Trajectories of change in prolonged exposure for primary care: Who most likely benefits?

Objective: Prolonged Exposure for Primary Care (PE-PC) leverages an opportunity to provide evidence-based posttraumatic stress disorder (PTSD) care in the PC setting where many veterans present for services and can greatly increase treatment access. However, such acute, short-term intervention may not be effective for all PTSD patients.

Method: We analyzed data from a randomized clinical trial evaluating PE-PC to determine who may or may not benefit from PE-PC.

Functional evaluation and clinical classification of BRCA2 variants.

Germline BRCA2 loss-of function variants, which can be identified through clinical genetic testing, predispose to several cancers. However, variants of uncertain significance limit the clinical utility of test results. Thus, there is a need for functional characterization and clinical classification of all BRCA2 variants to facilitate the clinical management of individuals with these variants.

Meningiomas in Rubinstein-Taybi syndrome: A case report and comprehensive review.

Rubinstein-Taybi syndrome (RTS) is a congenital disorder with characteristic clinical manifestations. In the vast majority of cases, it is caused by mutations of the gene encoding the transcriptional co-activator cAMP-response element binding protein (CBP)-binding protein (CREBBP). It has been thought to be a tumor predisposition syndrome as RTS patients have an increased risk of developing tumors including meningiomas.

Enhancing human phenotype ontology term extraction through synthetic case reports and embedding-based retrieval: A novel approach for improved biomedical data annotation.

With the increasing utilization of exome and genome sequencing in clinical and research genetics, accurate and automated extraction of human phenotype ontology (HPO) terms from clinical texts has become imperative. Traditional methods for HPO term extraction, such as PhenoTagger, often face limitations in coverage and precision. In this study, we propose a novel approach that leverages large language models (LLMs) to generate synthetic sentences with clinical context, which were semantically encoded into vector embeddings.