Large language models for preventing medication direction errors in online pharmacies | Nature Medicine
Errors in pharmacy medication directions, such as incorrect instructions for dosage or frequency, can increase patient safety risk substantially by raising the chances of adverse drug events. This study explores how integrating domain knowledge with large language models (LLMs)—capable of sophisticated text interpretation and generation—can reduce these errors. Researchers introduce MEDIC (medication direction copilot), a system that emulates the reasoning of pharmacists by prioritizing precise communication of core clinical components of a prescription, such as dosage and frequency.
CASCC: a co-expression assisted single-cell RNA-seq data clustering method | Bioinformatics
Existing clustering methods for characterizing cell populations from single-cell RNA sequencing are constrained by several limitations stemming from the fact that clusters often cannot be homogeneous, particularly for transitioning populations. On the other hand, dominant cell populations within samples can be identified independently by their strong gene co-expression signatures using methods unrelated to partitioning. Here, researchers introduce a clustering method, CASCC, designed to improve biological accuracy using gene co-expression features identified using an unsupervised adaptive attractor algorithm.
Virtual reality-empowered deep-learning analysis of brain cells | Nature Methods
Researchers created DELiVR, a deep-learning pipeline for 3D brain-cell mapping that is trained with virtual reality-generated reference annotations. It can be deployed via the user-friendly interface of the open-source software Fiji, which makes the analysis of large-scale 3D brain images widely accessible to scientists without computational expertise.