Development and characterization of a low intensity vibrational system for microgravity studies.

Extended-duration human spaceflight necessitates a better understanding of the physiological impacts of microgravity. While the ground-based microgravity simulations identified low intensity vibration (LIV) as a possible countermeasure, how cells may respond to LIV under real microgravity remain unexplored. In this way, adaptation of LIV bioreactors for space remains limited, resulting in a significant gap in microgravity research.

Development and Characterization of a low intensity vibrational system for microgravity studies.

The advent of extended-duration human spaceflight demands a better comprehension of the physiological impacts of microgravity. One primary concern is the adverse impact on the musculoskeletal system, including muscle atrophy and bone density reduction. Ground-based microgravity simulations have provided insights, with vibrational bioreactors emerging as potential mitigators of these negative effects.

Large language models encode clinical knowledge.

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA.

User-centred design for machine learning in health care: a case study from care management.

Objectives: Few machine learning (ML) models are successfully deployed in clinical practice. One of the common pitfalls across the field is inappropriate problem formulation: designing ML to fit the data rather than to address a real-world clinical pain point.

Methods: We introduce a practical toolkit for user-centred design consisting of four questions covering: (1) solvable pain points, (2) the unique value of ML (eg, automation and augmentation), (3) the actionability pathway and (4) the model's reward function.