Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study.

PURPOSE: Neoadjuvant chemoradiotherapy has been the standard practice for patients with locally advanced rectal cancer. However, the treatment response varies greatly among individuals, how to select the optimal candidates for neoadjuvant chemoradiotherapy is crucial. This study aimed to develop an endoscopic image-based deep learning model for predicting the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. METHODS: In this multicenter observational study, pre-treatment endoscopic images of patients from two Chinese medical centers were retrospectively obtained and a deep learning-based tumor regression model was constructed. Treatment response was evaluated based on the tumor regression grade and was defined as good response and non-good response. The prediction performance of the deep learning model was evaluated in the internal and external test sets. The main outcome was the accuracy of the treatment prediction model, measured by the AUC and accuracy. RESULTS: This deep learning model achieved favorable prediction performance. In the internal test set, the AUC and accuracy were 0.867 (95% CI: 0.847-0.941) and 0.836 (95% CI: 0.818-0.896), respectively. The prediction performance was fully validated in the external test set, and the model had an AUC of 0.758 (95% CI: 0.724-0.834) and an accuracy of 0.807 (95% CI: 0.774-0.843). CONCLUSION: The deep learning model based on endoscopic images demonstrated exceptional predictive power for neoadjuvant treatment response, highlighting its potential for guiding personalized therapy.

Mechanism of Mutation-Induced Effects on the Catalytic Function of TEV Protease: A Molecular Dynamics Study.

Tobacco etch virus protease (TEVp) is wildly exploited for various biotechnological applications. These applications take advantage of TEVp's ability to cleave specific substrate sequences to study protein function and interactions. A major limitation of this enzyme is its relatively slow catalytic rate. In this study, MD simulations were conducted on TEV enzymes and known highly active mutants (eTEV and uTEV3) to explore the relationship between mutation, conformation, and catalytic function. The results suggest that mutations distant from the active site can influence the substrate-binding pocket through interaction networks. MD analysis of eTEV demonstrates that, by stabilizing the orientation of the substrate at the catalytic site, mutations that appropriately enlarge the substrate-binding pocket will be beneficial for Kcat, enhancing the catalytic efficiency of the enzyme. On the contrary, mutations in uTEV3 reduced the flexibility of the active pocket and increased the hydrogen bonding between the substrate and enzyme, resulting in higher affinity. At the same time, the MD simulation demonstrates that mutations outside of the active site residues could affect the dynamic movement of the binding pocket by altering residue networks and communication pathways, thereby having a profound impact on reactivity. These findings not only provide a molecular mechanistic explanation for the excellent mutants, but also serve as a guiding framework for rational computational design.