Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models.

OBJECTIVE: The recent surge in large language models (LLMs) across various fields has yet to be fully realized in traditional Chinese medicine (TCM). This study aims to bridge this gap by developing a large language model tailored to TCM knowledge, enhancing its performance and accuracy in clinical reasoning tasks such as diagnosis, treatment, and prescription recommendations. MATERIALS AND METHODS: This study harnessed a wide array of TCM data resources, including TCM ancient books, textbooks, and clinical data, to create 3 key datasets: the TCM Pre-trained Dataset, the Traditional Chinese Patent Medicine (TCPM) Question Answering Dataset, and the Spleen and Stomach Herbal Prescription Recommendation Dataset. These datasets underpinned the development of the Lingdan Pre-trained LLM and 2 specialized models: the Lingdan-TCPM-Chat Model, which uses a Chain-of-Thought process for symptom analysis and TCPM recommendation, and a Lingdan Prescription Recommendation model (Lingdan-PR) that proposes herbal prescriptions based on electronic medical records. RESULTS: The Lingdan-TCPM-Chat and the Lingdan-PR Model, fine-tuned on the Lingdan Pre-trained LLM, demonstrated state-of-the art performances for the tasks of TCM clinical knowledge answering and herbal prescription recommendation. Notably, Lingdan-PR outperformed all state-of-the-art baseline models, achieving an improvement of 18.39% in the Top@20 F1-score compared with the best baseline. CONCLUSION: This study marks a pivotal step in merging advanced LLMs with TCM, showcasing the potential of artificial intelligence to help improve clinical decision-making of medical diagnostics and treatment strategies. The success of the Lingdan Pre-trained LLM and its derivative models, Lingdan-TCPM-Chat and Lingdan-PR, not only revolutionizes TCM practices but also opens new avenues for the application of artificial intelligence in other specialized medical fields. Our project is available at https://github.com/TCMAI-BJTU/LingdanLLM.

Histone deacetylases and inhibitors in diabetes mellitus and its complications.

Diabetes mellitus (DM) is a metabolic disorder characterized by hyperglycemia, with its prevalence linked to both genetic predisposition and environmental factors. Epigenetic modifications, particularly through histone deacetylases (HDACs), have been recognized for their significant influence on DM pathogenesis. This review focuses on the classification of HDACs, their role in DM and its complications, and the potential therapeutic applications of HDAC inhibitors. HDACs, which modulate gene expression without altering DNA sequences, are categorized into four classes with distinct functions and tissue specificity. HDAC inhibitors (HDACi) have shown efficacy in various diseases, including DM, by targeting these enzymes. The review highlights how HDACs regulate ß-cell function, insulin sensitivity, and hepatic gluconeogenesis in DM, as well as their impact on diabetic cardiomyopathy, nephropathy, and retinopathy. Finally, we suggest that targeted histone modification is expected to become a key method for the treatment of diabetes and its complications. The study of HDACi offers insights into new treatment strategies for DM and its associated complications.

[Effect of jianpi tongluo jiedu recipe on expression levels of COX-2, NF-kappaBp65, and Bcl-2 in gastric mucosa of patients with precancerous lesions of gastric cancer].

OBJECTIVE: To explore the effect of Jianpi Tongluo Jiedu Recipe (JTJR) on protein expression levels of COX-2, NF-kappaBp65, Bcl-2, and Bax, mRNA expression levels of COX-2 and Bcl-2, and the apoptotic index (Al) in gastric mucosa of patients with precancerous lesions of gastric cancer (PL-GC). METHODS: Totally 65 PLGC patients were recruited and treated by JTJR (modified by syndrome typing), one dose per day for six successive months. Protein expression levels of COX-2, NF-KBp65, Bcl-2, and Bax were detected in 65 patients using immunohistochemical (IHC) assay before and after treatment. mRNA expression levels of COX-2 and Bcl-2 were detected in 54 patients using reverse transcription-polymerase chain reaction (RT-PCR). Meanwhile, changes of Al was detected in 65 patients using TdT-mediated dUTP-biotin nick end labeling (TUNEL) fluorescence method. RESULTS: After treatment with JTJR, positive protein expression levels of COX-2, NF-KBp65, and Bcl-2 were obviously decreased in the gastric mucosa of PLGC patients (P <0.01), but Bax positive protein expression was found to be higher (P < 0.05). At the same time mRNA expression levels of COX-2 and Bcl-2 were significantly lower after treatment than before treatment (P < 0.05, P < 0.01); Al also increased after treatment (P < 0.05). CONCLUSION: JTJR could promote apoptosis possibly via NF-kappaBp65/COX-2, COX-2/Bcl-2, and NF-kappaBp65/Bcl-2 signaling pathways, thereby affecting PLGC patients.