Scientia
Scientia is The Triple Helix’s primary student research publication. Scientia is a space for UChicago’s undergraduate scholars to contribute to the dynamic and ever-expanding field of science. Each issue of our journal is an affirmation of our commitment to fostering scholarly inquiry and intellectual growth among undergraduates.
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USING PHOTOGRAPHY AND ARTIFICIAL INTELLIGENCE TO ESTIMATE HEMOGLOBIN FOR REMOTE ANEMIA DIAGNOSTIC IMPROVEMENT by Mohammed I. Lone
1. Tamburrano, A. et al. “Evaluation and cost estimation of laboratory test overuse in 43 commonly ordered parameters through a Computerized Clinical Decision Support System (CCDSS) in a large university hospital.” PloS one vol. 15,8 e0237159. doi:10.1371/journal.pone.0237159 (2020).
2. Sheth, T.N. et al. “The relation of conjunctival pallor to the presence of anemia.” Journal of general internal medicine vol. 12,2: 102-6. doi:10.1046/j.15251497.1997.00014.x (1997).
3. Lin, S. et al. “Retooling Primary Care in the COVID-19 Era.” Mayo Clinic proceedings vol. 95,9: 1831-1834. doi:10.1016/j.mayocp.2020.06.050 (2020).
4. Park, S.M. et al. “mHealth spectroscopy of blood hemoglobin with spectral super-resolution,” Optica 7, 563-573 (2020).
5. Dimauro, G. et al. “An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset.” Artificial intelligence in medicine vol. 136: 102477. doi:10.1016/j.artmed.2022.102477 (2023).
6. Hasting, G. et al. Colour spaces – a review of historic and modern colour models*. African Vision and Eye Health. 71. 10.4102/aveh.v71i3.76 (2012).
7. Laird, Philip W. et al. “Cystic benign melanosis of the conjunctiva.” Cornea vol. 31,11: 1273-7. doi:10.1097/ICO.0b013e31823d1ec4 (2012).
8. Haleem A. et al. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sens Int.;2:100117. doi: 10.1016/j.sintl.2021.100117. Epub 2021 Jul 24. PMID: 34806053; PMCID: PMC8590973. (2021).
Kv1.8-NULL MICE HAVE SIGNIFICANT DECIFITS ON CHALLENGING BALANCE TASKS by Dana Silvian and Emily Scott
1. EatockLab
2. Lee, S. I., Conrad, T., Jones, S. M., Lagziel, A., Starost, M. F., Belyantseva, I. A., Friedman, T. B., & Morell, R. J. A null mutation of mouse KCNA10 causes significant vestibular and mild hearing dysfunction. Hearing Research 300, 1–9. https://doi.org/10.1016/j.heares.2013.02.009 (2013).
3. Eatock, RA. Specializations for fast signaling in the amniote vestibular inner ear. Integrative and Comparative Biology, 58, 341-350. https://doi.org/10.1093/icb/icy069 (2018).
4. Agrawal, Y., Ward, B. K., & Minor, L. B. Vestibular dysfunction: Prevalence, impact and need for targeted treatment. Journal of Vestibular Research 23, 113–117. https://doi.org/10.3233/ves-130498 (2013).
5. Huterer, M., Cullen, KE. Vestibuloocular reflex dynamics during high-frequency and high-acceleration rotations of the head on body in rhesus monkey. J. Neurophysiol. 88, 13–28. https://doi.org/10.1152/jn.2002.88.1.13 (2002).
6. Enthoven, L., Dalm, S., de Kloet, E. R., & Oitzl, M. S. Swim posture of mice does not affect performance in the Water Maze. Brain Research 1003, 36–41. https://doi.org/10.1016/j.brainres.2003.10.074 (2004).
7. Fiker, R., Kim, L. H., Molina, L. A., Chomiak, T., & Whelan, P. J. Visual Gait Lab: A user-friendly approach to gait analysis. Journal of neuroscience methods, 341, 108775. https://doi.org/10.1016/j.jneumeth.2020.108775 (2020).
8. Rahman, S. M.; Hauser, C.; Faucher, S.; Fine, E.; Jonnala, R.; Duzgezen, V.; Strangio, B.; Liang, B.; Luebke. Role of Calcitonin Gene-Related Peptide (CGRP) in Auditory, Static, and Dynamic Imbalance Behaviors: Implications for Vestibular Migraine. bioRXIV. https://doi.org/10.1101/2022.06.03.494764 (2022).
9. Chang, H. H. V., Morley, B. J., & Cullen, K. E. Loss of α-9 Nicotinic Acetylcholine Receptor Subunit Predominantly Results in Impaired Postural Stability Rather Than Gaze Stability. Frontiers in cellular neuroscience 15, 799752. https://doi.org/10.3389/fncel.2021.799752 (2022).
10. Jones SM, Vijayakumar S, Dow SA, Holt JC, Jordan PM, Luebke AE. Loss of α-Calcitonin Gene-Related Peptide (αCGRP) Reduces Otolith Activation Timing Dynamics and Impairs Balance. Front Mol Neurosci. 11, 289. https://doi.org/10.3389/fnmol.2018.00289 (2018).
11. Tung, V. W., Burton, T. J., Dababneh, E., Quail, S. L., & Camp, A. J. Behavioral assessment of the aging mouse vestibular system. Journal of visualized experiments : JoVE 89, 51605. https://doi.org/10.3791/51605 (2014).
12. Tung, V. W., Burton, T. J., Quail, S. L., Mathews, M. A., & Camp, A. J. Motor Performance is Impaired Following Vestibular Stimulation in Ageing Mice. Frontiers in aging neuroscience 8, 12. https://doi.org/10.3389/fnagi.2016.00012 (2016).
CHARACTERIZATION OF PLASMA cfDNA METHYLA TION PROFILE IN COLORECTAL CANCER PATIENTS AND HEALTHY INDIVIDUALS: EXPLORING SIGNAL AND NOISES IN cfDNA DATA ANALYSIS by Carisa Zeng
1. Keum, N. et al. Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. Nature reviews Gastroenterology & hepatology, 16(12), 713-732 (2019).
2. Sinicrope, F. A. Increasing incidence of early-onset colorectal cancer. New England Journal of Medicine, 386(16), 1547-1558 (2022).
3. Conteduca, V. et al. Precancerous colorectal lesions. International journal of oncology, 43(4), 973-984 (2013).
4. Carethers, J. M. et al. Genetics and genetic biomarkers in sporadic colorectal cancer. Gastroenterology, 149(5), 1177-1190 (2015).
5. Volckmar, A. L. et al. A field guide for cancer diagnostics using cell-free DNA: From principles to practice and clinical applications. Genes, Chromosomes and Cancer, 57(3), 123-139 (2018).
6. Nishiyama, A. et al. Navigating the DNA methylation landscape of cancer. Trends in Genetics, 37(11), 1012-1027 (2021).
7. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487(7407), 330 (2012).
8. Hinoue, T. et al. Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome research, 22(2), 271-282 (2012).
9. Su, J. et al. Homeobox oncogene activation by pan-cancer DNA hypermethylation. Genome biology, 19(1), 1-12 (2018).
10. Kong, X. et al. Defining UHRF1 domains that support maintenance of human colon cancer DNA methylation and oncogenic properties. Cancer Cell, 35(4), 633-648 (2019).
11. Vymetalkova, V. et al. Circulating cell-free DNA and colorectal cancer: a systematic review. International journal of molecular sciences, 19(11), 3356 (2018).
12. Kang, S. et al. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome biology, 18(1), 1-12 (2017).
13. Salvi, S. et al. Cell-free DNA as a diagnostic marker for cancer: current insights. Oncotargets and therapy, 6549-6559 (2016).
14. Lo, Y. D. et al. Epigenetics, fragmentomics, and topology of cell-free DNA in liquid biopsies. Science, 372(6538), eaaw3616 (2021).
15. Blank, A. et al. Tumor heterogeneity in primary colorectal cancer and corresponding metastases. Does the apple fall far from the tree?. Frontiers in medicine, 5, 234 (2018).
16. Wang, W. et al. Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities. In Seminars in cancer biology (Vol. 55, pp. 37-52). Academic Press. (2019).
17. Jia, M. et al. Different definitions of CpG island methylator phenotype and outcomes of colorectal cancer: a systematic review. Clinical epigenetics, 8(1), 1-14 (2016).
18. Müller, D. et al. DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer, 188722 (2022).
19. Salvi, S., et al. “Cell-free DNA as a diagnostic marker for cancer: current insights.” OncoTargets and Therapy Volume 9: 6549-6559. (2016).
20. Hanahan, D. et al. Hallmarks of cancer: the next generation cell, 144(5), 646-674 (2011).
21. Hanahan, D. Hallmarks of cancer: new dimensions. Cancer discovery, 12(1), 31-46 (2022).
22. Curradi, M. et al. Molecular mechanisms of gene silencing mediated by DNA methylation. Molecular and cellular biology, 22(9), 3157-3173 (2002).
23. Zhu, W. G. et al. Methylation of adjacent CpG sites affects Sp1/Sp3 binding and activity in the p21Cip1 promoter. Molecular and cellular biology, 23(12), 4056-4065 (2003).
24. Wu, M. C. et al. Powerful SNP-set analysis for case-control genome-wide association studies. The American Journal of Human Genetics, 86(6), 929-942 (2010).
25. Lövkvist, C. et al. DNA methylation in human epigenomes depends on local topology of CpG sites. Nucleic acids research, 44(11) 5123-5132 (2016).
26. Nishiyama, A., & Nakanishi, M. Navigating the DNA methylation landscape of cancer. Trends in Genetics, 37(11), 1012-1027 (2021).
27. Moore, L. D. et al. DNA Methylation and Its Basic Function. Neuropsychopharmacology, 38(1), 23-38 (2013).
28. Saxonov, S. et al. A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters. Proceedings of the National Academy of Sciences, 103(5), 1412-1417 (2006).
29. Irizarry, R. A. et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nature genetics, 41(2), 178-186 (2009).
30. Schulz, W. A. et al. Methylation of endogenous human retroelements in health and disease. DNA Methylation: Development, Genetic Disease and Cancer, 211-250 (2006).
31. Krueger, F. et al. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 27(11), 1571-1572 (2011).
32. Karolchik, D. et al. The UCSC Table Browser data retrieval tool. Nucleic acids research, 32(suppl_1), D493-D496 (2004).
33. Wang, Y. et al. The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol 19, 151 (2018).
34. Quinlan, A. R et al. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841-842 (2010).
35. Du, P. et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC bioinformatics, 11(1), 1-9 (2010).
36. Akalin, A. et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome biology, 13(10), 1-9 (2012).
37. Love, M. I. et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), 1-21 (2014).
38. Cristiano, S. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature, 570(7761), 385-389 (2019).
39. Esfahani, M. S. et al. Inferring gene expression from cell-free DNA fragmentation profiles. Nature biotechnology, 40(4), 585-597 (2022).
40. Mouliere, F. et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Science Translational Medicine, 10(466), eaat4921 (2018).
41. Shen, S. Y. et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature, 563(7732), 579-583 (2018).
42. Cisneros-Villanueva, M. et al. Cell-free DNA analysis in current cancer clinical trials: a review. British Journal of Cancer, 126(3), 391-400 (2022).
43. Toyota, M. et al. CpG island methylator phenotype in colorectal cancer. Proceedings of the National Academy of Sciences (1999).
SURVEY OF THE ACCESS AND DISTRIBUTION OF GREEN SPACES IN THE GREATER CHICAGO AREA by Olivia Cheng et al.
1. University of Chicago Environmental Research Group.
2. “Green Streets and Community Open Space.” United States Environmental Protection Agency, 3 Apr. 2023, www.epa.gov/G3/greenstreets-and-community-openspace.
3. “Heat Island Compendium.” United States Environmental Protection Agency, 8 Mar. 2023, www.epa.gov/heatislands/heat-island-compendium.
4. Ibid., “Chapter 2: Trees and Vegetation” https://www.epa.gov/sites/default/files/2017-05/documents/reducing_urban_heat_islands_ch_2.pdf.
5. Twohig-Bennett, Caoimhe, and Andy Jones. “The Health Benefits of the Great Outdoors: A Systematic Review and Meta-Analysis of Greenspace Exposure and Health Outcomes.” Environmental Research, vol. 166, Elsevier BV, Oct. 2018, pp. 628–637. Crossref, doi:10.1016/j.envres.2018.06.030.
6. Dai, Dajun. “Racial/Ethnic and Socioeconomic Disparities in Urban Green Space Accessibility: Where to Intervene?” Landscape and Urban Planning, vol. 102, no. 4, Elsevier BV, Sept. 2011, pp. 234–244. Crossref, doi:10.1016/j.landurbplan.2011.05.002.
7. Hoffman, Jeremy S., et al. “The Effects of Historical Housing Pol
icies on Resident Exposure to Intra-Urban Heat: A Study of 108
US Urban Areas.” Climate, vol. 8, no. 1, MDPI AG, 13 Jan. 2020, p. 12.
Crossref, doi:10.3390/cli8010012.
8. “About Openlands.” Openlands, 28 Mar. 2023, https://openlands.org/about-openlands/.
9. “About CMAP.” CMAP, https://cmap.illinois.gov/about/.
THE ROLE OF CANDIDALYSIN IN REGULATING PEP TIDE YY 1-35 SECRETION IN RESPONSE TO VIRU LENT CANDIDA ALBICANS by Felicia Su
1. M. H. Mirhakkak, et al., Metabolic modeling predicts specific gut bacteria as key determinants for Candida albicans colonization levels. ISME J 15, 1257–1270 (2021).
2. C. L. Hager, M. A. Ghannoum, The mycobiome: Role in health and disease, and as a potential probiotic target in gastrointestinal disease. Dig Liver Dis 49, 1171–1176 (2017).
3. G. G. Kaplan, The global burden of IBD: from 2015 to 2025. Nat. Rev Gastroenterol Hepatol 12, 720–727 (2015).
4. R. L. Batterham, et al., PYY modulation of cortical and hypothalamic brain areas predicts feeding behaviour in humans. Nature 450, 106–109 (2007).
5. M. M. Mahé, et al., Establishment of gastrointestinal epithelial organoids. Curr Protoc Mouse Biol 3, 217–240 (2013).
6. S. Yu, et al., Paneth cell-derived lysozyme defines the composition of mucolytic microbiota and the inflammatory tone of the intestine. Immunity 53, 398-416.e8 (2020).
7. G.-W. He, et al., Optimized human intestinal organoid model reveals interleukin-22-dependency of paneth cell formation. Cell Stem Cell 29, 1333-1345.e6 (2022).
8. A.-C. Villani, et al., Common variants in the NLRP3 region contribute to Crohn’s disease susceptibility. Nat Genet 41, 71–76 (2009).
9. I. Schoultz, et al., Combined polymorphisms in genes encoding the inflammasome components NALP3 and CARD8 confer susceptibility to Crohn’s disease in Swedish men. Am J Gastroenterol 104, 1180–1188 (2009).
WHY IS MATH SO SLIPPERY? by Lucy Horowitz
1. Wolchover, N. “Neutrinos Lead to Unexpected Discovery in Basic Math.” Quanta Magazine https://www.quantamagazine.org/neutrinos-lead-to-unexpected-discovery-in-basic-math-20191113/ (2019).
2. Fong, B. et al. “Introducing the MathFoldr Project.” Topos Institute Blog https://topos.site/blog/2021/07/introducing-the-mathfoldr-project/ (2021).
3. de Paiva, V. “Networked Mathematics.” Topos Institute https://topos.institute/networked-mathematics (2021).
4. spacy.io https://spacy.io (2023).
5. Theory and Application of Categories http://www.tac.mta.ca/tac/ (2023).
6. CoNLL-U Format. Universal Dependencies https://universaldependencies.org/format.html (2022).
7. Universal Dependency Relations. Universal Dependencies https://universaldependencies.org/u/dep/index.html (2022).
8. Collard, J. et al. Extracting Mathematical Concepts from Text. WNUT https://doi.org/10.48550/arXiv.2208.13830 (2022).
9. SYM: symbol. Universal Dependencies https://universaldependencies.org/u/pos/SYM.html (2022).
10. dep: unspecified dependency. Universal Dependencies https://universaldependencies.org/u/dep/dep.html (2022).
THE ROLE OF CERAMIDES IN AGE-RELATED PATHOLOGIES: INSIGHTS FROM CELLULAR SENESCENCE AND IDIOPATHIC PULMONARY FIBROSIS by Hannah Heller
1. Dos Santos, G., et al. (2015). “Vimentin regulates activation of the NLRP3 inflammasome.” Nature Communications 6(1): 6574.
2. Ferrell, P. D., et al. (2022). “Pathologic Proteolytic Processing of
N-Cadherin as a Marker of Human Fibrotic Disease.” Cells 11(1).
3. Flor, A. C. and S. J. Kron (2016). “Lipid-derived reactive aldehydes link oxidative stress to cell senescence.” Cell Death & Disease 7(9): e2366.
4. Jensen, J. M., et al. (2005). “Acid and neutral sphingomyelinase,
ceramide synthase, and acid ceramidase activities in cutaneous aging.” Exp Dermatol 14(8): 609-618.
5. Jia, W., et al. (2021). “Trajectory modeling of endothelial-to-mesenchymal transition reveals galectin-3 as a mediator in pulmonary fibrosis.” Cell Death Dis 12(4): 327.
6. Wynn, T. A. (2011). “Integrating mechanisms of pulmonary fibro
sis.” J Exp Med 208(7): 1339-1350.
7. Yamamura, T. and T. Tezuka (1990). “Change in sphingomyelin
ase activity in human epidermis during aging.” J Dermatol Sci 1(2): 79-83.
THE PIERIS CLIMATE ACCORDS: HOW CLIMATE CHANGE IMPACTS THE PIERIS BUTTERFLY--ARABIDOPSIS HOST PLANT RELATIONSHIP by Annika Munson
1. Kronforst Lab, University of Chicago
2. IPCC. Global Warming of 1.5°C: An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty.
IPCC Report pp. 3-24, doi:10.1017/9781009157940.001. (2018).
3. Lindsey, R. Climate Change: Atmospheric Carbon Dioxide. NOAA Climate.gov. (2023).
4. Sharma, H. C. Climate Change Effects on Insects: Implications for Crop Protection and Food Security. Journal of Crop Improvement 28, 229–259. (2014).
5. Logan, J. A. et al. Assessing the impacts of global warming on
forest pest dynamics. Frontiers in Ecology and the Environment 1, 130–137. (2003).
6. Cipollini, D. Stretching the Limits of Plasticity: Can a Plant Defend against Both Competitors and Herbivores? Ecology 85, 28–37 (2004).
7. Nallu, S. et al. The molecular genetic basis of herbivory between butterflies and their host plants. Nat Ecol Evol 2, 1418–1427. (2018).
8. Wellcome Sanger Institute. Pieris rapae genome assembly ilPieRapa1.1. NCBI https://www.ncbi.nlm.nih.gov/data-hub/assembly/GCF_905147795.1/ (2021).
9. Berardini, T. Z. Functional annotation of the Arabidopsis genome using controlled vocabularies. Plant Physiol. 135(2):1-11. (2004).
10. McDermaid, A. et al. Interpretation of differential gene expression results of RNA-seq data: review and integration. Brief Bioinform 20, 2044–2054. (2018).
11. Zou, X. et al. Glutathione S-transferase SlGSTE1 in Spodoptera litura may be associated with feeding adaptation of host plants. Insect Biochemistry and Molecular Biology 70, 32–43. (2016).
PHYSICS WITH STRINGS: AN INQUIRY WITH PROFESSOR JEFFREY A. HARVEY by Swega S.S
UTILIZING COMPUTER ADAPTIVE TESTING (CAT) SURVEYS IN EMERGENCY ROOM SETTINGS FOR MENTAL HEALTH SCREENING: A PRELIMINARY RE VIEW OF PURPOSE AND METHODS by Mohammed I. Lone and David G. Beiser
1. Bains N, Abdijadid S. Major Depressive Disorder. [Updated 2023 Apr 10]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK559078/.
2. Munir S, Takov V. Generalized Anxiety Disorder. [Updated 2022 Oct 17]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK441870/ .
3. Armbrecht, E., Shah, R., Poorman, G. W., Luo, L., Stephens, J. M., Li, B., Pappadopulos, E., Haider, S., & McIntyre, R. S. (2021). Economic and Humanistic Burden Associated with Depression and Anxiety Among Adults with Non-Communicable Chronic Diseases (NCCDs) in the United States. Journal of multidisciplinary healthcare, 14, 887–896. https://doi.org/10.2147/JMDH.S280200.
4. Vermani, M., Marcus, M., & Katzman, M. A. (2011). Rates of detection of mood and anxiety disorders in primary care: a descriptive, cross-sectional study. The primary care companion for CNS disorders, 13(2), PCC.10m01013. https://doi.org/10.4088/PCC.10m01013.
5. Musey PI, Patel R, Fry C, Jimenez G, Koene R, Kline JA. Anxiety Associated With Increased Risk for Emergency Department Recidivism in Patients With Low-Risk Chest Pain. Am J Cardiol. 2018;122(7):1133-1141. doi:10.1016/j.amjcard.2018.06.044.
6. Beiser DG, Ward CE, Vu M, Laiteerapong N, Gibbons RD. Depression in Emergency Department Patients and Association With Health Care Utilization. Academic Emergency Medicine. 2019;26(8). doi:10.1111/acem.13726.
7. Achtyes ED, Halstead S, Smart LA, et al. Validation of computerized adaptive testing in an outpatient non academic setting: The VOCATIONS trial. Psychiatric Services. 2015;66(10):1091-1096. doi:10.1176/appi.ps.201400390.
8. Gibbons RD, Weiss DJ, Frank E, Kupfer D. Computerized Adaptive Diagnosis and Testing of Mental Health Disorders. Annual Review of Clinical Psychology. 2016;12(1):annurev-clinpsy-021815-093634. doi:10.1146/annurev-clinpsy-021815-093634.
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