Large Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians.

Large language models (LLMs) are poised to have a disruptive impact on health care. Numerous studies have demonstrated promising applications of LLMs in medical imaging, and this number will grow as LLMs further evolve into large multimodal models (LMMs) capable of processing both text and images. Given the substantial roles that LLMs and LMMs will have in health care, it is important for physicians to understand the underlying principles of these technologies so they can use them more effectively and responsibly and help guide their development.

Defining the Cost of Arthroscopic Rotator Cuff Repair: A Multicenter, Time-Driven Activity-Based Costing and Cost Optimization Investigation.

Background: Rotator cuff repair (RCR) is a frequently performed outpatient orthopaedic surgery, with substantial financial implications for health-care systems. Time-driven activity-based costing (TDABC) is a method for nuanced cost analysis and is a valuable tool for strategic health-care decision-making. The aim of this study was to apply the TDABC methodology to RCR procedures to identify specific avenues to optimize cost-efficiency within the health-care system in 2 critical areas: (1) the reduction of variability in the episode duration, and (2) the standardization of suture anchor acquisition costs.

Impact of chronic ethanol consumption and SARS-COV-2 on the liver and intestine: A pilot dose-response study in mice.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, there was a marked increase in alcohol consumption. COVID-19 superimposed on underlying liver disease notably worsens the outcome of many forms of liver injury. The goal of a current pilot study was to test the dual exposure of alcohol and COVID-19 infection in an experimental animal model of alcohol-associated liver disease (ALD).

Protein-Enabled Size-Selective Defect-Sealing of Atomically Thin 2D Membranes for Dialysis and Nanoscale Separations.

Atomically thin 2D materials present the potential for advancing membrane separations via a combination of high selectivity (from molecular sieving) and high permeance (due to atomic thinness). However, the creation of a high density of precise nanopores (narrow-size-distribution) over large areas in 2D materials remains challenging, and nonselective leakage from nanopore heterogeneity adversely impacts performance. Here, we demonstrate protein-enabled size-selective defect sealing (PDS) for atomically thin graphene membranes over centimeter scale areas by leveraging the size and reactivity of permeating proteins to preferentially seal larger nanopores (≥4 nm) while preserving a significant amount of smaller nanopores (via steric hindrance).