Abstract
Introduction:
My technical capstone project and STS research are closely connected through a shared focus on improving understanding and treatment of endometriosis, but they approach the problem in different ways. My technical project addresses a biomedical gap by developing an improved ex vivo model that preserves patient-specific tissue characteristics, while my STS thesis examines how historical gender bias has shaped the way endometriosis symptoms are classified and understood. Together, these projects highlight both the scientific and social limitations that have slowed progress in endometriosis research and treatment. This relationship is important because my technical work aims to improve how endometriosis is modeled in lab settings, while my STS work explains why endometriosis has been so difficult to define, diagnose, and classify in the first place.
Technical Project:
My capstone project focused on optimizing an ex vivo model of endometriosis using PEG-based hydrogels to better preserve tissue structure, cellularity, and viability over time. The goal was to create a more physiologically relevant platform for studying the disease, including therapeutic applications. The team hypothesized that embedding endometriotic lesions in hydrogels would improve cell retention and maintain native tissue characteristics. Across the project, we examined gelation time, tissue retention, and viability across various hydrogel compositions. Results showed that hydrogel-embedded tissues maintained structure and cellularity longer than media only controls, with no significant differences between degradable and non-degradable crosslinkers. Additionally, cryopreserved tissue samples performed similarly to fresh samples, suggesting broader applicability of the model. Overall, the project demonstrates that hydrogel-based systems are a promising platform for studying endometriosis in a more clinically relevant and personalized way. This work is important because current endometriosis treatments remain limited, and better ex vivo models may help researchers test therapies in a way that reflects the specificity of individual patients more accurately.
STS Thesis:
My STS thesis argues that gender bias has significantly influenced how endometriosis symptoms have been classified, contributing to inconsistencies in diagnosis and treatment. Using historical analysis and the framework from Bowker and Star’s Sorting Things Out, I examine how various classification systems such as the ICD, rASRM, and ENZIAN evolved under conditions of limited knowledge and social bias. My analysis shows that symptom classification often prioritizes visible surgical findings over patient-reported experiences, partly because of a longstanding tendency to dismiss or psychologize women’s pain. Additionally, the reliance on invasive diagnostic methods and the lack of reliable biomarkers have reinforced inconsistent and incomplete classification systems. I discuss how endometriosis has not only been under-researched but also sorted into categories shaped by institutional and gendered assumptions about what counts as legitimate disease. Overall, my paper demonstrates that the challenges in understanding endometriosis are not purely scientific, but are also shaped by social and institutional factors that have historically marginalized women’s health.
Conclusion:
Working on both of these projects simultaneously has allowed me to gain a better understanding of endometriosis than either project could have provided alone. By physically working with endometriosis tissues for my technical project, I understand how variable the samples are, and why better models are needed to study therapies and disease mechanisms. Working on this project pushed me to question why the disease has been so hard to study in the first place, which led to focusing my STS research on how historical bias, diagnostic dependence on surgery, and skepticism toward women’s symptoms have shaped the available knowledge. At the same time, the STS paper allowed me to frame the technical project as part of a larger systemic issue rather than an isolated engineering problem. This combination reinforced the idea that improving women’s health outcomes requires both better technologies and critical examination of the social structures that shape scientific knowledge. Doing both of these projects simultaneously helped me see that technical limitations are not separate from social ones.
