/10.1093/cercor/bhw220 Kirsch, I., & Low, C. B. (2013). Suggestion in the Treatment of Depression. American Journal of Clinical Hypnosis, 55(3), 221–229. Koivisto, M., Kirjanen, S., Revonsuo, A., & Kallio, S. (2013). A Preconscious Neural Mechanism of Hypnotically Altered Colors: A Double Case Study. PLoS ONE, 8(8). https://doi. org/10.1371/journal.pone.0070900 Lipari, S., Baglio, F., Griffanti, L., Mendozzi, L., Garegnani, M., Motta, A., … Pugnetti, L. (2012). Altered and asymmetric default mode network activity in a “ hypnotic virtuoso” : An fMRI and EEG study. Consciousness and Cognition, 21(1), 393–400. https://doi.org/10.1016/j. concog.2011.11.006 Lynn, S. J., Malakataris, A., Condon, L., Maxwell, R., & Cleere, C. (2012). Post-traumatic stress disorder: Cognitive Hypnotherapy, Mindfulness, and Acceptance-Based Treatment Approaches. American Journal of Clinical Hypnosis, 54(4), 311–330. https://doi.org/10.1080/0002915 7.2011.645913 Mazzola, L. A., Lujan Calcagno, M. de, Obdrzalek, A., Pueyrredon, J. H., Cavanagh, S., Shubaroff, P., … Salvat, F. (2017). Hypnosis for Chronic Pain Management. Journal of Physiotherapy & Physical Rehabilitation, 02(01), 1–8. https://doi.org/10.4172/2573-0312.1000128 McGeown, William J., Mazzoni, G., Venneri, A., & Kirsch, I. (2009). Hypnotic induction decreases anterior default mode activity. Consciousness and Cognition, 18(4), 848– 855. https://doi.org/10.1016/j.concog.2009.09.001 McGeown, William J., Venneri, A., Kirsch, I., Nocetti, L., Roberts, K., Foan, L., & Mazzoni, G. (2012). Suggested visual hallucination without hypnosis enhances activity in visual areas of the brain. Consciousness and Cognition, 21(1), 100–116. https://doi.org/10.1016/j. concog.2011.10.015 McGeown, William Jonathan, Mazzoni, G., Vannucci, M., & Venneri, A. (2015). Structural and fun ctional correlates of hypnotic depth and suggestibility. Psychiatry Research Neuroimaging, 231(2), 151–159.https://doi.org/10.1016/ jpscychresns.2014.11.015 Palsson, O. S. (2015). Hypnosis treatment of gastrointestinal disorders: A comprehensive review of the empirical evidence. American Journal of Clinical Hypnosis, 58(2), 134–158. https://doi.org/10.1080/00029157.2015.1039114 Pekala, R. J. (2017). Addictions and Relapse Prevention. In G. R. Elkins (Ed.), Handbook of medical and psychological hypnosis: Foundations, applications, and professional issues (pp. 443–452). New York, NY: Springer Publishing Company, LLC. Picerni, E., Santarcangelo, E., Laricchiuta, D., Cutuli, D., Petrosini, L., Spalletta, G., & Piras, F. (2019). Cerebellar structural variations in subjects with different hypnotizability. Cerebellum, 18(1), 109–118. https://doi. org/10.1007/s12311-018-0965-y Pyka, M., Burgmer, M., Lenzen, T., Pioch, R., Dannlowski, U., Pfleiderer, B., … Konrad, C. (2011). Brain correlates of hypnotic paralysis-a resting-state fMRI study. NeuroImage, 56(4), 2173–2182.

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Review Article

in a nonrandomized clinical trial. Annals of Palliative Medicine, 7(1), 17–31. https://doi.org/10.21037/ apm.2017.10.03 Burgmer, M., Kugel, H., Pfleiderer, B., Ewert, A., Lenzen, T., Pioch, R., … Konrad, C. (2013). The mirror neuron system under hypnosis - Brain substrates of voluntary and involuntary motor activation in hypnotic paralysis. Cortex, 49(2), 437–445. https://doi.org/10.1016/j. cortex.2012.05.023 Cardeña, E., Jönsson, P., Terhune, D. B., & MarcussonClavertz, D. (2013). The neurophenomenology of neutral hypnosis. Cortex, 49(2), 375–385. https://doi.org/10.1016/j. cortex.2012.04.001 Cojan, Y., Waber, L., Schwartz, S., Rossier, L., Forster, A., & Vuilleumier, P. (2009). The Brain under Self-Control: Modulation of Inhibitory and Monitoring Cortical Networks during Hypnotic Paralysis. Neuron, 62(6), 862– 875. https://doi.org/10.1016/j.neuron.2009.05.021 Deeley, Q., Oakley, D. A., Toone, B., Giampietro, V., Brammer, M. J., Williams, S. C. R., & Halligan, P. W. (2012). Modulating the default mode network using hypnosis. International Journal of Clinical and Experimental Hypnosis, 60(2), 206–228. https://doi.org/10.1080/0020714 4.2012.648070 Elkins, G. R. (2014). Hypnotic relaxation therapy: Principles and applications. New York, NY: Springer Publishing Company. Elkins, G. R., Barabasz, A. F., Council, J. R., & Spiegel, D. (2015). Advancing Research and Practice: The Revised APA Division 30 Definition of Hypnosis. International Journal of Clinical and Experimental Hypnosis, 63(1), 1–9. https://doi.org/10.1080/00029157.2015.1011465 Elkins, G. R., Fisher, W. I., Johnson, A. K., Carpenter, J. S., & Keith, T. Z. (2013). Clinical hypnosis in the treatment of postmenopausal hot flashes. Menopause, 20(3), 291–298. https://doi.org/10.1097/gme.0b013e31826ce3ed Facco, E., Ermani, M., Rampazzo, P., Tikhonoff, V., Saladini, M., Zanette, G., … Spiegel, D. (2014). Top-down regulation of left temporal cortex by hypnotic amusia for rhythm: A pilot study on mismatch negativity. International Journal of Clinical and Experimental Hypnosis, 62(2), 129–144. https:// doi.org/10.1080/00207144.2014.869124 Green, J. P., & Lynn, S. J. (2017). Smoking Cessation. In G. R. Elkins (Ed.), Handbook of medical and psychological hypnosis: Foundations, applications, and professional issues (pp. 621–628). New York, NY: Springer Publishing Company, LLC. Halligan, P. W., & Oakley, D. A. (2014). Hypnosis and beyond: Exploring the broader domain of suggestion. Psychology of Consciousness: Theory, Research, and Practice, 1(2), 105–122. https://doi.org/10.1037/cns0000019 Hoeft, F., Gabrieli, J. D. E., Whitfield-Gabrieli, S., Haas, B. W., Bammer, R., Menon, V., & Spiegel, D. (2012). Functional brain basis of hypnotizability. Archives of General Psychiatry,69(10), 1064–1072. https://doi. org/10.1001/archgenpsychiatry.2011.2190 Jiang, H., White, M. P., Greicius, M. D., Waelde, L. C., & Spiegel, D. (2017). Brain activity and functional connectivity associated with hypnosis. Cerebral Cortex, 27(8),


Review Article

Sliwinski, J. R., & Elkins, G. R. (2017). Hypnotherapy to reduce hot flashes: Examination of response expectancies as a mediator of outcomes. Journal of Evidence-Based Complementary and Alternative Medicine, 22(4), 652–659. https://doi.org/10.1177/2156587217708523 Terhune, D. B., Cardeña, E., & Lindgren, M. (2011). Differential frontal-parietal phase synchrony during hypnosis as a function of hypnotic suggestibility. Psychophysiology, 48(10), 1444–1447. https://doi. org/10.1111/j.1469-8986.2011.01211.x Ulrich, M., Kiefer, M., Bongartz, W., Grön, G., & Hoenig, K. (2015). Suggestion-induced modulation of semantic priming during functional magnetic resonance imaging. PLoS ONE, 10(4), 1–15. https://doi.org/10.1371/journal. pone.0123686 Valentini, E., Betti, V., Hu, L., & Aglioti, S. M. (2013). Hypnotic modulation of pain perception and of brain activity triggered by nociceptive laser stimuli. Cortex, 49(2), 446–462. https://doi.org/10.1016/j.cortex.2012.02.005 Weitzenhoffer, A. M., Hilgard, E. R., & Kihlstrom, J. F. (1962). Stanford hypnotic susceptibility scale, form C. Palo Alto, CA: Consulting Psychologists Press.

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Adjusting Sample Concentration within Dynamic Range for Improved Data Analysis using Vacuum Ultraviolet Automated Library-Integrated Deconvolution Shubhneet Warar, Anna Arvidson, Meredith Ehlmann, Nick von Waaden, Ian G. M. Anthony, Touradj Solouki, Ph.D. Rapid identification of analytes is crucial in forensic, biomedical, and defense applications. Automated data analysis techniques can aid in time-sensitive and accuracy-focused analyte detection. Gas chromatography combined with vacuum ultraviolet spectroscopy (GC-VUV) is a powerful approach for detection of volatile organic compounds. GC-VUV allows for separation and identification of most analytes; however, mixtures of analytes that are not fully separated in the GC dimension can be difficult to analyze. GC-VUV peak convolution occurs when two or more detected analytes are not fully separated and can lead to misidentifications. Deconvolution is a mathematical method to extract the data corresponding to the convoluted analytes, and it can aid in identifying and quantifying convoluted components. We have automated deconvolution of GC-VUV data using a custom-built software package: “Vacuum Ultraviolet Automated Library-Integrated Deconvolution,” abbreviated VALID. In this study, we assess the performance of VALID on GC-VUV data collected from “real-world” essential oil samples. Samples of frankincense and grapefruit-based essential oil were injected into a GC-VUV instrument and analyzed both manually (via the standard VUV software) and automatically (via VALID). VALID utilized SIMPLISMA-ALS for the deconvolution and R-squared for the library-comparison metric.

Mosquito Surveillance Techniques and Results in Waco, TX Batool Unar Syed, Carolyn Carper, Henry Lyons, Deborah Olayinka, Jason Pitts, Ph.D. Mosquitoes are significant vectors for various pathogenic diseases, and due to climate change, some mosquito species may be able to expand into new territories, including Texas. McLennan County is experiencing rapid population growth making mosquito surveillance and dissemination of information about vector-borne diseases of particular interest. Setting up mosquito traps is a necessary public health objective in Texas, especially in the urban areas like Waco. Frequently used trapping methods include odor-baited BGS2 and EVS traps for collecting adults, and mosquito larvae collections from natural pool sites and human created sites such as used tires. After setting multiple traps and examining larvae and adult mosquitoes using microscopy, the most common mosquito species in Waco, Texas were identified. Mosquitoes were further identified by DNA extraction, polymerase chain reaction, and sequencing of a mitochondrial gene. More efficient techniques could be developed to streamline the identification process. Efficient identification of mosquito species and the additional detection of possible pathogens associated with them can help promote public awareness and improve disease prevention in urban settings.

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Original Abstracts

VALID succeeded in identifying 24.3% more analytes than manual identification alone (37 with only manual analysis vs. 46 with VALID). In addition, VALID successfully identified analytes that could not be detected by manual inspection (i.e., “hidden” GC peaks such as β-elemene in the grapefruit-based essential oil sample). The accuracy of the VALID-identified analytes was confirmed by GC retention time analysis. Moreover, GC retention time analysis allowed correction of manually miss-assigned GC peaks (viz., octyl acetate and p-propyltoluene, corrected to pentyl acetate and p-isopropyltoluene, respectively). Compared to manual analysis, VALID improved quantitation and reduced the analysis time by about 50%. VALID provided accurate analysis reports, saved time on data mining, and allowed for identification of hidden components present in complex samples. No single experimental trial was effective to yield correct assignments for all unknowns; however, injection of varied sample concentrations allowed for complete coverage of the VUV spectrometer’s dynamic range. More analytes were successfully assigned via the combination of manual VUV analysis, VALID, and GC retention time analysis than utilizing either of the three data analysis approaches alone.


Disparity of Race Reporting and Representation in Clinical Trials Leading to Cancer Dug Approvals from 2008 to 2018 Jonathan M. Loree, Seerat Anand, Arvind Dasari, Joseph M. Unger, Anirudh Gothwal, Lee M. Ellis, Gauri Varadhachary, Scott Kopetz, Michael J. Overman, Kanwal Raghav GI Medical Oncology, MD Anderson Cancer Center, Houston, TX

Original Abstracts

Importance: Representative racial/ethnic participation in research, especially in clinical trials that establish standards of care, is necessary to minimize disparities in outcomes and to uphold societal equity in health care. Objective: The objective of this study was to evaluate the frequency of race reporting and proportional race representation in trials supporting US Food and Drug Administration (FDA) oncology drug approvals. Design, Setting, and Participants: This database study comprised of all reported trials supporting FDA oncology drug approvals granted between July 2008 and June 2018. Primary reports of trials were obtained from PubMed and ClinicalTrials.gov. Food and Drug Administration approvals were identified using the FDA archives. The US population-based cancer estimates by race were calculated using National Cancer Institute–Surveillance, Epidemiology, and End Results and US Census databases. Main Outcomes and Measures: Primary outcomes were the proportion of trials reporting race and the proportion of patients by race participating in trials. Secondary outcomes included race subgroup analyses reporting and gaps between race proportion in trials and the US population. Descriptive statistics, Fisher exact, and χ2 tests were used to analyze the data. Proportions and odds ratios (OR) with 95% CIs were reported. Results: Among 230 trials with a total of 112,293 participants, 145 (63.0%) reported on at least 1 race, 18 (7.8%) documented the 4 major races in the US (white, Asian, black, and Hispanic), and 58 (25.2%) reported race subgroup analyses. Reporting on white, Asian, black, and Hispanic races was included in 144 (62.6%), 110 (47.8%), 88 (38.2%), and 23 (10.0%) trials, respectively. Between July 2008 and June 2013 vs July 2013 and June 2018, the number of trials reporting race (45 [56.6%] vs 100 [67.1%]; OR, 1.63; 95% CI, 0.93-2.87; P = .09) and race subgroup analysis (13 [16.1%] vs 45 [30.2%]; OR, 2.26, 95% CI, 1.16-4.67; P = .03) changed minimally and varied across races. Whites, Asians, blacks, and Hispanics represented 76.3%, 18.3%, 3.1% and 6.1% of trial participants, respectively, and the proportion for each race enrolled over time changed nominally (blacks, 3.6% vs 2.9% and Hispanics, 5.3% vs 6.7%) from July 2008 to June 2013 vs July 2013 to June 2018. Compared with their proportion of US cancer incidence, blacks (22% of expected) and Hispanics (44% of expected) were underrepresented compared with whites (98% of expected) and Asians (438% of expected). Conclusions and Relevance: Race and race subgroup analysis reporting occurs infrequently, and black and Hispanic races are consistently underrepresented compared with their burden of cancer incidence in landmark trials that led to FDA oncology drug approvals. Enhanced minority engagement is needed in trials to ensure the validity of results and reliable benefits to all.

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Anthropology

Descriptive analysis of blunt force trauma from variations of hammers on bovine bones Shawn Cleaver, Timothy L. Campbell In this study we assess if different types of hammers leave behind distinct trauma markers and develop novel descriptions that can be used to identify what tool was used. These descriptions are then tested using student participants to determine their accuracy. In forensic cases, determining the instrument responsible for a death is an important part of an investigation. For this study, three distinct types of hammers with unique head shapes were used to create trauma marks: a claw hammer, a tack hammer, and a rock hammer. Marks were standardized using a single individual applying similar force to three bovine long bones per hammer. Descriptions were formed by examining patterns of layered breakage, flaking, and crushed margins near the impact zone. Student participants then used the descriptions to match each cow bone to the associating hammer. The data from participants was then evaluated in order to determine the accuracy of each description. Similar studies involving blunt force trauma found some difficulty ascribing unique characteristics to differentiate blunt force instruments. This study, however, examines whether a beginning model can be created to accurately characterize different marks left by variations of hammers. Further research could build upon this study by including a wider range of instruments capable of generating blunt force trauma.

Observable Effects of Water Salinity on Bone Tammy Wake, Katie Binetti, Ph.D.

Biology

Analysis of breast cancer profiles in TCGA by TNBC subgrouping reveals a novel microRNA-specific cluster distinguishing tumor subtypes Rebecca Modisette, Joseph H. Taube, Ph.D. Breast cancers are diverse diseases comprising specific sub-types with unique characteristics and individual treatments. Patients with tumors that possess hormone receptorsâ&#x20AC;&#x201D;estrogen receptor (ER) and progesterone receptor (PR)â&#x20AC;&#x201D;or elevated human epidermal growth factor receptor 2 (HER2) respond to treatments targeted at these proteins. If tumors lack these markers, they are termed

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URSA Award Winners

An experiment was conducted in 2018 to explore the effects of water salinity on bone. At the time there was, and continues to be, limited research on the subject with most studies focused on soft tissue decomposition in aquatic environments. For the first experiment, five pieces of bone of approximately the same size were cut from a single bone sample. Four pieces were placed into tanks of water of differing salinity levels while the fifth was left as a dry control. Observable changes were noted throughout the tenweek experiment. These experiments produced unexpected results in the bone structure and appearance. The current study attempts to replicate the original experiment with the goal of reproducing the observed structural and morphological changes. Minor modifications were made to handling procedures during the second experiment and the study has covered a longer observation period, however, these modifications were not expected to change the outcome dramatically. I expected that the bones would react similarly in the second experiment as they did in the first, but perhaps with more pronounced changes due to the increased submersion time over an extended observation period (6 months versus 10 weeks). The methodologies of both experiments, as well as the subsequent results, are compared and contrasted in this paper. The results of this study may have implications for paleoanthropology, archaeology and forensic science.


growth factor receptor 2 (HER2) respond to treatments targeted at these proteins. If tumors lack these markers, they are termed triple-negative breast cancer (TNBC) and do not have targeted treatment options. Within TNBC, we can identify intrinsic subtypes by gene profiling. The subtypes include mesenchymal (M), basal-like 1 (BL1), basal-like 2 (BL2), and luminal androgen receptor (LAR). While the biological origins of M and LAR TNBCs have been made evident, the regulatory nodes that set BL1 apart from BL2 TNBCs remain ambiguous. The Cancer Genome Atlas (TCGA) was used to derive small-RNA inclusive RNA sequencing data from TNBC-subtype classified breast cancers. We then identified microRNA-centric regulatory clusters that are likely to create different gene expressions between said subtypes. I then tested a group of these microRNA-mRNA regulatory nodes in cell line models of TNBC and found that specific microRNAs both had expression differences between TNBC subtypes, and reexpression is sufficient to modulate cell cycle and DNA damage-related gene expression networks. These data provide a rationale for genomically-informed treatments in patients with TNBC.

The Effects of Exposure to Various Frequencies of Noise on the Reproductive Rates of Drosophila melanogaster Kamerin Smith, Peyton Mizell, Lauren Hoogenakker, Marty Harvill, Ph.D.

URSA Award Winners

The objective of this project was to explore the effects of noise pollution on the model organism Drosophila melanogaster. Three test groups were exposed to different frequencies while the population growth and behavior of the organisms were monitored. To conduct this experiment, four sound resistant boxes, each containing three vials of D. melanogaster, were exposed to varying frequencies. The control group was exposed to a relatively noiseless environment (no additional sound) while the first, second, and third test groups were constantly exposed to frequencies of 200 Hz, 9000 Hz, and 15000 Hz, respectively. After constant exposure for 28 days, we observed that the flies exposed to 200 Hz and 15000 Hz exhibited an overall increase in population size when compared to the flies exposed to 9000 Hz. The specimensâ&#x20AC;&#x2122; behaviors were carefully observed and recorded across all groups. Notably, the test group exposed to a frequency of 9000 Hz experienced a stagnant period of growth followed by a significant decrease in population size. Additionally, these flies showed a significant decline in activity (i.e. flying) compared to the 200 Hz and 15000 Hz test groups.

Genetic suppressors of str-2 serotonin response defects Amy Kumar, Shiv Gakhar, Julian Harris-Quanquin, Shelby Story, Bill Vo, Henry Vo, Myeongwoo Lee, Ph.D. C. elegans, a nematode, is a model organism to study animal behavior and development. The genome of the C. elegans shows that there is a similarity between the genes of the nematode and that of humans. C. elegans are widely used because of its rapid life cycle and its small size which makes it easy for laboratory cultivation. The str-2 gene in C. elegans is predicted to be responsible for receptor activity linked to olfactory responses. Through binding of odorants in specific olfactory neurons, str2 allows them to detect pheromones, environmental threats, and nutrition, essentially playing a vital role in their behavioral functioning. The egg-laying behavior of C. elegans is regulated by its surroundings and can be activated or inactivated through various environmental cues. However, without a properly functioning olfactory system, we hypothesize that C. elegans will be unable to initiate standard egglaying activity through its inability to pick up on these environmental cues even in the presence of serotonin. In this study, we created a mutagenized str-2 C. elegans that was induced by ethyl methanesulfonate (EMS) which would also be resistant to the egg-laying ability response to serotonin. The C. elegans were treated and screened to ensure that they represented their ability to lay the most or least eggs in response to serotonin. Egg laying assays were repeated until the offspring was uniform. There is a report that the str-2 mutants have a decreased life cycle compared to the non-mutants. These mutants had crippled olfactory responses to environmental transmitters, and lacked sensory abilities that hindered life. In addition, we found that the number of offspring produced by the mutants were significantly less than those of the non-mutants.

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Anopheles species identification in Ethiopia: Comparison of morphology and molecular based techniques Joseph Spear, Sae Hee Choi, Tamar E. Carter, Ph.D. Malaria is an ongoing health challenge in Ethiopia. One way to address it is to know the vector species involved in its spread. Anopheles stephensi, a common malaria vector in Southeast Asia, the Middle East, and the Arabian Peninsula, was recently discovered in east Ethiopia. Here we compared morphological identification of An. stephensi to molecular based approaches to determine best approaches for An. stephensi identification in Ethiopia. An. stephensi and Anopheles gambiae s.l. specimens were collected as wild caught adults using CDC light traps and pyrethrum spray catch. Larvae and pupae were also collected using dipping method at various breeding sites and reared in lab to adult-stage. Mosquitoes were then morphologically identified using morphology keys. An. stephensi and An. gambiae s.l. (as a control) then underwent molecular 129 analysis. The ITS2 and CO1 loci were PCR amplified and sequenced for each mosquito for species determination. An endpoint PCR assay was completed based on ITS2 amplicons (presence/ absence of band in gel). The results indicate that out of the 49 mosquitoes that were morphologically identified as An. stephensi, 44 have been confirmed to be An. stephensi through PCR and gel electrophoresis. There were some mosquitoes that were incorrectly morphologically determined in both An. stephensi and An. gambiae s.l. specimen which could be due to collection methods or morphology keys. Overall, the molecular analysis of the An. stephensi was consistent with the morphologically determined species identification. Further study into whether method of collection correlates with misidentification are needed.

Chemistry & Biochemistry

Comparing Basis Sets and Methods in Density Functional Theory to Optimize the Electronic Structure of Sodium Adducted Carbohydrates

Characterizing glycan structures is necessary because of their functions in cell-cell communication, cancer progression, and genetic diseases. However, analyzing glycan structures is challenging due to the complexity of glycan branching and carbohydrate stereochemistry, thus computational techniques can be employed to model structures. Here, we use Density Functional Theory (DFT), a computational method, to compare electronic structures for carbohydrate model systems. Experimentally, electrospray ionization-mass spectrometry (ESI-MS) analyzes carbohydrates that have adducted to sodium ions, thus we are investigating sodiated-glucose and fucose. Herein, we use the Gaussian09 platform to provide optimized geometries. The methods, B3LYP and BPW91, utilize DFT to treat bonds as “springs”, optimizing their positions to deduce parameters such as zero-point vibrational energy. Variations among molecular parameters are monitored to discriminate amongst methods and basis sets. For example, there was greater variation in the calculated zero-point vibrational energy of β-fucose (standard deviation, SD= 14193, n=11) compared to β-glucose (SD=12283, n=11). These calculated parameters should be consistent, as the sugar-adduct complex does not change; thus, this variance implies that some basis sets and methods are not optimizing the adducts to have the same electronic structures. Such optimizations are necessary to ensure that more complex calculations, like collisional cross sections (CCS), are using experimentally observed structures. Future work will calculate CCS from optimized geometries to be compared to experimentally measured CCS of these sodium-adducts using ion mobilityMS. Through this work, we will define the optimal DFT methods and basis sets to use for modeling carbohydrate and glycan structures.

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URSA Award Winners

Meg E. McCutcheon, Emily D. Ziperman, Srinivas Pulipakas, Emvia I. Calixte, Elyssia S. Gallagher, Ph.D.


Communication Science & Disorders

Effects of Simulated Therapeutic Horse-Riding on Speech Therapy in Adult Brain Injury Callie Terrell, Clare Kuhlmann, Kat Delgado, Donna C. Powell, Paul T. Fillmore, Ph.D., Kathy Whipple, Ph.D. This study investigated the effects of simulated therapeutic horse-riding on speech therapy in adults who had sustained brain injuries. Therapeutic horse riding has been used effectively in many patient populations (e.g. autism, PTSD, and brain injury), but its mechanisms remain somewhat poorly understood. In the current work, we tested a novel form of this therapy (a mechanical horse), to assess its impact as a supplement to traditional speech therapy. Five participants completed the study (2 female, mean age=67.8), and brain injuries included stroke (CVA), traumatic brain injury (TBI), craniotomy/tumor removal, and mild dementia. Speech and language symptoms included aphasia, apraxia, and dysarthria, among others. Each patient was assessed both before and after therapy using standard speech-language and cognitive tests, and a resting-state EEG exam. We used a single-subject, multiplebaseline design, in which patients waited to begin riding until baseline sessions were complete. Treatment sessions were conducted twice a week for approximately nine weeks. During each treatment session, patients completed a standardized object-naming test while riding the mechanical horse, followed by individualized speech therapy after dismount. We focused on naming accuracy and speed of response as within-session measures of progress, as well as on overall changes in pre-post assessments. Data analyses are ongoing, but preliminary results suggest meaningful changes in speech, language and cognitive function following therapy for some but not all patients. We will discuss these results in the overall context of speech therapy following brain injury, and the potential of hippotherapy to improve the outcomes of traditional methods.

Binaural Frequency Discrimination: Implication for Bimodal and Electric Acoustic Stimulation Users

URSA Award Winners

Sophie Suri, Yang-Soo Yoon, Ph.D. Ability to resolve different frequency components is critical for speech perception in noise. In this study, we compared the ability of normal-hearing listeners to discriminate tones when the stimuli were presented in dichotic and sequential manner across ears. Results showed that binaural frequency discrimination thresholds became worse as target frequencies increased both in dichotic and sequential listening. However, dichotic listening requires larger frequency differences across ears than the sequential listening.

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Family & Consumer Science

Tailoring Fashion Therapy (FT) for Mental Health Patients and their Needs Victoria McKenty, Jay Yoo, Ph.D. The goal of this study is to review the current literature on Fashion Therapy (FT) and explore how FT can be implemented into treatment plans with more personalization for mental patients. FT is a form of cognitive-behavioral therapy that incorporates Appearance Management Behavior (AMB) and Retail Therapy (RT) as tools to achieve a positive body-image and improved selfesteem. AMB looks to behaviors and practices such as hygiene, grooming, skincare, makeup, posture, body language, diet, exercise, and strategic dress (SD), in order to improve self-image and body satisfaction. SD makes up the largest section of AMB, focusing on clothing and dress to strategically highlight and cover features of the body to make the physical appearance seem more attractive to both the patient and others. To do this, SD looks to elements of clothing and dress that include style, silhouette, color, pattern, fiber content, fabric structure, and fit. Mental patients who have previously participated in FT programs have experienced a reduction in anxiety, depression, and obsessive-compulsive behavior, and an increase in their self-esteem, and more realistic ideas of body image. To better treat these symptoms, experts can begin to categorize patients based on their needs. FT can use the study by Lee and Kim (2007) that separated patients into four lifestyle groups: well-being (WB), reasonable value-oriented (RVO), ostentatious consumption (OC), and bad-being (BB), in order to tailor treatment plans to patientsâ&#x20AC;&#x2122; needs. This personalization provides opportunities for more effective treatment of mental health patients.

Physics

N-Cadherin Dimerization Attenuated by Cadmium at Calcium Concentration in Neural Synapses Garrett Williams, Zhenrong Zhang, Ph.D.

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URSA Award Winners

Cadherins are calcium-dependent cell-adhesion proteins that are vital to the formation and maintenance of solid tissues. Neural (N-) Cadherin plays an essential role in early development processes such as angiogenesis and development of the neural plate. Cell-cell adhesion and hence, the integrity of tissue and organ systems is entirely dependent on the ability of two adjacent calciumbound cadherins to form dimer. The prerequisite of calcium (Ca2+) binding for adhesion begs the question of whether other divalent cations could promote or inhibit dimer formation. Due to its ionic radius and chelation geometry, cadmium (Cd2+) has been shown to substitute for Ca2+ in select physiological processes. The studies described here evaluate whether Cd2+ binds to N-Cadherin as a heteroligand, thereby disrupting calcium-induced dimerization. Studies were also conducted to predict the effects of Cd2 at relatively low Ca2+ concentration, as typical for excitatory neural synapses. This study features both experimental and numerical analysis of ligand binding 102 and ligand-induced dimerization. Based on the model for ligand-induced dimerization, computational studies of linked equilibria were conducted. These algorithms resolve binding affinity constants for ligand binding to N-Cadherin, and dimerization constants for the self-association of ligand-bound monomers. Experimentally, N-Cadherin was titrated with Ca2+ in the absence and presence of Cd2+ as monitored by fluorimetry. Monomer-dimer equilibrium experiments were conducted, and then the fraction of dimer was assessed using size exclusion chromatography to determine Cd2+ effects on calcium-induced dimerization. Studies were also designed and conducted in vitro at physiologically relevant levels of Ca2+. The ligand binding constants resolved for calcium and cadmium indicated that cadmium binds to N-cadherin with ~4x higher affinity than that of calcium. Further, low levels of Cd2+ decrease dimer formation at calcium concentrations found at neurological synapses. Analysis shows that Cd2+ disrupts dimerization of N-Cadherin, consistent with its competition for the Ca2+-binding sites. Our observations of dimer disassembly in the presence of Cd2+ support the hypothesis that at very low levels, Cd2+ will have minimal effect on N-Cadherin mediated celladhesion in the body; however, Cd2+ at these same levels at excitatory synapses can disrupt cell adhesion and compromise normal neurological processes including the formation of memory and reflex stimulation.


Student Research Spotlight: Roger Neuberger

Student Spotlight

Year: Sophomore Major: Science Research Fellow (concentration in Biochemistry) Faculty Mentor: Daniel Romo, Ph.D.

The Romo Group’s focus is on the total synthesis of bioactive natural products. These are compounds discovered in nature that are often structurally complex and cause specific effects in target cells. In the process of synthesizing these compounds, multiple intermediates of increasing complexity are collected and analyzed to further understanding of the compounds’ structure-activity relationships, or which functional groups provide the best activity and selectivity for a molecules target inside the cell. This method of data collection en-route to the natural product is called pharmacophore-directed and was recently disclosed by the Romo Group. Roger Neuberger, a sophomore Science Research Fellows major, joined the Romo Group at the beginning of summer of 2019 through BTRUE (Baylor Transdisciplinary Research Undergraduate Experience). Despite already being accepted into the lab before the beginning of summer, BTRUE made it a financial possibility for him to stay over the summer and have an excellent opportunity to meet students in other labs on campus. Roger says that he has made some excellent progress over the summer and has since continued to work during the school year. Over the summer, his work was primarily toward ineleganolide, a natural product originally isolated from the Taiwanese soft coral Sinularia inelegans. Ineleganolide has shown to be cytotoxic towards leukemia cells, and the group is hoping to analyze these effects alongside their synthetic studies. Since the end of the summer, his work has shifted toward yonarolide, another natural product sourced from the same genus of soft corals. Roger’s favorite experience in the lab was hitting his first synthetic target at the end of the summer. He had selected an intermediate in the synthesis of ineleganolide that he wanted to reach before BTRUE ended, and it took the entirety of the 10-week program to reach it. He remembers “all the stress, uncertainty, and frustration from all the failed attempts finally lifted as I collected the 17 milligrams that took an entire summer to synthesize over ten steps”. After putting in hundreds of hours of work into synthesizing this compound and being completely unsure if the route would actually work, the feeling as all that work paid off was indescribable, he says. Roger states that the best part of his work is the independence of it. From speaking with students in other labs, it’s fairly common for undergraduates to be stuck doing the tedious and repetitive tasks that nobody else wants to do. While his research requires a tremendous time commitment, he feels that it never really gets old; he’s always doing something new, trying different reactions on different scales under different conditions. He has the freedom to do just about anything he thinks will work to reach a target, but also has the experience and expertise of the grad students in the lab to draw on if there is anything he’s unsure of or unfamiliar with. “There is something extremely satisfying about mixing flammables and carcinogens to create a new compound that could potentially cure cancer. Chances are it won’t, of course, but it could”, Roger says. When asked what a typical day in the lab looks like, he says that every day in the lab is a little different. His schedule is completely dependent on what reactions he’s trying to run. Over the summer, he would show up around 8 AM and usually wouldn’t leave until 8 PM or later once his reactions for the day were all complete. During the semester, though, it’s tough to be able to maintain those kinds of hours. Now, he feels lucky if he can complete one or two reactions per week, usually having to split them over multiple days (which is definitely not ideal). Typically, he will choose a day or two where he will be available at the times he needs to be, and then he’ll set everything up before his first class. In between classes, he’ll head back to check the progress, making additions where necessary. Once the reaction is complete, he’ll spend the next few hours working it up, purifying the product, and then running a few tests (e.g. NMR and mass spec) to see if the reaction worked. For those aspiring to get involved with research, Roger recommends talking to your professors! He says that “so many professors at Baylor genuinely love to speak with students, and any number of them would be happy to help you find a lab that suits your interests. Once you’ve found one, reach out to the PI and ask if they have any papers that would help you explore their research further, and eventually ask to set up a meeting to discuss joining their lab”. Additionally, summer programs are a great opportunity to get some experience before the semester starts up. He cautions that your time in the lab becomes so limited once the semester starts up that it is definitely beneficial to get your training out of the way beforehand. Another piece of advice from Roger is “don’t count yourself out, either; it seems like a lot of students are afraid to apply or feel like they won’t get into a lab just because they don’t have previous research experience. Any experience others have had at the undergraduate level is not all that valuable when going into a new lab and they will have to be trained just the same as you”. The most important traits that the majority of professors look for are availability and interest—they want to make sure you will be around long enough for training you to be worthwhile, and they want you to have enough of an interest in the material so that they won’t have to drag you through every step. We hope that an insight into Roger’s experience with research motivates you all to stay driven and get involved with research at Baylor!

64 | Scientia 2020


Student Research Spotlight: Tina Li Year: Sophomore Major: Bioinformatics Faculty Mentor: Erich Baker, Ph.D.

From Left to Right: Dr. Erich Baker, Ting-Chen Wang , Tina Li, Iris Chen, and Samuel Shenoi

Scientia 2020 | 65

Student Spotlight

In light of the recent coronavirus outbreaks that began reaching headlines early last December, student researchers here at Baylor have used the current situation as an opportunity to examine the novel coronavirus (SARS-CoV-2). The research team is composed of four undergraduates, all of whom are bioinformatics majors, and includes Tina Li (‘22 ), Sam Shenoi (‘21), Ting-Chen Wang (‘21), and Iris Chen (‘21). Their research article, titled “Phylogenetics analysis of SARS-CoV-2 within known coronavirus diversity”, seeks to analyze the SARS-CoV-2 virus from three perspectives: intergenic analysis between SARS-CoV-2 and other Coronaviridae viruses, intragenic analysis within the SARS-CoV-2 virus, and the SARS-CoV-2 relationship to the human proteome. The lab has approached these three perspectives by collecting genomic and protein sequences from SARS-CoV-2 isolates, other viruses from the Coronaviridae family, and human proteins. With these sequences, the team utilizes BLAST software to determine similarity between the sequences. The research currently involves solo work, with weekly group meetings to evaluate the objectives they aim to achieve with their data. Beginning the project also came with considerable challenges. New sequences are added to the global reference databases almost daily, so proper data collection and result reproduction have become paramount. The team has managed to accomplish this by automating much of the work which allows for a fast turnaround when new information about the virus comes to light. Regarding her favorite aspect of their research, Wang said that she finds it rewarding to be able to study a topic that is currently at an early stage of discovery. Since knowledge about the novel virus and its transmission is limited, Wang also acknowledges that it is oftentimes difficult to gather enough data to hypothesize the generalized structure of the virus. For students interested in undergraduate research, Shenoi recommends that they begin to investigate their research interests and find a faculty mentor who specializes in researching those certain topics. For this project, all four lab members took the initiative to reach out to Dr. Baker and propose the idea of conducting SARS-CoV-2 research on campus.


Scientia's Mission Scientia shall provide a professional platform upon which undergraduates of Baylor University are able to publish personally conducted and outstanding research in the areas of biological sciences, physical sciences, mathematics, and technology.

Accepted Formats Research Articles (maximum 4500 words including captions and references) presenting major findings performed by current undergraduate level students enrolled at Baylor University. Research articles must include an abstract, introduction, materials and methods, up to six figures or tables, results, and discussion. Review Articles (maximum 6000 words including captions and references) synthesizing developments of interdisciplinary significance written by current undergraduate level students enrolled at Baylor University. Review articles must include an abstract and an introduction outlining the topic of discussion. Abstracts (maximum 500 words) proposing research topics currently being investigated by current undergraduate level students enrolled at Baylor University.

Submitting to Scientia To submit to Scientia for publication, email your research article, review article, abstract, or Student Spotlight Submission to baylor.scientia@gmail.com. As we work to develop our new website for the 2021 edition, you may also be asked to submit your publication on the new website (url currently unavailable). Please check baylor.edu/burst for any updates. To read previous editions of Scientia online, please visit https://www.baylor.edu/burst/index.php?id=863108 For more information, please email Scientia's Editorial Board at baylor.scientia@gmail.com.

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Want to become involved in research at Baylor University and beyond? Learn about Baylor Undergraduate Research in Science and Technology (BURST)! BURST is the student organization for Baylor undergraduate students interested in scientific research.

Mission

To increase awareness of undergraduate research within the Baylor campus, we aim to provide opportunities for undergraduates to optimize their research experiences, and educate them in the proper habits and techniques of research in scientific fields.

Journal Clubs

Members participate in peer-led Journal Clubs of a variety of fields. Each Journal Club reads through and discusses a selection of research articles. Some Journal Clubs consist of a discussion with a Baylor professor or another expert research about the research that they pursue.

Lab Tours

A tour of the lab in the Baylor Sciences Building (BSB), guided by the Baylor University professor or graduate student of whose research paper was read during the Journal Club preceding the tour allow members to see various research environments across campus. Members have the opportunity to ask questions, visualize the research techniques they have learned about, and occasionally gain hands-on experience with lab equipment.

Scientia

Scientia is the Baylor Undergraduate Research Journal of Science and Technology. First published in the Spring of 2014, Scientia is a yearly publication produced by BURST members and supported by the Baylor College of Arts and Sciences.

Conferences

Members who are currently doing research are encouraged to attend a variety of conferences, where they can present their findings to the scientific community in a professional environment. BURST works closely with URSA during URSA Scholars' Week, the annual Baylor conference showcasing undergraduate research. We will also promote other conference opportunities in Texas and around the nation.

Lab Technique Workshops

BURST organizes a workshop once a semester to teach its members fundamental laboratory skills, such as pipetting and gel electrophoresis. The goal of this workshop is to provide students who have never done research an opportunity to learn fundamental skills that will be useful in their first research experience.

Service in the STEM Fields

BURST organizes opportunities for members to actively engage in spreading interest for the sciences and technology in Waco. Members may choose to serve by tutoring and advising students at a high school in Waco, offer their time with Habitat for Humanity, or even volunteer on campus at the Baylor Beauchamp Addiction Recovery Center (BARC).

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Lecture Series

Each semester, BURST organizes a lecture series featuring research experts from both Baylor and beyond. These lectures provide members with an increasing scope of knowledge about current research and how they can become involved.

Academic Workshops

New to research? Come join us for the BURST Research Series where we walk you through each step of the scientific method and even give you a chance to help create a study! Stressed out about your MCAT or GRE? Led by members who have excelled on these exams, BURST offers workshops that will help prepare you and equip you with the resources you need to succeed.

Social Events

Throughout the semester, BURST offers several opportunities to just hang out with other members! Whether that’s playing basketball or volleyball at the SLC, watching movies on the big screens, or playing card games in the BSB, you’ll have a blast getting to know your fellow BURST memebrs!

Research Internship Day

BURST hosts an annual BURST Research Internship Day to increase awareness of the many research internship opportunities for undergraduate students at Baylor. This event allows students to meet with representatives from a variety of internships across Texas, listen to other students speak about their own research internship experiences, attend presentations by BURST officers regarding how to find, apply, and make the most of research internships, and also listen to lectures by Baylor University professors about the research they partake in.

For Prospective Members Are you interested in joining BURST? Please contact us at burst@baylor.edu. We’d love to get to know you!

68 | Scientia 2020


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