Objective To understand the antibody levels of diphtheria and tetanus among healthy population in Shanghai Pudong New Area, and to provide a scientific basis for improving the vaccine immunization strategy. Methods Random sampling was used to select healthy people of all ages in 16 communities in Shanghai Pudong New Area from 2018 to 2024, and serum samples were collected and tested for serum anti-diphtheria and tetanus toxin IgG antibodies by enzyme-linked immunosorbent assay (ELISA) method to analyze the antibody positivity rate (≥0.1 IU/ml) and the geometric mean concentration (GMC) of antibodies. Results A total of 3 312 serum samples were included, with a male-to-female ratio of 0.76:1, and 53.77% were local residents. The seropositivity rates and geometric mean concentrations (GMC) of both diphtheria and tetanus antibodies generally declined with increasing age, but exhibited a transient rebound in the 7y-. A total of 1 175 individuals (35.48%) were seropositive for diphtheria, with a GMC of 0.054 IU/mL. For tetanus, 988 individuals (29.83%) were seropositive, with a GMC of 0.033 IU/mL. Significant differences in seropositivity rates (χ²_diphtheria=950.005,χ²_tetanus=1 324.393) and GMC (H_diphtheria=1027.160,H_tetanus=1 142.007) were observed among different age groups (P<0.001). Significant differences in seropositivity rates (χ²_diphtheria=950.005,χ²_tetanus=1324.393) and GMC (H_diphtheria=1027.160,H_tetanus=1142.007) were also found across different years (P<0.001). Conclusion The prevalence of diphtheria and tetanus antibodies in the healthy population of Pudong New Area is relatively low, particularly among adults over 20 years of age with inadequate immunization. This underscores the need to reinforce the National Immunization Program (NIP) vaccine specifications for children under 6 years of age and implement an immunization strategy for adolescents or adults against diphtheria and tetanus.

The development of bone in human body and the maintenance of bone mass in adulthood are regulated by a variety of biological factors. Myocyte enhancer factor 2C (MEF2C), as one of the many factors regulating bone tissue development and balance, has been shown to play a key role in bone development and metabolism. However, there is limited systematic analysis on the effects of MEF2C on bone tissue. This article reviews the role of MEF2C in bone development and metabolism. During bone development, MEF2C promotes the development of neural crest cells (NC) into craniofacial cartilage and directly promotes cartilage hypertrophy. In terms of bone metabolism, MEF2C exhibits a differentiated regulatory model across different types of osteocytes, demonstrating both promoting and other potential regulatory effects on bone formation, with its stimulating effect on osteoclasts being determined. In view of the complex roles of MEF2C in bone tissue, this paper also discusses its effects on some bone diseases, providing valuable insights for the physiological study of bone tissue and strategies for the prevention of bone diseases.
Humans ; MEF2 Transcription Factors/physiology* ; Bone and Bones/metabolism* ; Animals ; Bone Development/physiology* ; Osteogenesis/physiology* ; Myogenic Regulatory Factors/physiology*

Tripartite motif-containing (TRIM) family proteins are crucial E3 ubiquitin ligases that have garnered significant attention for their regulatory roles in bone metabolism in recent years. This article reviews the function and regulatory mechanisms of TRIM family proteins in bone metabolism, focusing on their dual roles in bone formation and resorption. It also provides a detailed analysis of signaling pathways and molecular mechanisms by which TRIM family members regulate the activities of osteoblasts and osteoclasts. Research findings suggest that modulating the expression or activity of TRIM family proteins could be beneficial for treating bone diseases such as osteoporosis. This review highlights the molecular mechanisms of TRIM family members in bone physiology and pathology, aiming to provide theoretical basis and scientific guidance for developing novel therapeutic strategies for bone diseases.
Humans ; Ubiquitin-Protein Ligases/physiology* ; Bone and Bones/metabolism* ; Animals ; Tripartite Motif Proteins/physiology* ; Osteoclasts/metabolism* ; Osteoblasts/metabolism* ; Signal Transduction/physiology* ; Osteogenesis/physiology*

This study identified blood-entering components of Anshen Dropping Pills and explored their anti-insomnia effects and mechanisms. The main blood-entering components of Anshen Dropping Pills were detected and identified by UPLC-Q-TOF-MS/MS. The rationality of the formula was assessed by using enrichment analysis based on the relationship between drugs and symptoms, and core targets of its active components were selected as the the potential anti-insomnia targets of Anshen Dropping Pills through network pharmacology analysis. Furthermore, protein-protein interaction(PPI) network, Gene Ontology(GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were performed on the core targets. An active component-core target network for Anshen Dropping Pills was constructed. Finally, the effects of low-, medium-, and high-dose groups of Anshen Dropping Pills on sleep episodes, sleep duration, and sleep latency in mice were measured by supraliminal and subliminal pentobarbital sodium experiments. Moreover, total scores of the Pittsburgh sleep quality index(PSQI) scale was used to evaluate the changes before and after the treatment with Anshen Dropping Pills in a clinical study. The enrichment analysis based on the relationship between drugs and symptoms verified the rationality of the Anshen Dropping Pills formula, and nine blood-entering components of Anshen Dropping Pills were identified by UPLC-Q-TOF-MS/MS. The network proximity revealed a significant correlation between eight components and insomnia, including magnoflorine, liquiritin, spinosin, quercitrin, jujuboside A, ginsenoside Rb_3, glycyrrhizic acid, and glycyrrhetinic acid. Network pharmacology analysis indicated that the major anti-insomnia pathways of Anshen Dropping Pills involved substance and energy metabolism, neuroprotection, immune system regulation, and endocrine regulation. Seven core genes related to insomnia were identified: APOE, ALB, BDNF, PPARG, INS, TP53, and TNF. In summary, Anshen Dropping Pills could increase sleep episodes, prolong sleep duration, and reduce sleep latency in mice. Clinical study results demonstrated that Anshen Dropping Pills could decrease total scores of PSQI scale. This study reveals the pharmacodynamic basis and potential multi-component, multi-target, and multi-pathway effects of Anshen Dropping Pills, suggesting that its anti-insomnia mechanisms may be associated with the regulation of insomnia-related signaling pathways. These findings offer a theoretical foundation for the clinical application of Anshen Dropping Pills.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Tandem Mass Spectrometry/methods* ; Sleep Initiation and Maintenance Disorders/metabolism* ; Mice ; Network Pharmacology ; Male ; Chromatography, High Pressure Liquid ; Humans ; Protein Interaction Maps/drug effects* ; Sleep/drug effects* ; Female ; Adult

Hepatocellular carcinoma(HCC), the third leading cause of cancer-related death worldwide, is characterized by high mortality and recurrence rates. Common treatments include hepatectomy, liver transplantation, ablation therapy, interventional therapy, radiotherapy, systemic therapy, and traditional Chinese medicine(TCM). While exhibiting specific advantages, these approaches are associated with varying degrees of adverse effects. To alleviate patients' suffering and burdens, it is crucial to explore additional treatments and elucidate the pathogenesis of HCC, laying a foundation for the development of new TCM-based drugs. With emerging research on gut microbiota, it has been revealed that microbiota plays a vital role in the development of HCC by influencing intestinal barrier function, microbial metabolites, and immune regulation. TCM, with its multi-component, multi-target, and multi-pathway characteristics, has been increasingly recognized as a vital therapeutic treatment for HCC, particularly in patients at intermediate or advanced stages, by prolonging survival and improving quality of life. Recent global studies demonstrate that TCM exerts anti-HCC effects by modulating gut microbiota, restoring intestinal barrier function, regulating microbial composition and its metabolites, suppressing inflammation, and enhancing immune responses, thereby inhibiting the malignant phenotype of HCC. This review aims to elucidate the mechanisms by which gut microbiota contributes to the development and progression of HCC and highlight the regulatory effects of TCM, addressing the current gap in systematic understanding of the "TCM-gut microbiota-HCC" axis. The findings provide theoretical support for integrating TCM with western medicine in HCC treatment and promote the transition from basic research to precision clinical therapy through microbiota-targeted drug development and TCM-based interventions.
Humans ; Gastrointestinal Microbiome/drug effects* ; Carcinoma, Hepatocellular/microbiology* ; Liver Neoplasms/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

OBJECTIVE: To investigate the regulatory effects of two traditional mineral medicines (TMMs), Gypsum Fibrosum (Shigao, GF) and Terra Flava Usta (Zaoxintu, TFU), on gut-beneficial bacteria in mice, and preliminarily explore their mechanisms of action. METHODS: Mice were randomly divided into 3 groups (n=10 per group): the control group (standard diet), the GF group (diet supplemented with 2% GF), and the TFU group (diet supplemented with 2% TFU). After 4-week intervention, 16S rRNA gene sequencing was used to analyze the changes in the gut microbiota (GM). Scanning electron microscopy, in combination with coumarin A tetramethyl rhodamine conjugate and Hoechst stainings, was used to observe the bacteria and biofilm formation. RESULTS: Principal coordinate analysis revealed that GF and TFU significantly altered the GM composition in mice. Further analysis revealed that GF and TFU affected different types of gut bacteria, suggesting that different TMMs may selectively modulate specific bacterial populations. For certain bacteria, such as Faecalibaculum and Ileibacterium, both GF and TFU exhibited growth-promoting effects, implying that they may be sensitive to TMMs and that different TMMs can increase their abundance through their respective mechanisms. Notably, Lactobacillus reuteri, a widely recognized and used probiotic, was significantly enriched in the GF group. Random forest analysis identified Ileibacterium valens as a potential indicator bacterium for TMMs' impact on GM. Further mechanistic studies showed that gut bacteria formed biofilm structures on the TFU surface. CONCLUSIONS This study provides new insights into the interaction between TMMs and GM. As safe and effective natural clays, GF and TFU hold promise as potential candidates for prebiotic development.
Animals ; Gastrointestinal Microbiome/drug effects* ; Bacteria/growth & development* ; Mice ; Biofilms/drug effects* ; Male ; RNA, Ribosomal, 16S/genetics*

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

Objective To analyze clinical characteristics of mesenchymal-subtype gastric cancer(Mes-GC)by integrating multi-omics data and explore the characteristics of tumor cells and microenvironment associated with the risk for recurrence.Methods Gastric tumor tissue samples were collected from the patients who visited Department of Gastroenterology of Army Medical Center of PLA from January 2022 to December 2023.Transcriptome and genome sequencing were applied for these tissue samples,including 19 cases of diffuse-type gastric cancer,22 cases of intestinal-type gastric cancer,and 23 cases of mixed-type gastric cancer patients.Bioinformatics analysis was employed to investigate the differences in clinical characteristics and tumor microenvironment between Mes-GC and non-mesenchymal-subtype gastric cancer(non-Mes-GC)by integrating data resources including The Cancer Genome Atlas(TCGA),Gene Expression Omnibus(GEO),and National Genomics Data Center(NGDC).Results Compared to non-Mes-GC patients,Mes-GC ones were characterized by later clinical stages,deeper tumor infiltration,and higher rates of lymph node metastasis.Kaplan-Meier survival analysis confirmed that Mes-GC patients were associated with shorter survival time,poor prognosis as well as increased risk of cancer recurrence(P<0.05).Single-cell RNA sequencing data revealed that tumor cells in Mes-GC showed higher expression levels of the genes related to stemness,metastasis(P<0.05),and epithelial-mesenchymal transition(EMT).And in the tumor microenvironment,there were significant more myeloid cells,smooth muscle cells,endothelial cells and fibroblasts,with the most pronounced elevation in the proportion of fibroblasts(P<0.05).Moreover,the patients with larger proportion of fibroblasts were associated with poorer prognosis.Conclusion Mes-GC tumor cells exhibit higher stemness and EMT characteristics,and stromal cells such as myeloid cells,endothelial cells,and fibroblasts are enriched in the tumor microenvironment.These features may be key factors contributing to poor prognosis and high recurrence rate of Mes-GC.