Ultra-sensitive, tumor-informed ctDNA profiling in pembrolizumab-treated esophagogastric cancer patients predicts clinical responses.

To explore whether ultra-sensitive circulating tumor DNA (ctDNA) profiling enables early prediction of treatment response and early detection of disease progression, we applied NeXT Personal, an ultra-sensitive bespoke tumor-informed liquid biopsy platform, to profile tumor samples from the KeyLargo study, a phase II trial in which metastatic esophagogastric cancer (mEGC) patients received capecitabine, oxaliplatin, and pembrolizumab. All 25 patients evaluated were ctDNA-positive at baseline. Minimal residual disease (MRD) events varied from 406,067 down to 1.

The application of deep learning in early enamel demineralization detection.

Objective: The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces.

Methods: A retrospective analysis was conducted with 208 patients aged 14 to 44. A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN).

Graves' disease diagnosed nearly six months after microwave ablation of benign thyroid nodules: a case report.

Background: Microwave ablation is a new, minimally invasive technique for the treatment of thyroid nodules. Hyperthyroidism due to destructive thyroiditis is a known risk of microwave ablation, though it occurs in only a minority of cases. We report a rare case of a patient diagnosed with Graves' disease nearly six months after undergoing microwave ablation of a thyroid nodule.

Identifying Predictors of Spatiotemporal Variations in Residential Radon Concentrations across North Carolina Using Machine Learning Analytics.

Radon is a naturally occurring radioactive gas derived from the decay of uranium in the Earth's crust. Radon exposure is the leading cause of lung cancer among non-smokers in the US. Radon infiltrates homes through soil and building foundations.

PPI-CoAttNet: A Web Server for Protein-Protein Interaction Tasks Using a Coattention Model.

Predicting protein-protein interactions (PPIs) is crucial for advancing drug discovery. Despite the proposal of numerous advanced computational methods, these approaches often suffer from poor usability for biologists and lack generalization. In this study, we designed a deep learning model based on a coattention mechanism that was capable of both PPI and site prediction and used this model as the foundation for PPI-CoAttNet, a user-friendly, multifunctional web server for PPI prediction.