Objective: To develop QingNangTCM, a specialized large language model (LLM) tailored for expert-level traditional Chinese medicine (TCM) question-answering and clinical reasoning, addressing the scarcity of domain-specific corpora and specialized alignment. Methods: We constructed QnTCM_Dataset, a corpus of 100 000 entries, by integrating data from ShenNong_TCM_Dataset and SymMap v2.0, and synthesizing additional samples via retrieval-augmented generation (RAG) and persona-driven generation. The dataset comprehensively covers diagnostic inquiries, prescriptions, and herbal knowledge. Utilizing P-Tuning v2, we fine-tuned the GLM-4-9B-Chat backbone to develop QingNangTCM. A multi-dimensional evaluation framework, assessing accuracy, coverage, consistency, safety, professionalism, and fluency, was established using metrics such as bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), and LLM-as-a-Judge with expert review. Qualitative analysis was conducted across four simulated clinical scenarios: symptom analysis, disease treatment, herb inquiry, and failure cases. Baseline models included GLM-4-9B-Chat, DeepSeek-V2, HuatuoGPT-II (7B), and GLM-4-9B-Chat (freeze-tuning). Results: QingNangTCM achieved the highest scores in BLEU-1/2/3/4 (0.425/0.298/0.137/0.064), ROUGE-1/2 (0.368/0.157), and METEOR (0.218), demonstrating a balanced and superior normalized performance profile of 0.900 across the dimensions of accuracy, coverage, and consistency. Although its ROUGE-L score (0.299) was lower than that of HuatuoGPT-II (7B) (0.351), it significantly outperformed domain-specific models in expert-validated win rates for professionalism (86%) and safety (73%). Qualitative analysis confirmed that the model strictly adheres to the “symptom-syndrome-pathogenesis-treatment” reasoning chain, though occasional misclassifications and hallucinations persisted when dealing with rare medicinal materials and uncommon syndromes. Conclusion Combining domain-specific corpus construction with parameter-efficient prompt tuning enhances the reasoning behavior and domain adaptation of LLMs for TCM-related tasks. This work provides a technical framework for the digital organization and intelligent utilization of TCM knowledge, with potential value for supporting diagnostic reasoning and medical education.

Objective: To determine the change of gene expressions in human perirenal adipose tissue (PAT) and oblique abdomen subcutaneous adipose tissue (SAT) of overweight and obese subjects. Methods: Ninety-seven patients, including 35 overweight/obese patients and 62 non-obese patients, who underwent renal surgery were included. The clinical data and various gene expressions in PAT and SAT of two groups were analyzed. Results: Body mass index, waist circumference, systolic blood pressure, resting heart rate, fasting blood glucose, and serum creatinine were significantly higher in overweight/obese patients than those in non-obese patients(P<0.05 or P<0.01). Compared with non-obese patients, PAT adipocytes showed bigger, and the expressions of uncoupling protein-1 (UCP1) mRNA and protein were markedly lower in overweight/obese patients. The mRNA expressions of CIDEA and adiponectin in PAT of overweight/obese patients were significantly lower, while leptin, monocyte chemotactic protein 1, interleukin (IL)-6, and IL-10 mRNA expressions were significantly higher. There were no significant differences in the related gene expressions of SAT between the two groups. Conclusions Different parts of adipose tissue reveal various characteristics, and play different roles in the occurrence and development of obesity. UCP1 expression is decreased in PAT of overweight/obese patients, with the changes of adipocytokine expressions. (Chin J Endocrinol Metab, 2018, 34: 567-572)

Objective To investigate the distribution and antimicrobial resistance of Coagulase-negative staphylococci ( CoNS) isolated from cerebrospinal fluids in neurosurgical patients.Methods CoNS strains isolated from cerebrospinal fluids of neurosurgical patients were collected from Beijing Tiantan Hospital of Capital Medical University during January 2013 and December 2015.CoNS infection was diagnosed according to the standards of US Centers for Disease Control and Prevention, and the distribution and antimicrobial resistance of pathogenic CoNS strains were analyzed. Results A total of 19 756 cerebrospinal fluid specimens were collected and 1 386 bacterial strains were isolated, in which 650 (46.9%) were CoNS.Among 650 CoNS strains, 130 were diagnosed as the pathogen, and the top 4 CoNS species were Staphylococcus epidermidis (77/130, 59.2%), Staphylococcus hominis (18/130, 13.8%), Staphylococcus haemolyticus (11/130, 8.5%) and Staphylococcus capitis (9/130, 6.9%).The rest 520 CoNS strains were contaminating strains.According to antimicrobial susceptibility test, there were 103 strains of methicillin-resistant CoNS (MR-CoNS) accounting for 79.1% (103/130).And among 77 Staphylococcus epidermidis isolates, 67 were MR-CoNS strains (87.0%) .More than 90.0%Staphylococcus epidermidis isolates were sensitive to vancomycin and linezolid, and the rest CoNS strains were also highly sensitive to these two antibacterial agents.Conclusions CoNS plays an important role in post-surgery infection in neurosurgical patients, and Staphylococcus epidermidis is the dominant CoNS species.Most CoNS strains are methicillin-resistant, but are highly sensitive to vancomycin and linezolid.