Thoracic Aorta Measurement Extraction from Computed Tomography Radiology Reports Using Instruction Tuned Large Language Models.

Chest computed tomography (CT) is essential for diagnosing and monitoring thoracic aortic dilations and aneurysms, conditions that place patients at risk of complications such as aortic dissection and rupture. However, aortic measurements in chest CT radiology reports are often embedded in free-text formats, limiting their accessibility for clinical care, quality improvement and research purposes. In this study, we developed a multi-method pipeline to extract structured aortic measurements from radiology reports, and compared the performance of fine-tuned BERT-based models with instruction-tuned Llama large language models (LLMs).

Complete genome sequence of type strain DSM33.

Here, we present the complete genome sequence of type strain DSM33, a model organism for microbially induced calcium carbonate precipitation. This genome consists of a single 3.3-Mb chromosome and is an improvement upon draft genome sequences currently available in public databases.

Are ICD codes reliable for observational studies? Assessing coding consistency for data quality.

Objective: International Classification of Diseases (ICD) codes recorded in electronic health records (EHRs) are frequently used to create patient cohorts or define phenotypes. Inconsistent assignment of codes may reduce the utility of such cohorts. We assessed the reliability across time and location of the assignment of ICD codes in a US health system at the time of the transition from ICD-9-CM (ICD, 9th Revision, Clinical Modification) to ICD-10-CM (ICD, 10th Revision, Clinical Modification).

Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning.

Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability).