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 investigate the drug resistance phenotype/genotype and virulence gene carrying status, serotype, and sequence typing (ST) of Campylobacter jejuni in Fengtai District in 2024. Methods The drug resistance phenotype was detected by agar dilution method, and serotyping was performed by real-time fluorescence quantitative polymerase chain reaction (PCR). The whole genome sequencing was used to analyze drug resistance genes, virulence genes, and other characteristics. Results Among the 35 Campylobacter jejuni strains analyzed, the antibiotics with the highest resistance rates were nalidixic acid and ciprofloxacin (both 82.86%), followed by tetracycline (74.89%). All strains exhibited a 100.00% sensitivity rate to erythromycin. The multiple drug resistance rate was 77.14%. The predominant drug resistance genes were gyrA-T86I (94.29%) and tetO (74.29%). The most common serotype was HS:2, accounting for 25 strains. A total of 132 virulence genes were detected. There were 12 clonal complexes (CC) and 24 ST types. Conclusion The drug resistance situation of Campylobacter jejuni in Fengtai District is relatively severe, and the drug resistance phenotype is related to specific genetic mutations to some extent. The serotypes are diverse, the virulence genes are complex, and the pathogenic potential is significant, posing a notable risk for inducing Guillain-Barré syndrome (GBS). The high number of ST types indicates substantial genetic diversity.

Objective: To examine the causal association and potential mechanisms between bone mineral density in different age groups and primary malignant bone tumor based on two sample Mendelian randomization (MR), so as to provide a reference for the prevention and treatment of primary malignant bone tumor. Methods: The genome-wide association study (GWAS) of bone mineral density was obtained from the GEFOS database,which included 66 628 subjects divided into five age groups (0-15, 15-30, 30-45, 45-60, and >60 years) based on the phases of human bone development. The GWAS of primary malignant bone tumor was sourced from the FinnGen database, including 648 cases and 378 749 controls. Using bone mineral density of five age groups as the exposure and primary malignant bone tumor as the outcome, an MR analysis was performed with the inverse-variance weighted (IVW) method. Sensitivity analysis were conducted using Cochran's Q test, MR-Egger regression, MR-PRESSO test and MR Steiger test. The potential mechanisms underlying the causal association between bone density and primary malignant bone tumors were explored using Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results: The MR analysis results showed that there was a negative causal association between bone density and primary malignant bone tumors in the 30-45 age group (OR=0.301, 95%CI: 0.126-0.721). No statistically significant associations between bone density and primary malignant bone tumors were found in the 0-15, 15-30, 45-60, and >60 age groups (all P>0.05). Sensitivity analysis did not detect heterogeneity, pleiotropy (all P>0.05) and reverse causality. KEGG enrichment analysis revealed that genes highly associated with bone density and primary malignant bone tumors were enriched in the mTOR signaling pathway and the Wnt signaling pathway, among which Low Density lipoprotein Receptor Related protein 5 and Wnt Family Member 16 are key regulatory genes. Conclusion The decrease in bone mineral density among individuals aged 30-45 may increase the risk of primary malignant bone tumors through the mTOR signaling pathway and the Wnt signaling pathway.

The aging microenvironment, as a key driver of tumorigenesis and progression, plays a critical role in tumor immune regulation through one of its core features-the senescence-associated secretory phenotype (SASP). SASP consists of a variety of interleukins, chemokines, proteases, and growth factors. It initially induces surrounding cells to enter a state of senescence through paracrine mechanisms, thereby creating a sustained inflammatory stimulus and signal amplification effect within the tissue microenvironment. Furthermore, these secreted factors activate key signaling pathways such as NF-κB, cGAS-STING, and mTOR, which regulate the expression of immune-related molecules (such as PD-L1) and promote the recruitment of immunosuppressive cells, including regulatory T cells and myeloid-derived suppressor cells. This process ultimately contributes to the formation of an immunosuppressive tumor microenvironment. Furthermore, the article explores potential anti-tumor immunotherapy strategies targeting SASP and its associated molecular mechanisms, including approaches to inhibit SASP secretion or eliminate senescent cells. Although these strategies have shown promise in certain tumor models, the high heterogeneity among tumor types may result in varied responses to SASP-targeted therapies. This highlights the need for further research into adaptive stratification and personalized treatment approaches. Targeting immune regulatory mechanisms in the aging microenvironment-particularly SASP-holds great potential for advancing future anti-tumor therapies.

Dysfunction of the interoceptive system is recognized as an important component of clinical symptoms, including anxiety, depression, psychosis, and other mental disorders. Non-invasive neuromodulation is an emerging clinical intervention approach, and over the past decade, research on non-invasive neuromodulation aimed at regulating interoception has rapidly developed. This review first outlines the pathways of interoceptive signals and assessment methods, then summarizes the interoceptive abnormalities in psychiatric disorders and current studies for non-invasive neuromodulation targeting interoception, including intervention modes, target sites, interoceptive measures, and potential neurobiological mechanisms. Finally, we discuss significant research challenges and future directions.
Humans ; Interoception/physiology* ; Mental Disorders/therapy*

Objective:To screen the key genetic,diagnostic and therapeutic targets of chronic obstructive pulmonary disease(COPD)patients by using microarray datasets and Mendelian randomization(MR)method,and to provide the evidence for clinical diagnosis and treatment of COPD.Methods:Four COPD gene expression profile datasets were obtained from the Gene Expression Omnibus(GEO)database.The data were processed and normalized using R software,and differentially expressed genes(DEGs)were screened.MR analysis was performed to explore the causal relationship between COPD and expression quantitative trait loci(eQTL),intersection with DEGs was taken to identify potential key targets.Gene Set Enrichment Analysis(GSEA),Gene Ontology(GO)functional enrichment analysis,and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis were conducted to investigate the functional roles and pathways of the key targets,external datasets were used to validate their expression.Results:A total of 1 571 DEGs were screened,including 820 upregulated genes and 751 downregulated genes.MR analysis identified 286 COPD-related genes,and intersection with DEGs revealed 3 upregulated genes:diacylglycerol kinase gamma(DGKG),neurofilament heavy polypeptide(NEFH),and Fc receptor like B(FCRLB);and 6 downregulated genes:STEAP4 metalloreductase(STEAP4),pleckstrin homology domain containing family F member 2(PLEKHF2),CD3d molecule(CD3D),transgelin 2(TAGLN2),tripartite motif containing 22(TRIM22),and ribosomal protein L9(RPL9).The biological function analysis results indicated that these genes were mainly involved in pathways such as iron ion transport into the cells,oxidoreductase activity,primary immunodeficiency,and Th1 and Th2 cell differentiation.The MR analysis results confirmed the causal relationship between these targets and COPD.The external validation results showed that compared with healthy controls,the expression level of FCRLB in COPD samples was significantly increased(P<0.01),while the expression levels of CD3D and RPL9 were significantly decreased(P<0.05 or P<0.01),which was consistent with the MR analysis results,highlighting the reliability of this study.Conclusion:DGKG,NEFH,FCRLB,STEAP4,PLEKHF2,CD3D,TAGLN2,TRIM22,and RPL9 may serve as important regulatory factors and clinical diagnostic/therapeutic targets in the pathogenesis of COPD,providing clues for early screening,diagnosis,and targeted treatment of COPD.

Objective·To investigate the longitudinal changes in amygdala and hippocampal subfield volumes before and after transcranial magnetic stimulation(TMS)treatment in patients with major depressive disorder(MDD)and explore their correlation with the antidepressant and anxiolytic efficacy of TMS.Methods·A total of 58 patients diagnosed with MDD at Shanghai Mental Health Center,Shanghai Jiao Tong University School of Medicine,were included in this study between January 2018 and August 2023.Clinical depressive and anxiety symptoms were assessed by using the Hamilton Depression Scale(HAMD),Montgomery-Asberg Depression Rating Scale(MADRS),and Hamilton Anxiety Scale(HAMA)at baseline and post-TMS treatment.Patients underwent a baseline magnetic resonance imaging(MRI)scan followed by TMS treatment targeting the left dorsolateral prefrontal cortex(DLPFC)at a frequency of 10 Hz,totaling 20 sessions.A follow-up MRI scan was conducted on the same day the TMS treatment concluded.Amygdala and hippocampal subfield volumes were segmented and calculated by using FreeSurfer v6.0.0 software.Longitudinal changes in the subfield volumes were analyzed with two-way analysis of variance.Controlling for age,sex,and intracranial volume,partial correlation analysis was conducted between subfield volumes and baseline clinical scores.The association between the rate of volume change in brain regions with significant volume changes and symptom improvement(reduction in HAMD,MADRS,and HAMA scores)was evaluated.Results·Following TMS treatment,a significant increase in the volume of the right amygdala central nucleus was observed(t=-2.441,P=0.018).While the volumes of bilateral hippocampal fimbria decreased,the volumes of most hippocampal subfield and the total hippocampus increased(P<0.05).No significant correlations were found between baseline amygdala or hippocampal subfield volumes and clinical depressive and anxiety symptoms.However,only in patients who responded effectively to TMS treatment,a positive correlation was found between the volume change rate of the left hippocampal tail and reductions in anxiety symptoms(HAMA:r=0.334,P=0.044).Conclusion·High-frequency TMS targeting the left DLPFC may induce volume increases in the right amygdala central nucleus and specific hippocampal subfields.Additionally,the volume change rate of the left hippocampal tail is associated with anti-anxiety effects in TMS responders,suggesting that high-frequency TMS targeting the left DLPFC may induce neuroplastic changes in the central nucleus of the right amygdala and key subfields of the hippocampus.

Objective:To investigate the factors influencing the delayed onset of intrapartum fever following epidural labor analgesia and their impact on maternal and neonatal outcomes.Methods:Select parturients who experienced intrapartum fever following labor analgesia(T≥38.0℃,age≥18 years,singleton pregnancy,ASA classification Ⅱ)between January 1,2021,and December 31,2023.Group them based on the median time of intra-partum fever onset after labor analgesia:those with onset times less than the median were classified as the ear-ly-onset fever group,and those with onset times greater than the median were classified as the late-onset fever group.Using univariate and multivariate Logistic regression analysis to explore factors influencing the delay in in-trapartum fever onset and the pregnancy outcomes of the mothers and newborns in both groups.Results:A total of 253 parturients were included,and the time range of onset of intrapartum fever following epidural labor analgesi-a was 1.83-28.42 hours,with a median fever onset time of 8.00 hours.There were 126 cases in the early-onset group and 127 in the late-onset group.Multivariate Logistic regression analysis indicated that primiparous women,artificial membrane rupture,and neonatal birth weight were independent risk factors for delayed fever onset(OR＞1,P＜0.05),whereas the administration of oxytocin prior to labor analgesia was found to be a protective factor(OR＜1,P＜0.05).The late-onset group exhibited higher levels of white blood cells(WBC),C-reactive pro-tein,longer hospital stays,higher hospitalization costs,greater diagnosis rates of chorioamnionitis,higher NICU ad-mission rates,as well as a higher incidence of neonatal pneumonia,for newborns compared to the early-onset group(P＜0.05).Conclusions:Primiparous women,artificial membrane rupture,and higher neonatal birth weight may be associated with delayed onset of intrapartum fever,while oxytocin administration prior to labor analgesia may offer some protective benefit.The later the onset of intrapartum fever,the worse the clinical outcomes for both mother and infants.

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In general, bioassay-guided fractionation and isolation of bioactive constituents from botanical materials frequently ended up with the reward of a single compound. However, botanical materials typically exert their therapeutic actions through multi-pathway effects due to the intrinsic complex nature of chemical constituents. In addition, the content of bioactive compounds in botanical materials is largely dependent on humidity, temperature, soil, especially geographical origins, from which rapid and accurate identification of plant materials is pressingly needed. These long-standing obstacles collectively impede the deep exploitation and application of these versatile natural sources. To address the challenges, a new paradigm integrating biogravimetric analyses and machine learning-driven origin classification (BAMLOC) was developed. The biogravimetric analyses are based on absolute qHNMR quantification and in vivo zebrafish model-assisted activity index calculation, by which bioactive substance groups jointly responsible for the bioactivities in all fractions are pinpointed before any isolation effort. To differentiate origin-different botanical materials varying in the content of bioactive substance groups, principal component analysis, linear discriminant analysis, and hierarchical cluster analysis in conjunction with supervised support vector machine are employed to classify and predict production areas based on the detection of volatile organic compounds by E-nose and GC-MS. Expanding BAMLOC to Codonopsis Radix enables the identification of polyacetylenes and pyrrolidine alkaloids as the bioactive substance group for immune restoration effect and accurately determines the origins of plants. This study advances the toolbox for the discovery of bioactive compounds from complex mixtures and lays a more definitive foundation for the in-depth utilization of botanical materials.