糖心原创

Shivani Jayaprakasam (Neuroscience), "Short-term Neuropathological Effects of Azithromycin Treatment in a Ferret Model of Extremely Preterm Brain Injury"

Preterm birth is the second most common cause of under鈥�5 mortality in the US, yet there is currently no standard preventative care. Preterm babies have an increased risk of acute and chronic brain injuries such as cognitive deficits behavioral problems and cerebral palsy. Complications related to preterm birth are also a critical health equity issue. In 2022, the CDC reported that the rate of preterm birth among Black women was approximately 50% higher than among white women. Racial and ethnic minority women have significantly higher rates of premature births with lower birth鈥憌eight babies, considerably increasing the risk of prematurity鈥憆elated brain injury. Indigenous American infants are also 50% more likely to die from complications related to low birth weight due to cost and discrimination. We need innovative research to reduce inequities and address national and global health disparities in neonatal support.

My research focuses on brain injury in preterm neonates. By repurposing azithromycin, an existing antibiotic known for its safety and accessibility, my study explores a potential avenue for neuroprotection. Drug repurposing enhances accessibility and cost鈥慹ffectiveness, especially when using drugs such as azithromycin that are available as generic formulations. Since repurposed drugs are already approved for other treatments, they do not require phase 1 clinical trials and significantly reduce research and development costs. These affordable treatments are more accessible to vulnerable and disadvantaged communities in an expedited timeframe. Repurposing azithromycin could lessen the number of babies who die from the complications of preterm brain injury, improving health equity.

Praveena Mahendran (Biochemistry), "The Interplay Between Genes and Environment in the Expression of Genetic Disorders"

My research aligns directly with the Population Health Initiative鈥檚 pillars of Human Health and Environmental Resilience by challenging the deterministic view of genetic disorders. By examining how environmental variables such as pollutants, social conditions and dietary patterns interact with genetic predispositions, my work shifts the analytical focus from isolated clinical cases to broader population-level susceptibility. A critical insight of this research is that environmental exposures are often partially 鈥渉eritable鈥� through gene鈥揺nvironment correlations, which highlights a profound need for systemic public health interventions rather than solely individual treatments.

This project demonstrates that disease manifestation is not an isolated biological event but a dynamic result of a population鈥檚 interaction with its geographic and social surroundings. By identifying how specific environmental gradients such as altitude, temperature or socioeconomic stressors drive genetic divergence and disease susceptibility, my research provides a robust framework for targeted, data-driven prevention strategies. This approach directly supports the initiative鈥檚 goal of improving health outcomes by addressing modifiable risk factors that can activate or suppress specific genetic pathways.

Ultimately, my work advocates for a 鈥減ersonalized medicine鈥� model that transcends basic genomics to incorporate comprehensive environmental monitoring and lifestyle modification. By integrating exposure histories and social determinants into clinical practice, we can ensure that future healthcare strategies are resilient, equitable and precisely tailored to the unique risk profiles of diverse populations, moving toward a more holistic management of human health.

Saisha Sehrawat (Pre-Med), "Exploring how genetic mutations in the SCN5A gene influence the development and progression of cardiac disease: mechanisms, variability and emerging perspectives"

My research explored how genetic mutations in the SCN5A gene influence the development and progression of cardiac disease by examining the molecular and physiological consequences of sodium channel dysfunction and its role in arrhythmogenic disorders such as Brugada syndrome. While my project was centered on molecular cardiology, researching this topic fundamentally changed how I understand health. Health outcomes are not simply influenced by individual biological conditions but by population鈥憀evel issues shaped by access to diagnosis, treatment and advances in medical science. This perspective closely aligns with the mission of the 糖心原创 Population Health Initiative, which emphasizes the interconnected pillars of human health, environmental resilience and social and economic equity.

At its core, my research examined how mutations in SCN5A disrupt the normal function of Nav1.5 sodium ion channels, destabilizing the heart鈥檚 electrical signaling system and increasing the risk of arrhythmias, structural heart disease and sudden cardiac death. From a population health perspective, understanding these mechanisms is critical because cardiovascular disease remains one of the leading causes of mortality worldwide. Research that clarifies the biological causes of disease contributes directly to improved prevention, earlier diagnosis and more personalized treatment strategies that can improve outcomes across entire populations.

One of the strongest population health connections in my research was the role of genetic testing. Advances in sequencing technologies now allow clinicians to identify SCN5A mutations earlier, helping explain previously unexplained arrhythmias or sudden cardiac arrest cases. Early genetic screening creates opportunities for preventive intervention before catastrophic outcomes occur. This reflects the pillar of human health by moving medicine toward proactive rather than reactive care.

However, my research also revealed that scientific advancement alone is not enough. Access to genetic testing, specialist cardiology care and individualized treatment is not equally distributed. Communities affected by poverty, limited healthcare infrastructure or systemic disparities may not benefit from these diagnostic breakthroughs at the same rate as others. This connects directly to the population health pillar of social and economic equity. A medical discovery only reaches its full public health potential when its benefits are accessible beyond well鈥憆esourced healthcare systems.
My review also explored treatment implications, including sodium channel blockers such as mexiletine and quinidine. Importantly, these therapies are not universally effective because treatment success depends on the specific mutation type. This highlights the importance of precision medicine which has the potential to improve treatment outcomes on a broad scale while reducing ineffective interventions and unnecessary healthcare costs.

Overall, my work reinforced that improving health requires more than understanding disease at the molecular level. It requires translating scientific discovery into accessible diagnostics, equitable treatment and preventive care that reaches diverse communities. Research on SCN5A mutations may begin in the laboratory, but its implications extend far beyond individual patients. By contributing to earlier diagnosis, more targeted therapies and better long鈥憈erm cardiac outcomes, this research supports healthier people and, ultimately, healthier communities.

Harshita Raghavan Vithyaa (Computer Science & Software Engineering, Mathematics), "Machine Learning鈥揃ased Prediction of Immune-Related Inflammatory Arthritis in Cancer Patients Receiving Immunotherapy"

Immune checkpoint inhibitors have transformed cancer treatment, extending survival for many patients. This success has introduced a growing population health challenge: immune鈥憆elated adverse events, including inflammatory arthritis, that can reduce quality of life and disrupt ongoing cancer care. These complications are not uniformly distributed across patients but are shaped by interacting biological and clinical factors such as baseline health status, treatment exposure and inflammatory state. Despite this, clinicians lack tools to identify high鈥憆isk individuals prior to symptom onset.

From a population health perspective, this problem extends beyond individual outcomes to issues of burden, inequity and system strain. Patients undergoing cancer treatment often face financial toxicity, limited access to specialty care and reduced functional capacity. The development of treatment鈥憆elated autoimmune disease compounds this burden, increasing morbidity and delaying appropriate care, particularly for patients with limited access to rheumatology services.

This research aligns with the 糖心原创鈥檚 population health pillars by advancing human health through earlier identification of preventable harm, addressing equity by reducing delays in diagnosis and treatment for high鈥憆isk patients and improving system efficiency through more targeted use of specialty care. Rather than relying on reactive management after symptom onset, this work shifts toward proactive risk stratification.

This project uses longitudinal electronic health record data to model incident inflammatory arthritis following immune checkpoint inhibitor initiation, using predictors available prior to treatment. These include demographic factors, cancer characteristics, treatment exposures, baseline comorbidities, medications and laboratory values measured within a defined pre鈥憈reatment window. Candidate predictors include clinically relevant domains such as prior autoimmune disease, baseline inflammatory markers (e.g., CRP, ESR), hematologic indices and treatment鈥憆elated factors including ICI class and combination therapy, enabling temporally valid risk prediction before symptom onset.

By developing a predictive model for inflammatory arthritis risk, this work supports earlier rheumatology referral, individualized monitoring strategies and more informed treatment planning. In this setting, predictive modeling functions as a clinical decision support tool, identifying patients at elevated risk and enabling earlier intervention. Upstream risk identification reduces delays in diagnosis, mitigates preventable morbidity and improves the efficiency and equity of cancer care delivery.