Project description:Endometriosis, affecting over 10% of women, presents treatment and diagnostic challenges. To address these issues, we generated the biggest single-cell atlas of endometrial tissue to date, comprising 466,371 cells from 35 endometriosis and 25 non-endometriosis patients without exogenous hormonal treatment. Detailed analysis reveals significant gene expression changes and altered receptor-ligand interactions present already in the endometrium of endometriosis patients, including increased inflammation, adhesion, proliferation, cell survival, and angiogenesis in various cell types. These alterations may enhance endometriosis lesion formation and offer novel therapeutic targets. Using ScaiVision neural networks, we developed accurate models predicting endometriosis of varying disease severity, including a minimal 11-gene signature-based model. In conclusion, our findings illuminate numerous pathway and ligand-receptor changes in endometriosis endometrium, offering insights into pathophysiology, targets for novel treatments and accurate diagnostic models for enhanced outcomes in endometriosis management.

2026-03-20 | GSE266265 | GEO

Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled Digital Stethoscope.

Project description: Not available

| S-EPMC9708035 | biostudies-literature

Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model.

Project description: Not available

| S-EPMC6689506 | biostudies-literature

Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric.

Project description: Not available

| S-EPMC12634809 | biostudies-literature

Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.

Project description: Not available