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.

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*

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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.

Current epilepsy prediction methods are not effective in characterizing the multi-domain features of complex long-term electroencephalogram (EEG) data, leading to suboptimal prediction performance. Therefore, this paper proposes a novel multi-scale sparse adaptive convolutional network based on multi-head attention mechanism (MS-SACN-MM) model to effectively characterize the multi-domain features. The model first preprocesses the EEG data, constructs multiple convolutional layers to effectively avoid information overload, and uses a multi-layer perceptron and multi-head attention mechanism to focus the network on critical pre-seizure features. Then, it adopts a focal loss training strategy to alleviate class imbalance and enhance the model's robustness. Experimental results show that on the publicly created dataset (CHB-MIT) by MIT and Boston Children's Hospital, the MS-SACN-MM model achieves a maximum accuracy of 0.999 for seizure prediction 10 ~ 15 minutes in advance. This demonstrates good predictive performance and holds significant importance for early intervention and intelligent clinical management of epilepsy patients.
Humans ; Electroencephalography/methods* ; Epilepsy/physiopathology* ; Neural Networks, Computer ; Seizures/physiopathology* ; Signal Processing, Computer-Assisted ; Algorithms

Objective: To investigate the causal association between amino acids and the primary malignant bone tumor and its underlying mechanism. Methods: Genome-wide association study (GWAS) data of glycine, serine, arginine, glutamine, methionine, and leucine was sourced from the IEU OpenGWAS database and the GWAS Catalog. GWAS data of primary malignant bone tumor were obtained from the FinnGen database. Using each of the six amino acids as the exposure and primary malignant bone tumor as the outcome, two-sample Mendelian randomization (MR) analysis was performed with the inverse-variance weighted method as the primary approach. Multivariable MR analysis was employed to control for collinearity among amino acids. Sensitivity analyses were conducted using Cochran's Q test, MR-Egger regression and the MR Steiger test. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction network analysis were explored to explore potential mechanisms and identify key genes. Results: MR analysis results indicated a statistically significant causal association between glycine and primary malignant bone tumor (OR=1.719, 95%CI: 1.083-2.728). No significant causal associations were found for the other five amino acids (all P>0.05). Multivariable MR analysis revealed that, after adjusting for the other five amino acids, confirmed a positive causal association between glycine and primary malignant bone tumor (OR=1.512, 95%CI: 1.125-2.031). Sensitivity analyses revealed no significant heterogeneity, horizontal pleiotropy, or reverse causality (all P>0.05). Genes associated with both glycine metabolism and primary malignant bone tumor were enriched in the JAK-STAT signaling pathway, with serine hydroxymethyltransferase 2 (SHMT2) identified as a key gene. Conclusion Higher glycine levels may increase the risk of primary malignant bone tumor via the SHMT2-JAK-STAT pathway.

Pediatric nuclear medicine achieve precise functional and metabolic assessments with renal dynamic imaging,bone scintigraphy and 18F-FDG PET/CT,playing irreplaceable role for diagnosis and treatment of pediatric diseases.The emergence of novel molecular imaging probes,such as 68 Ga-DOT AT ATE,18F-DOPA and 18F-MFBG,expand clinical application field of pediatric nuclear medicine,while radionuclide therapy using 131I,131I-MIBG and 177 Lu-DOT AT ATE offer targeted options for pediatric thyroid cancer and neuroendocrine tumors.The current status,challenges and future prospect of pediatric nuclear medicine were reviewed in this article.

Non-small cell lung cancer(NSCLC)is the most common form of lung cancer,and patients with advanced disease generally have a poor prognosis.In recent years,immune checkpoint inhibitors(ICIs)have been approved as first-line therapy for patients with NSCLC lacking actionable driver mutations.Emerging evidence indicates that ICIs reduce tumor burden in some patients and may affect pulmonary ventila-tion and diffusion capacity,which are associated with clinical outcomes.This review summarizes baseline pulmonary function characteristics in advanced NSCLC,outlines in the impact of ICI therapy on lung function,and discusses potential mechanisms through which immunother-apy can influence pulmonary function,with the aim of guiding lung function monitoring and individualized management during ICI treat-ment.