HIV-SEQ REVEALS GLOBAL HOST GENE EXPRESSION DIFFERENCES BETWEEN HIV-TRANSCRIBING CELLS FROM VIREMIC AND SUPPRESSED PEOPLE WITH HIV.

"Active" reservoir cells transcribing HIV can perpetuate chronic inflammation in virally suppressed people with HIV (PWH) and likely contribute to viral rebound after antiretroviral therapy (ART) interruption, so they represent an important target for new therapies. These cells, however, are difficult to study using single-cell RNA-seq (scRNA-seq) due to their low frequency and low levels of HIV transcripts, which are usually not polyadenylated. Here, we developed "HIV-seq" to enable more efficient capture of HIV transcripts - including non-polyadenylated ones - for scRNA-seq analysis of cells from PWH.

Assessing Large Language Models for Oncology Data Inference From Radiology Reports.

Purpose: We examined the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancreatic cancer from radiology reports.

Methods: We analyzed 203 deidentified radiology reports, manually annotated for disease status, location, and indeterminate nodules needing follow-up. Using generative pre-trained transformer (GPT)-4, GPT-3.

Repurposing an epithelial sodium channel inhibitor as a therapy for murine and human skin inflammation.

Inflammatory skin disease is characterized by a pathologic interplay between skin cells and immunocytes and can result in disfiguring cutaneous lesions and systemic inflammation. Immunosuppression is commonly used to target the inflammatory component; however, these drugs are often expensive and associated with side effects. To identify previously unidentified targets, we carried out a nonbiased informatics screen to identify drug compounds with an inverse transcriptional signature to keratinocyte inflammatory signals.

California Stress, Trauma, and Resilience Study (CalSTARS) protocol: A multiomics-based cross-sectional investigation and randomized controlled trial to elucidate the biology of ACEs and test a precision intervention for reducing stress and enhancing resilience.

Adverse Childhood Experiences (ACEs) are very common and presently implicated in 9 out of 10 leading causes of death in the United States. Despite this fact, our mechanistic understanding of how ACEs impact health is limited. Moreover, interventions for reducing stress presently use a one-size-fits-all approach that involves no treatment tailoring or precision.

Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes.

Objective: We aimed to investigate the impact of social circumstances on cancer therapy selection using natural language processing to derive insights from social worker documentation.

Materials And Methods: We developed and employed a Bidirectional Encoder Representations from Transformers (BERT) based approach, using a hierarchical multi-step BERT model (BERT-MS), to predict the prescription of targeted cancer therapy to patients based solely on documentation by clinical social workers. Our corpus included free-text clinical social work notes, combined with medication prescription information, for all patients treated for breast cancer at UCSF between 2012 and 2021.