Mainly based on the "constitution-syndrome-disease location correlation" approach， this study constructs a traditional Chinese medicine （TCM） framework for understanding hypertension comorbidities. From the perspective of shared constitutional predispositions and similar pathomechanistic characteristics， hypertension comorbidity should not be regarded as a simple aggregation of multiple diseases. Rather， it is generally rooted in biased constitutions such as yin-deficiency， qi-deficiency， yang-deficiency， phlegm-dampness， dampness-heat， and blood-stasis constitutions， accompanied by the long-term interplay of pathogenic mechanisms involving wind， fire， phlegm， stasis， and deficiency， and gradually manifests as comorbidities affecting different systems through the evolution of disease locations related to the liver， heart， spleen， and kidney. On this basis， cardiac-， cerebral-， renal-， and metabolic-related comorbidities are taken as the major categories， and their characteristics related to constitution， syndrome， and disease location are systematically summarized to clarify the correspondence between shared pathomechanisms and predominant disease locations， and to propose corresponding prevention and treatment strategies. This framework further incorporates constitution-based stratification into risk stratification for prospective intervention， and takes syndrome as the central link in developing multi-disease treatment strategies such as boosting qi and activating blood， as well as resolving phlegm and unblocking collaterals. A clinical pathway is thereby proposed， encompassing constitution assessment， syndrome differentiation， comorbidity risk stratification， integrated traditional Chinese and western medicine intervention， and follow-up feedback. This study aims to provide a reference for the theoretical development and collaborative management of hypertension comorbidities in TCM.

Objective To explore the relationship between intestinal flora imbalance and pulmonary function in patients with chronic obstructive pulmonary disease (COPD), and to analyze the related influencing factors. Methods A total of 310 patients with COPD who were admitted to Air Force Military Medical University Tangdu Hospital from June 2022 to December 2024 were retrospectively analyzed. Based on intestinal flora status, the enrolled patients were classified into imbalance group (n=83) and non-imbalance group (n=227). Logistic regression analysis was conducted to analyze the independent related factors of intestinal flora imbalance in COPD patients. Based on the above factors, a prediction model was constructed, and ROC curve analysis model was applied to analyze the predictive value of the model on intestinal flora imbalance. Results Logistic regression analysis revealed that age, IL-6, albumin, pulmonary function, long-term bed rest and long-term use of antibiotics were related to intestinal flora imbalance in COPD patients (all P<0.05). ROC results of the Logistic prediction model showed that the area under the curve, sensitivity, specificity and 95%CI were 0.961, 0.880, 0.996 and 0.932-0.989 respectively. Conclusion The intestinal flora imbalance in patients with COPD is closely related to lung function, age, inflammatory status, nutritional indicators, activity ability and antibiotic use.

ObjectiveTo provide evidence for strengthening tuberculosis control in Inner Mongolia by analyzing changes and influencing factors in public awareness of tuberculosis prevention and control information among residents between the early stage and 17 years after the implementation of the directly observed treatment， short-course （DOTS） strategy. MethodsBased on the "National Public Knowledge， Belief and Behavior Questionnaire on Tuberculosis Prevention and Control" designed by the Chinese Center for Disease Control and Prevention， a questionnaire survey was conducted among residents in Inner Mongolia using a multi-stage stratified random sampling method. Chi-square test was used for univariate analysis， and binary Logistic regression was employed to explore the influencing factors of public awareness of tuberculosis prevention and control information. ResultsThe overall awareness rate of core information on tuberculosis prevention and control among the public was 67.7% in 2006， and it decreased to 49.2% in 2023 （P<0.05）. Multivariate Logistic regression analysis showed， compared with the groups aged 15-29， illiterate and semi-illiterate， those with public medical care， and those with three or fewer family members， the awareness rate of the groups aged 60 and above （OR=0.689）， those with primary and junior high school education （OR=0.856）， and those with self-paid medical care （OR=0.468） was significantly lower in 2006. The awareness rate was relatively high in groups with more than 3 family members （OR=1.236） （P<0.05）. Compared with the groups aged 15-29， illiterate and semi-illiterate， married， Han ethnicity， employed， and those receiving public medical care， the awareness rate was higher among the groups aged 30-59 （OR=1.976）， 60 and above （OR=2.224）， those with high school education and above （OR=2.801）， and single （OR=2.244） in 2023. However， the awareness rates of ethnic minorities （OR=0.737）， the unemployed （OR=0.557）， and self-funded medical care （OR=0.497） groups were significantly lower （P<0.05）. ConclusionPublic awareness of TB prevention knowledge in Inner Mongolia remains suboptimal. Strengthening diversified health education campaigns， addressing social stigma， and improving healthcare access are critical to enhance regional TB control outcomes.

Autism spectrum disorder （ASD）， also known as autism， is a series of neurodevelopmental disorders characterized by social disorders and repetitive stereotyped behaviors/narrow interests. Its pathogenesis is complex， and there is a lack of effective treatment drugs， with some cases having adverse outcomes. Recent studies have consistently revealed that individuals with autism spectrum disorder （ASD） commonly exhibit characteristics such as gut microbiota dysbiosis （abnormal Bacteroidetes/Firmicutes ratio）， impaired intestinal barrier function （elevated serum levels of zonulin and LPS）， and intestinal immune dysregulation （increased pro-inflammatory cytokines including IL-6 and TNF-α）， suggesting that gastrointestinal abnormalities may influence central nervous system development through neuroendocrine， immunoregulatory， and metabolic pathways. Consequently， growing scholarly attention has focused on dietary interventions as potential approaches to alleviate clinical symptoms in children with ASD. This review systematically summarizes the role of gut microbiota and their metabolite alterations in ASD pathogenesis， along with recent advancements in understanding the microbiota-gut-brain axis mechanisms. Additionally， it elaborates on the therapeutic effects and underlying biological basis of restrictive diet therapy， modified diet therapy， and nutritional supplementation therapy in promoting the health of children with ASD. This systematic review reveals that children with ASD exhibit significant gut microbiota dysbiosis （e.g.， increased Clostridium， decreased Faecalibacterium） and abnormal metabolite profiles （e.g.， altered short-chain fatty acid spectra， elevated 4EPS levels）. These alterations exacerbate neuroinflammation and immune dysregulation through the microbiota-gut-brain axis， thereby impacting nervous system development and function. Furthermore， interventions such as ketogenic diets， camel milk， and specific nutritional supplements can alleviate certain ASD symptoms by modulating gut microbiota， restoring intestinal barrier function， and improving metabolic pathways. Future investigations should aim to create multi-omics evaluation systems for pinpointing potential beneficiaries， devise individualized intervention strategies rooted in microbiome characteristics， and verify their therapeutic value and safety in large-scale randomized controlled trials. These efforts are crucial to transitioning ASD treatment from symptomatic control to address disease etiology， thereby paving the way for improving prognoses.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo conduct an epidemiological investigation and analysis of a clustered scabies outbreak in a middle school, so as to provide evidence for developing targeted prevention and control strategies. MethodsCase data on a clustered scabies outbreak occurring in a middle school of Shaanxi Province in 2025 were collected. Field epidemiological approaches were adopted to define cases, search for infected individuals, and describe the temporal, spatial, and population distribution. Specimens were collected from individuals and surrounding environments for laboratory testing. ResultsA total of 25 scabies cases were identified in 4 classes and 9 dormitories of the school. All cases were first-year high school students. The overall attack rate was 6.00% (25/414); the attack rate was 10.53% (22/209) among male students and 1.46% (3/205) among female students. The disease course ranged from 10 to 30 days, with a median duration of 14 days, and 3 students (12.00%) continued attending classes with ongoing infection. Skin scrapings collected from lesion sites of 3 patients were examined under microscopy, all of which were positive for Sarcoptes scabiei; no laboratory testing was performed for the remaining cases. This clustered scabies infection outbreak lasted for one month. All affected students recovered completely without severe complications or deaths, and the outbreak was effectively contained. ConclusionThis scabies infection is a clustered school-based scabies outbreak caused by Sarcoptes scabiei. Affected patients present relatively severe symptoms and a prolonged disease course. Scientific and standardized preventive systems should be established to enable timely early warning and emergency response.

Objective To investigate the prognostic value of albumin-corrected anion gap (ACAG) in patients with advanced lung cancer receiving immune checkpoint inhibitors (ICIs). Methods A total of 637 patients with advanced lung cancer who received ICI treatment were enrolled. Baseline clinical data and laboratory parameters were collected, and ACAG was calculated. The optimal prognostic Cut-off value for ACAG (11.58 mmol/L) was determined using X-Tile software, and patients were divided into low (≤11.58 mmol/L) and high (>11.58 mmol/L) ACAG groups. Propensity score matching was performed to control for confounding factors. Survival curves were plotted using the Kaplan-Meier method. A Cox proportional hazards regression model was employed to analyze prognostic factors, and subgroup analyses were conducted. Results Among 637 patients, 440 patients remained in the matched cohort. Multivariate Cox analysis showed that after adjusting for clinical and laboratory parameters, ACAG >11.58 mmol/L was an independent risk factor for OS (HR=1.74, 95%CI: 1.02–2.98, P=0.044) but was not an independent predictor for PFS. After adjusting for different sets of covariates, stratified analysis further confirmed that ACAG >11.58 mmol/L was consistently associated with a significantly shortened OS. Survival curves demonstrated that the median OS in the high ACAG group (47.0 months) was significantly lower than that in the low ACAG group. Subgroup analysis indicated that the predictive value of ACAG for OS was more pronounced in males, patients aged <65 years, and patients with squamous cell carcinoma than in other patients. Conclusion In patients with advanced lung cancer treated with ICIs, a high baseline ACAG level (>11.58 mmol/L) is an independent risk factor for shortened OS but is not significantly associated with PFS. ACAG may serve as a simple and effective biological marker for assessing overall survival prognosis in patients with advanced lung cancer undergoing immunotherapy.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

Objective:To investigate the association between serum 25-hydroxyvitamin D [25(OH)D] levels and 24-h urinary calcium excretion (24-h UCaE) and hypercalciuria in Chinese adults.Methods:This cross-sectional study was conducted from March 2022 to March 2023 in nine cities in China and included 1 239 residents. Demographic characteristics were collected through questionnaires and physical examinations, fasting blood samples were assessed for bone metabolism indicators, and 24-h urine samples were used to determine the 24-h UCaE. Multiple linear regression analysis was used to explore the relationship between serum 25(OH)D and 24-h UCaE and bone metabolism indexes. The relationship between serum 25(OH)D and hypercalciuria was analyzed using a multiple logistic regression model combined with restricted cubic spline modeling.Results:The mean participant age was (47.9±18.1) years, of which 453 (36.6%) were male. The percentages of vitamin D sufficiency, insufficiency, and deficiency were 7.6% (94/1 239), 29.0% (359/1 239), and 63.4% (786/1 239), respectively. The multiple linear regression model showed that after adjusting for the covariates the 24-h UCaE gradually increased with higher levels of 25(OH)D ( P overall <0.001, P nonlinear <0.001). The logistic regression analysis revealed that compared with the vitamin D deficient group, the OR for the prevalence of hypercalciuria in the vitamin D sufficient and vitamin D insufficient groups were 3.290 (95% CI 1.745 to 6.202) and 3.742 (95% CI 2.458 to 5.697), respectively. The results of the restricted cubic spline modeling showed a positive nonlinear relationship between 25(OH)D and the prevalence of hypercalciuria ( P overall <0.001, P nonlinear <0.001). The prevalence of hypercalciuria increased when 25(OH)D was >17.00 μg/L and peaked at 26.71 μg/L, after which there was a decreasing trend in the prevalence of hypercalciuria with increasing 25(OH)D. Conclusion:Associations between serum 25(OH)D levels and urinary calcium excretion and the prevalence of hypercalciuria were observed in the Chinese adult population.

Objective To identify the key factors affecting the risk of medical surges and their coupling relation5 ships,providing strategic support for medical institutions to optimize risk management and emergency governance.Methods 17 influencing factors were determined based on WSR theory,and an expert scoring method was employed to assess the impact strength among the factors.The DEMATEL method was applied to calculate the centrality,cau5 sality,influence,and being influenced degrees of the influencing factors.The ISM method was used to construct a hierarchical structure of the influencing factors related to medical surge risks,thereby revealing the connections and interaction mechanisms among these factors.Results Seven critical influencing factors were identified,including the crisis decision-making capacity and leadership effectiveness of emergency managers,the completeness of the emer5 gency system and dynamic execution capabilities,and the cross-departmental coordination mechanism and com5 mand collaboration efficiency.Deep driving factors and coupling pathways were also revealed.Conclusion The risk of medical surges exhibits multi-factorial coupling cascade effects;attention should be directed towards the construc5 tion of mid-to-deep level mechanisms such as information systems,institutional frameworks,and organizational management,to enhance targeted capabilities and systemic resilience in risk governance.