Empowering High-Throughput High-Content Analysis of Microphysiological Models: Open-Source Software for Automated Image Analysis of Microvessel Formation and Cell Invasion.

Purpose: The primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in microphysiological models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of endothelial tube formation and cell invasion in vitro, facilitating the rapid assessment of drug sensitivity.

Methods: Our approach involved annotating over 1000 2 mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics.

Engineering high throughput screening platforms of cervical cancer.

Cervical cancer is the second leading cause of cancer-related death in women under 40 and is one of the few cancers to have an increased incidence rate and decreased survival rate over the last 10 years. One in five patients will have recurrent and/or distant metastatic disease and these patients face a 5-year survival rate of less than 17%. Thus, there is a pressing need to develop new anticancer therapeutics for this underserved patient population.

Biomaterial strategies to replicate gynecological tissue.

Women's health is an important and understudied area of research. The current standard of care for many gynecological diseases such as cancer or autoimmune-linked disorders such as endometriosis is surgery; however, the underlying mechanisms of action of many gynecological diseases are poorly understood. The field of tissue engineering has the potential to transform the field of women's health by developing in vitro models of healthy and diseased tissue that could be used to identify novel treatment strategies as well as gain a better understanding of complex signaling dynamics.