Brigham and Women's Hospital

Ovarian aging is not a one-way, fixed process. Using an ovary-specific transcriptomic clock, OvaAge, alongside epigenetic and pathway methylation clocks, researchers transplanted old ovaries into young mice and young ovaries into old mice and found that ovarian molecular age shifts in both directions with host age, but only partially. Ovaries from 13-month-old donors supported full-term pregnancy and live births at 17-18 months of age in young hosts, whereas ovaries of the same age failed to reproduce in old hosts.

Ovarian aging is not a one-way, fixed process. Using an ovary-specific transcriptomic clock, OvaAge, alongside epigenetic and pathway methylation clocks, researchers transplanted old ovaries into young mice and young ovaries into old mice and found that ovarian molecular age shifts in both directions with host age, but only partially. Ovaries from 13-month-old donors supported full-term pregnancy and live births at 17-18 months of age in young hosts, whereas ovaries of the same age failed to reproduce in old hosts.

The study introduces MergeRepair, which trains one LoRA adapter per task on the Python split of CommitPackFT (59,113 samples, with Bug Fixes/APR at 19.02%) for Development, Bug Fixes, Misc, Test & QA, and Improvement, merges them with weight-averaging, ties, and dare-ties under equal weights, and proposes continual merging that adds adapters sequentially; evaluated on StarCoder2 and Granite 3B code models with HumanEvalFix pass@1/pass@10, it finds that merging task-specific adapters can improve APR without additional training, reaching up to 2.38% pass@1 and 4.

The study introduces MergeRepair, which trains one LoRA adapter per task on the Python split of CommitPackFT (59,113 samples, with Bug Fixes/APR at 19.02%) for Development, Bug Fixes, Misc, Test & QA, and Improvement, merges them with weight-averaging, ties, and dare-ties under equal weights, and proposes continual merging that adds adapters sequentially; evaluated on StarCoder2 and Granite 3B code models with HumanEvalFix pass@1/pass@10, it finds that merging task-specific adapters can improve APR without additional training, reaching up to 2.38% pass@1 and 4.

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.