Life Sciences Commons^™

Therapeutic Potential Of Snake Venom: Toxin Distribution And Opportunities In Deep Learning For Novel Drug Discovery, Anas Bedraoui, Montamas Suntravat, Salim El Mejjad, Salwa Enezari, Naoual Oukkache, Elda E. Sanchez, Jacob Galan, Rachid El Fatimy, Tariq Daouda

School of Medicine Publications and Presentations

Snake venom is a rich source of bioactive molecules that hold great promise for therapeutic applications. These molecules can be broadly classified into enzymes and non-enzymes, each showcasing unique medicinal properties. Noteworthy compounds such as Bradykinin Potentiating Peptides (BPP) and Three-Finger Toxins (3FTx) are showing therapeutic potential in areas like cardiovascular diseases (CVDs) and pain-relief. Meanwhile, components like snake venom metalloproteinases (SVMP), L-amino acid oxidases (LAAO), and Phospholipase A2s (PLA2) are paving new ways in oncology treatments. The full medicinal scope of these toxins is still emerging. In this review, we discuss drugs derived from snake venoms that address …

Classification Of Colorectal Cancer Using Resnet And Efficientnet Models, Abhishek Ranjan, Priyanshu Srivastva, B Prabadevi, R Sivakumar, Rahul Soangra, Shamala K. Subramaniam

Physical Therapy Faculty Articles and Research

Introduction:

Cancer is one of the most prevalent diseases from children to elderly adults. This will be deadly if not detected at an earlier stage of the cancerous cell formation, thereby increasing the mortality rate. One such cancer is colorectal cancer, caused due to abnormal growth in the rectum or colon. Early screening of colorectal cancer helps to identify these abnormal growth and can exterminate them before they turn into cancerous cells.

Aim:

Therefore, this study aims to develop a robust and efficient classification system for colorectal cancer through Convolutional Neural Networks (CNNs) on histological images.

Methods:

Despite challenges in …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …