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TAMARA BERNER - Comparison of Carotid Artery Stenting and Carotid Endarterectomy for Short-Term and Long-Term Outcomes in MINIMIZING STROKE RISK AND PROCEDURAL COMPLICATIONS
Carotid artery disease is a common complication of hyperlipidemia and plaques, impacting up to 5% of people in the United States [1] The disease is associated with significant clinical consequences, such as strokes and myocardial infarctions. Carotid artery stenting (CAS) and carotid endarterectomy (CEA) are procedures to address carotid artery disease and reduce risk. The following study identifies available literature addressing complication rates after CAS and CEA to assist with comparing safety and efficacy of both procedures. Across multiple studies, CAS consistently showed a higher immediate risk of stroke compared to CEA. This increased risk is significant and highlights the importance of careful patient selection and procedural expertise when opting for CAS over CEA. CAS is associated with lower rates of myocardial infarction (MI), which occurs when blood flow to a part of the heart is blocked, typically by a blood clot, leading to damage or death of heart muscle tissue, compared to CEA [2]. This finding suggests that while CAS may carry a higher immediate stroke risk, it may be less likely to cause myocardial infarction, a critical consideration for patients with cardiac comorbidities. The analysis identified key trends across multiple studies, demonstrating that CAS is consistently associated with a higher immediate risk of stroke compared to CEA, particularly within the first 30 days post-procedure. Across the studies reviewed, CAS showed an increased incidence of early stroke, whereas CEA presented lower short-term neurological risks [3]. This comparative evaluation highlights the procedural differences in stroke risk and the need for careful patient selection when determining the most appropriate intervention. Understanding these short-term risks is essential for guiding clinical decision-making and identifying areas for further research into long-term outcomes.
Tamara Berner is a junior in high school with a strong interest in medicine, especially cardiology and neurology, and hopes to pursue a future career in surgery. Her passion for becoming a doctor grew through advanced science classes such as biology, chemistry, and physics, which deepened her curiosity about how different systems work synergistically.
OYUJIN Damdinsuren and Ashley ZHANG - Modeling Tularemia: Strategies for Effective Treatment in Bioterrorism ScenarioS
Francisella tularensis is the causative agent of tularemia and is considered a likely bioterrorism agent by the Centers for Disease Control and Prevention (CDC) in part because of its low infectious dose, high associated mortality, and potential for easy dissemination. Tularemia can lead to fatal pneumonia in humans but is treatable with antibiotics. While intravenous (IV) antibiotics are more effective, they require hospital care, unlike oral antibiotics, which are easier to distribute. To understand the outcomes of an intentional F. tularensis aerosol release, we developed a short-term, SIR-based ordinary differential equations (ODE) model to evaluate the effectiveness of IV and oral antibiotic treatment. Numerical simulations show that oral treatment alone could mitigate deaths by 56%, while IV antibiotics, limited by hospital beds, could only reduce deaths by 11%. Thus, in the event of an aerosolized tularemia release, large-scale oral antibiotics treatment should be prioritized over IV treatment.
Oyujin Damdinsuren is a senior at Centennial High School (MD) whose interests in biology, public health, and data analysis direct her research on improving healthcare systems to better serve vulnerable populations. She will join Johns Hopkins University's class of 2029 in the fall and, in the meantime, enjoys choral singing, science fiction novels, and baking cakes.
Ashley Zhang is a junior at Montgomery Blair High School in Maryland with a passion for math, computer science, and physics, and their interdisciplinary applications. Outside of academics, she practices wushu, plays chess, and enjoys unwinding with dramas, manga, and crossword puzzles.
NEIL DIXIT - A Study on the Compatibility of Gerrymandering Metrics
Gerrymandering has been a very controversial topic in politics and law in the past decade. There is no single universal metric to measure partisan gerrymandering. The four most widely used metrics, efficiency gap (EG), declination, partisan bias, and mean–median difference (MMD), each have their own characteristics. A strong legal definition of a gerrymander would likely encompass several of them. In order to do this, the metrics used must be compatible. This paper examines how they relate to each other in the context of the 2020 redistricting maps in the USA by running Pearson correlation and linear regression tests. Results showed that partisan bias and declination and partisan bias and MMD were not compatible at all (p = 0.656, 0.198), while the rest were somewhat compatible (p < 0.001). Of these, declination and MMD, and declination and EG were most compatible (R2 = 0.37, 0.48). Given the size of the dataset, the results encompass a small portion of scenarios, so running simulations over a wide range of scenarios for the maps would be an excellent way to build upon this work.
MANAAL TAFSEER - Evaluating the Performance of Convolutional Neural Networks in Predicting Hurricane Intensity from Satellite Imagery
Hurricanes are among the most devastating natural disasters, causing significant loss of life and property annually. Accurate classification of hurricane intensity is essential for effective disaster preparedness and response. While traditional meteorological methods are effective, integrating advanced machine learning techniques can enhance prediction accuracy. This study examines the use of Convolutional Neural Networks (CNNs) to classify hurricanes using infrared (IRWIN) satellite imagery from the National Oceanic and Atmospheric Administration (NOAA) Hurricane Satellite (HURSAT) database. Leveraging image-based data from 2016 hurricanes, the CNN model achieves 85% accuracy in categorizing hurricane intensity, demonstrating the potential of CNNs to improve hurricane classification and serve as a complementary tool to existing meteorological models.
Manaal Tafseer is passionate about science, technology, and sustainability, with a focus on AI and engineering solutions for global challenges. She has conducted research on extreme weather prediction and smart irrigation, presenting her work at COP28 and other global platforms.
Jennifer choi - Enhancing Cancer Treatment Efficacy with Magnetic Chitosan Nanoparticles: A Novel Approach to Targeted Drug Delivery of Doxorubicin
The study develops a novel approach to targeted cancer therapy using magnetic chitosan nanoparticles (MCNPs) complexed with doxorubicin (DOX). Chitosan nanoparticles offer biodegradability, surface modifiability, and a unique ability to be magnetically directed, making them ideal for selective drug delivery. By applying an external magnetic field, the MCNP-DOX complex was selectively guided to melanoma cancer cells (SK-MEL-28) in a dynamic flow system that simulated blood circulation, which significantly reduced cancer cell confluency (from 50.82% to 6.77%) with minimal impact on normal skin cells (Detroit551). These findings suggest that MCNP-DOX complexes enhance the therapeutic effect of DOX, targeting cancer cells while sparing healthy tissues. Further research is needed to assess long-term effects and potential clinical applications, but this study marks an important step toward more effective and controlled cancer therapies.
Jennifer Choi is a sophomore at Chadwick International in South Korea. Passionate about chemistry, biology, and mathematics, she is particularly interested in nanoparticle delivery for targeted drug delivery.
Gopalaniket Tadinada - The Development of a Low-Cost and Multimodal Surgical Assistance System for the Detection and Treatment of Gliomas
Gliomas are one of the deadliest forms of cancer. Traditional diagnostic and treatment techniques rely on outdated imaging that can't detect and define the borders of the tumor early enough to prevent recurrence. This project aims to create a low-cost, comprehensive surgical assistance system using deep learning to reduce the recurrence of brain tumors, with a focus on gliomas. CereVis presents 1 model for detection, 3 for pre-operative planning, 1 for surgery, and 1 for post-operative evaluation. Using a Support Vector Machine, CereVis uses highly correlated RNAs identified through RNA-Seq data found in an ELISA to detect potential GBM. The Tumor Classification Model distinguishes pituitary tumors, meningiomas, and gliomas. The Glioma Classification Model categorizes gliomas’ grades and types between oligodendrogliomas, astrocytomas, and glioblastomas 1-4. CereVis's core is the 3D Segmentation Model, utilizing a novel framework to delineate tumor borders. An intra-operative system of CereVis employs three scalpel detection models to provide real-time 3D tracking of the scalpel. Lastly, CereVis includes a system that predicts recurrence using postoperative MRI scans via voxel-wise radiomic features. While there are some limitations regarding rare tumor subtypes, CereVis has the potential to revolutionize brain tumor removal by properly planning surgeries, assisting during operations, and providing novel postoperative insights, all at a much lower cost than current methods.
Gopalaniket “Aniket” Tadinada is a sophomore from Kentucky, and he has been interested in medicine research since middle school, focusing on brain tumor research in the summer between his 8th and 9th grade. Outside of this research, his other scientific interests include neurological areas such as radiology & Alzheimer’s disease, and he hopes to become a neurologist one day.
GAYOUNG SHIN - Analysis of the effects of high temperature on rapid-cycling Brassica rapa’s germination, growth, and gene expression
This study aims to understand how plants in the Brassica genus, which includes many diverse crops integral to the human diet, are affected by exposure to high temperatures due to global warming. Using the Rapid-Cycling Brassica rapa (RCBr), the effects of high temperature on germination, vegetative growth, flower and fruit production, leaf chlorophyll concentration, and differential gene expression through RNA transcriptomes were investigated. After planting the seeds and observing them for 10 days, only 24% of those planted in 35℃ germinated, which is significantly lower than 84% germination rate observed in the 25℃ control group. In 11 individuals grown at 33°C, both shoot and leaf length increased by only about 50% compared to the 25°C control group. Furthermore, when observing reproductive growth, the number of flowers and bean pods in the experimental group decreased by 63% and 24%, respectively, compared to the control group. The experimental concentrations of chlorophyll a and b examined through absorbance analysis were 80% of the control. KEGG Enrichment analysis was performed on the RNA transcriptome, and 15 of the top 20 p-values in the enrichment map test were related to metabolism, including photosynthesis, glycolysis, and biosynthesis. In conclusion, the high-temperature environment inhibited the growth and photosynthesis of RCBr, directly affected the chlorophyll concentration, and delayed its life cycle. These findings are consistent with RNA transcriptome analysis, which indicates that high temperature has a multifaceted effect on the metabolism. Therefore, the results will provide a basis for research on the cultivation of Brassica species under high temperatures, with further analysis of specific metabolic pathways providing more sophisticated information.
Gayoung Shin is a passionate junior student with strong interests in zoology, forensic science, conservation biology, and environmental issues. With a dream of pursuing a career in the biomedical field, they aspire to save and improve lives through science.
Emma chung - Exploring Various Biomarkers of Parkinson’s Disease as Potential Early Diagnostic Tools
Parkinson’s Disease (PD) is a neurodegenerative disease that results in impaired motor control and cognitive functions. PD also increases patients’ susceptibility to life-threatening infections such as pneumonia and bronchitis. Despite the debilitating effect of the disease on patients, there are currently no disease-modifying therapeutics available to treat the root cause of the pathogenesis, which is the degeneration of specific neuron populations in the midbrain. Instead, patients are forced to resort to symptomatic therapies. While symptomatic therapies can greatly alleviate the discomfort caused by motor impairments, they are ineffective in altering the progression of the disease. Currently, there are novel potentially disease-modifying treatments in the therapeutic pipeline; however, to maximize their efficacy, earlier diagnoses of PD are critical. The inability to treat PD until its later stages renders most treatments ineffective in altering disease progression. This paper explores the potential early diagnostic biomarkers of PD such as alpha-synuclein alterations, hyposmia, REM sleep behavior disorder, constipation, oxidative stress markers, and more.
Emma Chung is a neuroscience nerd, a passionate dancer, a great pianist, a strong leader, and a dedicated volunteer at Project Cure, an organization that has helped many underdeveloped countries receive much-needed medical supplies by recycling excess medical equipment in the US. Her long-time scientific curiosity about the human brain has led her to explore topics that include neurodegenerative diseases, olfactory dysfunction, brain cancer, inflammation, immunology, and stem cells.
Dhruv veda - Phytoncides: A Possible Epigenetic Treatment for Mental and Physical Pain
The opioid epidemic and mental health crises are major challenges in modern medicine. Opioid use triggers a cyclical effect, worsening depression, leading to more opioid use and anxiety while lowering pain tolerance. Despite their addictive nature, opioids remain the most commonly prescribed treatment for severe pain, leading to increased addiction and its consequences. Phytoncides, antimicrobial compounds from plants like eucalyptus, pine, and citrus, reduce stress, anxiety, and pain without addictive properties, making them potential alternatives or supplements to opioids. Research also shows phytoncides lower blood pressure, heart rate, and cortisol levels while promoting relaxation and immune cell proliferation, but their mechanism of action is unknown. This study explores the hypothesis that phytoncides modulate pain and anxiety through epigenetic regulation of the GABA pathway (which directly regulates both pain and anxiety perception), making them promising avenues for pain treatment. To investigate this, N2A cells were treated with 5 common varieties of phytoncides (Alpha-Pinene, Beta-Pinene, Limonene, Linalool, and Eucalyptol) at a concentration of 1% reagent in medium and subsequently incubated for 4 or 24 hours. Total RNA was then extracted and converted into cDNA via reverse transcription. Then, qRT-PCR was used to determine the transcription rates of GABA receptor subtypes Alpha, Beta, and Gamma. The ddCT value was then calculated using GAPDH as a loading control to measure relative expression The study found Alpha-Pinene and Eucalyptol caused a statistically significant (p<0.05) change in expression of GABA alpha and gamma receptors, proving epigenetic influence on the target genes (all other varieties of phytoncides showed a nonsignificant upregulation of receptor expression). This pathway is upstream of those that lower sensitivity to pain and anxiety. Thus, these results substantiate previous hypotheses that they have potential as pain regulating medications which can act as an alternative or supplementary treatment to opioids. This study provides valuable insights into the modulation of GABA receptor subtypes (including magnitude and duration) by phytoncides and identifies key areas for future research to optimize dosing and elucidate their mechanisms of action.
Dhruv Veda is a junior with a passion for research, spanning neuroscience and machine learning in medicine. He founded Forest Harmony, a nonprofit that collaborates with veterans and local communities to harness the restorative power of nature—promoting mental clarity, emotional resilience, and well-being.
Ayaan seshadri - the promise of “Digital healthcare for all”: impact of age and english proficiency on adoption of ehealth apps in a vulnerable population of hospitalized patients
Smartphone-based digital health applications (apps) are touted as the future of medicine. Today, across all major U.S. hospitals, patients are given access to a customized health app to connect them with their entire care team from the comfort of their homes. Yet surprisingly, US government data shows that 21% of American adults never access any apps. Our interview-based public health research seeks to assess the overall receptivity, and the impact of age and English proficiency (as independent variables) on the adoption of the MyMountSinai (MMS) app in hospitalized inpatients at the Mt. Sinai Morningside Hospital in New York City. A promising 56% initial digital health education success rate, but a low 21.4% final app download rate was observed. Given the acuteness of their medical condition, app adoption in this hospitalized population is a complex, multifactorial decision. Yet our data shows that age ≥65 years is a reliable negative determinant of receptivity to health-app use (Pearson’s coefficient -0.406) and single-handedly contributed to ~17% of the decision on whether to use the app or not (r-squared analysis 0.166; 16.6%). Surprisingly, English proficiency played no part in app adoption or rejection rates. Given the frailty of the subjects in question, the true success of the research is in its ability to collect hundreds of patients’ verbatims which shed light on the real-world obstacles to digitalization that seniors face. Through our work we urge doctors and hospitals globally not to phase out the traditional “paper-and-pencil record-keeping just yet; overzealous digitalization in medicine disservices the seniors who need medical care the most.
Ayaan Seshadri is a sixteen-year-old high school junior from New York City who aspires to become a public health scientist (epidemiologist) studying genetic, medical and social determinants of diseases that disproportionately affect communities lacking representation. Ayaan is a staunch advocate for the medical causes on both ends of the age bell curve as he seeks to champion the causes of children and teens on one side, and geriatrics on the other.