Evaluating Large Language Models in extracting cognitive exam dates and scores.

Ensuring reliability of Large Language Models (LLMs) in clinical tasks is crucial. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA.

Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.

Importance: Large language models (LLMs) are crucial for medical tasks. Ensuring their reliability is vital to avoid false results. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR.

Substrate-dependent modulation of the leukotriene A hydrolase aminopeptidase activity and effect in a murine model of acute lung inflammation.

The aminopeptidase activity (AP) of the leukotriene A hydrolase (LTAH) enzyme has emerged as a therapeutic target to modulate host immunity. Initial reports focused on the benefits of augmenting the LTAH AP activity and clearing its putative pro-inflammatory substrate Pro-Gly-Pro (PGP). However, recent reports have introduced substantial complexity disconnecting the LTAH modulator 4-methoxydiphenylmethane (4MDM) from PGP as follows: (1) 4MDM inhibits PGP hydrolysis and subsequently inhibition of LTAH AP activity, and (2) 4MDM activates the same enzyme target in the presence of alternative substrates.