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.

Evaluating the use of large language models to provide clinical recommendations in the Emergency Department.

The release of GPT-4 and other large language models (LLMs) has the potential to transform healthcare. However, existing research evaluating LLM performance on real-world clinical notes is limited. Here, we conduct a highly-powered study to determine whether LLMs can provide clinical recommendations for three tasks (admission status, radiological investigation(s) request status, and antibiotic prescription status) using clinical notes from the Emergency Department.

The evolution of computational research in a data-centric world.

Computational data-centric research techniques play a prevalent and multi-disciplinary role in life science research. In the past, scientists in wet labs generated the data, and computational researchers focused on creating tools for the analysis of those data. Computational researchers are now becoming more independent and taking leadership roles within biomedical projects, leveraging the increased availability of public data.

Novel calibration design improves knowledge transfer across products for the characterization of pharmaceutical bioprocesses.

Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models.

A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports.

Objective: Although supervised machine learning is popular for information extraction from clinical notes, creating large annotated datasets requires extensive domain expertise and is time-consuming. Meanwhile, large language models (LLMs) have demonstrated promising transfer learning capability. In this study, we explored whether recent LLMs could reduce the need for large-scale data annotations.

Use of a Large Language Model to Assess Clinical Acuity of Adults in the Emergency Department.

Importance: The introduction of large language models (LLMs), such as Generative Pre-trained Transformer 4 (GPT-4; OpenAI), has generated significant interest in health care, yet studies evaluating their performance in a clinical setting are lacking. Determination of clinical acuity, a measure of a patient's illness severity and level of required medical attention, is one of the foundational elements of medical reasoning in emergency medicine.

Objective: To determine whether an LLM can accurately assess clinical acuity in the emergency department (ED).

Ethical and regulatory challenges of large language models in medicine.

With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs.

Evaluating Large Language Models for Drafting Emergency Department Discharge Summaries.

Importance: Large language models (LLMs) possess a range of capabilities which may be applied to the clinical domain, including text summarization. As ambient artificial intelligence scribes and other LLM-based tools begin to be deployed within healthcare settings, rigorous evaluations of the accuracy of these technologies are urgently needed.

Objective: To investigate the performance of GPT-4 and GPT-3.

Evaluating large language models as agents in the clinic.

Recent developments in large language models (LLMs) have unlocked opportunities for healthcare, from information synthesis to clinical decision support. These LLMs are not just capable of modeling language, but can also act as intelligent “agents” that interact with stakeholders in open-ended conversations and even influence clinical decision-making. Rather than relying on benchmarks that measure a model’s ability to process clinical data or answer standardized test questions, LLM agents can be modeled in high-fidelity simulations of clinical settings and should be assessed for their impact on clinical workflows.

Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores From Colonoscopy Reports.

Background: The Mayo endoscopic subscore (MES) is an important quantitative measure of disease activity in ulcerative colitis. Colonoscopy reports in routine clinical care usually characterize ulcerative colitis disease activity using free text description, limiting their utility for clinical research and quality improvement. We sought to develop algorithms to classify colonoscopy reports according to their MES.

An innovative hybrid modeling approach for simultaneous prediction of cell culture process dynamics and product quality.

The use of hybrid models is extensively described in the literature to predict the process evolution in cell cultures. These models combine mechanistic and machine learning methods, allowing the prediction of complex process behavior, in the presence of many process variables, without the need to collect a large amount of data. Hybrid models cannot be directly used to predict final product critical quality attributes, or CQAs, because they are usually measured only at the end of the process, and more mechanistic knowledge is needed for many classes of CQAs.

Real-world effectiveness of ustekinumab and vedolizumab in TNF-exposed pediatric patients with ulcerative colitis.

Objectives: Vedolizumab (VDZ) and ustekinumab (UST) are second-line treatments in pediatric patients with ulcerative colitis (UC) refractory to antitumor necrosis factor (anti-TNF) therapy. Pediatric studies comparing the effectiveness of these medications are lacking. Using a registry from ImproveCareNow (ICN), a global research network in pediatric inflammatory bowel disease, we compared the effectiveness of UST and VDZ in anti-TNF refractory UC.

Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLMs) like Bidirectional Encoder Representations from Transformers (BERT) have shown progress in a range of natural language processing tasks but have not yet been evaluated on adverse event (AE) detection.