To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

Aim To investigate the intervention effect of Rehmannia radix water extract on bleomycin(BLM)-induced pulmonary fibrosis in mice combined with metabolomics and to reveal the potential mechanism,in order to provide new ideas for clinical treatment of pul-monary fibrosis.Methods Male C57BL/6N mice were randomly divided into the control group,model group,pirfenidone group(positive control,PFD,270 mg·kg-1),and low dose(DH-L,4.55 g·kg-1)group,medium dose(DH-M,9.1 g·kg-1)group and high dose(DH-H,18.2 g·kg-1)group of Rehman-nia.Except for the control group,BLM(5 mg·kg-1)was instilled into the trachea to establish the model of pulmonary fibrosis in the other groups.The survival rate,lung index and blood oxygen saturation of mice in each group were evaluated.HE and Masson staining were used to observe the pathological changes of lung tissue.WBP was used to detect lung function.Flow cytometry was used to detect the apoptosis of primary lung cells,ROS and immune cells.ELISA was used to detect the levels of fibrosis markers and inflammatory factors(α-SMA,collagen Ⅰ,collagen Ⅲ,TGF-β1,TNF-α,IL-1 β,and IL-6).Biochemical method was employed to detect the contents of GSH-Px,T-SOD and MDA.Liquid chromatograph mass spectrometer(LC-MS)metabolomics was used to analyze the changes of serum metabolic profile.Results Water extract of Re-hmannia significantly increased the survival rate,oxy-gen saturation and lung function of mice with pulmona-ry fibrosis,reduced the lung coefficient,ameliorated pathological damage and collagen deposition in lung tissue,reduced the levels of apoptosis and oxidative stress,and down-regulated the levels of inflammatory factors in lung tissue.It regulated the levels of metabo-lites such as bile acid metabolism,sphingolipid metabo-lism,and unsaturated fatty acid metabolism.Conclu-sions Water extract of Rehmannia inhibits lung injury and collagen deposition in mice with pulmonary fibrosis by inhibiting inflammatory response,which may be a-chieved by regulating the levels of inflammatory factors through the metabolic pathways of bile acid and sphin-golipid.

Objective To elucidate the mechanisms of trauma-induced coagulopathy(TIC),clarify the specific pathogenic factors and pathophysiological processes,and discover the effective diagnostic indicators and therapeutic targets.Methods Transcriptomic data of traumatic hemorrhagic shock patients were obtained from the Gene Expression Omnibus(GEO)to identify differentially expressed genes(DEGs).Coagulation-related genes(CRGs)from the Kyoto Encyclopedia of Genes and Genomes(KEGG)were intersected with DEGs.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO)and random forest(RF),were applied to identify key genes.The CIBERSORT algorithm was used to analyze the correlation between key genes and immune cell infiltration.Through consensus clustering,subtype analysis was conducted on trauma patients to compare the infiltration of immune cells.A rat model of traumatic hemorrhagic shock was established to validate coagulation function and the expression of key genes.Results The dataset included samples from 17 healthy controls and 478 patients with traumatic hemorrhagic shock.A total of 6 315 DEGs were identified under the screening criterion of corrected P<0.05.Gene set enrichment analysis(GSEA)showed that the up-regulated DEGs were significantly enriched in the glucose metabolism pathway,while the down-regulated DEGs were enriched in the immune reaction-related pathways.Through cross-analysis of DEGs and CRGs,a total of 65 differentially expressed coagulation-related genes(DE-CRGs)were screened out.GO functional enrichment showed that these genes were mainly located in secreting granular membranes and platelet α-granules,and were involved in physiological processes such as blood coagulation,regulation of body fluid levels,and wound healing.KEGG pathway analysis revealed that these genes were significantly enriched in pathways such as platelet activation,complement and coagulation cascade reactions,Rap1 signaling pathway,and human cytomegalovirus infection.Six key DE-CRGs were identified through machine learning.Receiver operating characteristic(ROC)curve analysis indicated that these genes had good diagnostic efficacy.CIBERSORT analysis revealed a significant correlation between key genes and immune cell infiltration.Patients were classified into two subtypes based on the six key genes:subtype A was rich in CD8+T cells and activated NK cells,presented an immune-active state;subtype B was mainly composed of monocytes and resting NK cells,with insufficient activation of immune pathways.Animal experiments on rats showed that hemorrhagic shock can lead to coagulation dysfunction.The results of qRT-PCR further confirmed that the expression trend of key genes was consistent with the results of bioinformatics analysis.Conclusion In this study,through transcriptomics and machine learning methods,six key genes closely related to TIC were systematically screened out,namely GNA13,PIK3R3,ITGAM,MAPK14,PPP1CC and LYN,and their close connections with coagulation function and immune infiltration were revealed.Animal experiments have further verified the value of these genes as potential diagnostic and therapeutic targets.

Objective To elucidate the mechanisms of trauma-induced coagulopathy(TIC),clarify the specific pathogenic factors and pathophysiological processes,and discover the effective diagnostic indicators and therapeutic targets.Methods Transcriptomic data of traumatic hemorrhagic shock patients were obtained from the Gene Expression Omnibus(GEO)to identify differentially expressed genes(DEGs).Coagulation-related genes(CRGs)from the Kyoto Encyclopedia of Genes and Genomes(KEGG)were intersected with DEGs.Machine learning algorithms,including least absolute shrinkage and selection operator(LASSO)and random forest(RF),were applied to identify key genes.The CIBERSORT algorithm was used to analyze the correlation between key genes and immune cell infiltration.Through consensus clustering,subtype analysis was conducted on trauma patients to compare the infiltration of immune cells.A rat model of traumatic hemorrhagic shock was established to validate coagulation function and the expression of key genes.Results The dataset included samples from 17 healthy controls and 478 patients with traumatic hemorrhagic shock.A total of 6 315 DEGs were identified under the screening criterion of corrected P<0.05.Gene set enrichment analysis(GSEA)showed that the up-regulated DEGs were significantly enriched in the glucose metabolism pathway,while the down-regulated DEGs were enriched in the immune reaction-related pathways.Through cross-analysis of DEGs and CRGs,a total of 65 differentially expressed coagulation-related genes(DE-CRGs)were screened out.GO functional enrichment showed that these genes were mainly located in secreting granular membranes and platelet α-granules,and were involved in physiological processes such as blood coagulation,regulation of body fluid levels,and wound healing.KEGG pathway analysis revealed that these genes were significantly enriched in pathways such as platelet activation,complement and coagulation cascade reactions,Rap1 signaling pathway,and human cytomegalovirus infection.Six key DE-CRGs were identified through machine learning.Receiver operating characteristic(ROC)curve analysis indicated that these genes had good diagnostic efficacy.CIBERSORT analysis revealed a significant correlation between key genes and immune cell infiltration.Patients were classified into two subtypes based on the six key genes:subtype A was rich in CD8+T cells and activated NK cells,presented an immune-active state;subtype B was mainly composed of monocytes and resting NK cells,with insufficient activation of immune pathways.Animal experiments on rats showed that hemorrhagic shock can lead to coagulation dysfunction.The results of qRT-PCR further confirmed that the expression trend of key genes was consistent with the results of bioinformatics analysis.Conclusion In this study,through transcriptomics and machine learning methods,six key genes closely related to TIC were systematically screened out,namely GNA13,PIK3R3,ITGAM,MAPK14,PPP1CC and LYN,and their close connections with coagulation function and immune infiltration were revealed.Animal experiments have further verified the value of these genes as potential diagnostic and therapeutic targets.

Objective Itproposes a WISN staffing demand model for clinical physicians to address the challenge that current physicians staffing standards fail to meet the operational needs of general hospitals.Methods Collect the 2023 annual operation data of a tertiary general hospital,using inpatient visits and surgeries as benchmarks to measure the workload demands of physicians in internal medicine and surgery,respectively.Additionally,integrate non-clinical workloads of physicians and the workload of resident physicians in standardized training to address the limitation of traditional models that focus solely on clinical tasks.Results The good applicability and robustness of the model in practical applications are verified through model comparison and sensitivity analysis,accurately reflecting the demand for physicians.Conclusion The model accurately reflects the workload of physicians in general hospitals,providing a quantitative estimation formula for physicians staffing.It has broad applicability and significant potential for wider adoption.

Objective:To explore the relationship between parenting styles of toddlers aged 30 months and attention deficit hyperactivity disorder (ADHD) symptom trajectories in preschool children.Methods:The subjects were 2 111 children and their parents in Ma'anshan Birth Cohort. Parenting styles were assessed at 30 months of age using the Brief Toddler Parenting Style Questionnaire, which defines parenting styles on four dimensions: warmth and interaction, attention and acceptance, outdoor and social activities, and neglect and restrictions. The Chinese version of the Conners Abbreviated Symptom Questionnaire was used to examine preschooler's ADHD symptoms aged 3, 5, and 6 years. ADHD symptom trajectories were fitted using group-based trajectory modeling. The multinomial logistic regression model was used to evaluate the relationship between parenting styles at 30 months of age and ADHD symptom trajectories in children. The additive and multiplicative models were applied to analyze the interaction effect of negative parenting styles in children's ADHD symptom trajectories.Results:ADHD symptom trajectories in children included the low-score (48.3%), moderate-score (47.7%), and high-score groups (4.0%). After adjusting for the confounding factors, the results of the multinomial logistic regression analysis showed that negative parenting styles of the father/mother were associated with ADHD symptom trajectories in children ( P<0.05). The father/mother's low warmth and interaction (father: OR=2.07,95% CI:1.68-2.54; mother: OR=2.02,95% CI:1.64-2.48), low attention and acceptance (father: OR=1.94,95% CI:1.57-2.39; mother: OR=1.47,95% CI:1.20-1.81), low outdoor and social (father: OR=1.74,95% CI:1.43-2.13; mother: OR=1.60,95% CI:1.32-1.94), and high neglect and restriction (father: OR=2.09,95% CI:1.67-2.62; mother: OR=2.06,95% CI:1.65-2.56) were associated with ADHD symptom moderate-score trajectories compared to the low-score trajectory group, mother's low warmth and interaction, low outdoor and social, and high neglect and restrictions. Statistically significant association was seen in negative parenting styles of fathers with the high-score group. There was a multiplicative interaction for hostile parenting ( P<0.05). The additive interaction results showed that there was a synergistic effect of hostile parenting, which increased the risk of the high-score trajectories of ADHD symptoms in children. Conclusions:Both parents' parenting styles influenced ADHD symptom change the trajectories in preschoolers. Negative parenting styles of the father/mother increase the risk of the moderate-score and high-score trajectories of ADHD symptoms in children. Hostile parenting by both parents has a synergistic impact on the risk of ADHD symptom trajectories in children, and the simultaneous presence of both increases the risk of the high-score trajectory of ADHD symptoms.

Patients suffering from nerve injury often experience exacerbated pain responses and complain of memory deficits. The dorsal hippocampus (dHPC), a well-defined region responsible for learning and memory, displays maladaptive plasticity upon injury, which is assumed to underlie pain hypersensitivity and cognitive deficits. However, much attention has thus far been paid to intracellular mechanisms of plasticity rather than extracellular alterations that might trigger and facilitate intracellular changes. Emerging evidence has shown that nerve injury alters the microarchitecture of the extracellular matrix (ECM) and decreases ECM rigidity in the dHPC. Despite this, it remains elusive which element of the ECM in the dHPC is affected and how it contributes to neuropathic pain and comorbid cognitive deficits. Laminin, a key element of the ECM, consists of α-, β-, and γ-chains and has been implicated in several pathophysiological processes. Here, we showed that peripheral nerve injury downregulates laminin β1 (LAMB1) in the dHPC. Silencing of hippocampal LAMB1 exacerbates pain sensitivity and induces cognitive dysfunction. Further mechanistic analysis revealed that loss of hippocampal LAMB1 causes dysregulated Src/NR2A signaling cascades via interaction with integrin β1, leading to decreased Ca²⁺ levels in pyramidal neurons, which in turn orchestrates structural and functional plasticity and eventually results in exaggerated pain responses and cognitive deficits. In this study, we shed new light on the functional capability of hippocampal ECM LAMB1 in the modulation of neuropathic pain and comorbid cognitive deficits, and reveal a mechanism that conveys extracellular alterations to intracellular plasticity. Moreover, we identified hippocampal LAMB1/integrin β1 signaling as a potential therapeutic target for the treatment of neuropathic pain and related memory loss.
Animals ; Laminin/genetics* ; Hippocampus/metabolism* ; Neuralgia/metabolism* ; Cognitive Dysfunction/etiology* ; Male ; Peripheral Nerve Injuries/metabolism* ; Extracellular Matrix/metabolism* ; Integrin beta1/metabolism* ; Pyramidal Cells/metabolism* ; Signal Transduction

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

In this study, lipopolysaccharide(LPS), ovalbumin(OVA), and compound 48/80(C48/80) were administered to establish non-infectious pneumonia models under simulated clinical conditions, and the correlation between their pathological characteristics and traditional Chinese medicine(TCM) syndromes was compared, providing the basis for the selection of appropriate animal models for TCM efficacy evaluation. An acute pneumonia model was established by nasal instillation of LPS combined with intraperitoneal injection for intensive stimulation. Three doses of OVA mixed with aluminum hydroxide adjuvant were injected intraperitoneally on days one, three, and five and OVA was administered via endotracheal drip for excitation on days 14-18 to establish an OVA-induced allergic pneumonia model. A single intravenous injection of three doses of C48/80 was adopted to establish a C48/80-induced pneumonia model. By detecting the changes in peripheral blood leukocyte classification, lung tissue and plasma cytokines, immunoglobulins(Ig), histamine levels, and arachidonic acid metabolites, the multi-dimensional analysis was carried out based on pathological evaluation. The results showed that the three models could cause pulmonary edema, increased wet weight in the lung, and obvious exudative inflammation in lung tissue pathology, especially for LPS. A number of pyrogenic cytokines, inclading interleukin(IL)-6, interferon(IFN)-γ, IL-1β, and IL-4 were significantly elevated in the LPS pneumonia model. Significantly increased levels of prostacyclin analogs such as prostaglandin E2(PGE2) and PGD2, which cause increased vascular permeability, and neutrophils in peripheral blood were significantly elevated. The model could partly reflect the clinical characteristics of phlegm heat accumulating in the lung or dampness toxin obstructing the lung. The OVA model showed that the sensitization mediators IgE and leukotriene E4(LTE4) were increased, and the anti-inflammatory prostacyclin 6-keto-PGF2α was decreased. Immune cells(lymphocytes and monocytes) were decreased, and inflammatory cells(neutrophils and basophils) were increased, reflecting the characteristics of "deficiency", "phlegm", or "dampness". Lymphocytes, monocytes, and basophils were significantly increased in the C48/80 model. The phenotype of the model was that the content of histamine, a large number of prostacyclins(6-keto-PGE1, PGF2α, 15-keto-PGF2α, 6-keto-PGF1α, 13,14-D-15-keto-PGE2, PGD2, PGE2, and PGH2), LTE4, and 5-hydroxyeicosatetraenoic acid(5S-HETE) was significantly increased, and these indicators were associated with vascular expansion and increased vascular permeability. The pyrogenic inflammatory cytokines were not increased. The C48/80 model reflected the characteristics of cold and damp accumulation. In the study, three non-infectious pneumonia models were constructed. The LPS model exhibited neutrophil infiltration and elevated inflammatory factors, which was suitable for the efficacy study of TCM for clearing heat, detoxifying, removing dampness, and eliminating phlegm. The OVA model, which took allergic inflammation as an index, was suitable for the efficacy study of Yiqi Gubiao formulas. The C48/80 model exhibited increased vasoactive substances(histamine, PGs, and LTE4), which was suitable for the efficacy study and evaluation of TCM for warming the lung, dispersing cold, drying dampness, and resolving phlegm. The study provides a theoretical basis for model selection for the efficacy evaluation of TCM in the treatment of pneumonia.
Animals ; Disease Models, Animal ; Mice ; Pneumonia/genetics* ; Medicine, Chinese Traditional ; Male ; Humans ; Cytokines/immunology* ; Female ; Lipopolysaccharides/adverse effects* ; Lung/drug effects* ; Drugs, Chinese Herbal ; Ovalbumin ; Mice, Inbred BALB C

Retrieving keyframes most relevant to text from small intestine videos with given labels can efficiently and accurately locate pathological regions. However, training directly on raw video data is extremely slow, while learning visual representations from image-text datasets leads to computational inconsistency. To tackle this challenge, a small bowel video keyframe retrieval based on multi-modal contrastive learning (KRCL) is proposed. This framework fully utilizes textual information from video category labels to learn video features closely related to text, while modeling temporal information within a pretrained image-text model. It transfers knowledge learned from image-text multimodal models to the video domain, enabling interaction among medical videos, images, and text data. Experimental results on the hyper-spectral and Kvasir dataset for gastrointestinal disease detection (Hyper-Kvasir) and the Microsoft Research video-to-text (MSR-VTT) retrieval dataset demonstrate the effectiveness and robustness of KRCL, with the proposed method achieving state-of-the-art performance across nearly all evaluation metrics.
Humans ; Video Recording ; Intestine, Small/diagnostic imaging* ; Machine Learning ; Image Processing, Computer-Assisted/methods* ; Algorithms