University of Miami

scCont breaks cell-state transitions in time-course single-cell RNA data into interpretable functional transition units: instead of explicit time labels or clustering priors, it selects positive pairs with a k-nearest-neighbor graph in expression space for contrastive learning, then uses Shapley attribution to map latent dimensions back to gene programs. Across four SERGIO simulated datasets and 13 epithelial-to-mesenchymal transition time-course datasets, its gene modules carry statistical filtering and GO enrichment annotation; on simulated data at k=10, background genes make up 7.0% of the 100 highest-attributed genes per latent, against a 73.3% chance level.

scCont breaks cell-state transitions in time-course single-cell RNA data into interpretable functional transition units: instead of explicit time labels or clustering priors, it selects positive pairs with a k-nearest-neighbor graph in expression space for contrastive learning, then uses Shapley attribution to map latent dimensions back to gene programs. Across four SERGIO simulated datasets and 13 epithelial-to-mesenchymal transition time-course datasets, its gene modules carry statistical filtering and GO enrichment annotation; on simulated data at k=10, background genes make up 7.0% of the 100 highest-attributed genes per latent, against a 73.3% chance level.

Using Center for International Blood and Marrow Transplant Research registry data, this study evaluated 541 patients with relapsed/refractory multiple myeloma who received commercial cilta-cel between March 2022 and December 2023 and found that 183 (33.8%) were frail by an adapted simplified frailty score; overall response rates were comparable (82.8% vs. 88.5%), but frail patients had worse 12-month progression-free survival (62.7% vs. 75.9%) and overall survival (72.8% vs. 90.4%) and more any-grade ICANS (32.2% vs. 17.6%), with frailty independently predicting worse PFS (HR 1.67), OS (HR 2.46), and any-grade ICANS (OR 2.01) on multivariable analysis.

Using Center for International Blood and Marrow Transplant Research registry data, this study evaluated 541 patients with relapsed/refractory multiple myeloma who received commercial cilta-cel between March 2022 and December 2023 and found that 183 (33.8%) were frail by an adapted simplified frailty score; overall response rates were comparable (82.8% vs. 88.5%), but frail patients had worse 12-month progression-free survival (62.7% vs. 75.9%) and overall survival (72.8% vs. 90.4%) and more any-grade ICANS (32.2% vs. 17.6%), with frailty independently predicting worse PFS (HR 1.67), OS (HR 2.46), and any-grade ICANS (OR 2.01) on multivariable analysis.

This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.

This preprint benchmarks eight open-source thigh muscle MRI segmentation tools on the MyoSegmenTUM, AIPS and Sheffield base datasets plus derived pathological and augmented out-of-distribution sets, combining Dice, Jaccard, Hausdorff, boundary IoU and inter-slice Dice ratio with qualitative usability review, and finds that domain-specific U-Net models, especially MuscleMap, generally outperformed foundation-model and newer general-purpose approaches, that adding SAM variants usually degraded rather than improved segmentation quality, and that most tools showed reduced accuracy on pathological cases.

This systematic review and meta-analysis pooled 5 studies covering 577 patients with confirmed trigeminal neuralgia to evaluate deep learning and machine learning models for automated trigeminal nerve segmentation and neurovascular conflict detection on MRI, finding a pooled sensitivity of 71% (95% CI: 65-76%), specificity of 92% (95% CI: 88-94%), and AUC of 0.91 (95% CI: 0.88-0.93), suggesting high diagnostic accuracy for AI models in this task.

This systematic review and meta-analysis pooled 5 studies covering 577 patients with confirmed trigeminal neuralgia to evaluate deep learning and machine learning models for automated trigeminal nerve segmentation and neurovascular conflict detection on MRI, finding a pooled sensitivity of 71% (95% CI: 65-76%), specificity of 92% (95% CI: 88-94%), and AUC of 0.91 (95% CI: 0.88-0.93), suggesting high diagnostic accuracy for AI models in this task.