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 evaluate the reporting quality of randomized controlled trials on acupuncture cessation by using CONSORT statement and STRICTA checklist.Methods According to inclusion and exclusion criteria,9 Chinese and English databases such as CNKI and PubMed were searched by computer,and the retrieved literatures were evaluated according to the CONSORT statement 2010 and STRICTA checklist 2010.Results After exclusion,34 Chinese and English literatures were included,including 27 in Chinese and 7 in English.In a risk review using ROB2,the Cochrane Collaboration's bias risk assessment tool,29 reviews were judged to be high risk overall,2 to be at some risk,and 3 to be at low risk.In the CONSORT statement evaluation,the reporting rate of 23 items was less than 76.4%,including the reporting rate of 17 items was less than 47%.In the STRICTA checklist evaluation,the reporting rate of 9 items was less than 76.4%,including the reporting rate of 4 items was less than 29.4%.Conclusion There are still shortcomings in the methodological design and item reporting standards of acupuncture smoking cessation RCT.It is suggested that researchers should strictly follow the CONSORT declaration and STRICTA list in designing RCT schemes and reporting RCT results in the future.

Under the background of rapid development of artificial intelligence(AI),this paper systematically proposes AI-based education(AIBE).It empowers the teaching process,learning process,research process,and teaching management with AI,and constructs an AI-based educational paradigm,including AI-based teaching(AIBT),AI-based learning(AIBL),AI-based re-search(AIBR),and AI-based management(AIBM).Taking the AI course of immunology teaching as an example,this paper deeply analyzes the practices and explorations of implementing AIBT,AIBL,AIBR and AIBM based on the AI course,so as to accelerate the promotion of the transformation of the fourth generation of medical education.

Thousands of variant gene sequences of SARS-CoV-2 have been emerged since the COVID-19 epidemic broke out in Dec.2019.Alpha(B.1.1.7),Beta(B.1.351),Gamma(P.1),Delta(B.1.617.2)and Omicron(B.1.1.529)were the most representative variants.With the continuous emergence of new variants,the predomi-nant strain in the global pandemic as of Jan.2025 is the Omicron BA.2.86-derived mutant,JN.1.The effectiveness of drugs against Omicron variants remains a key research focus in the treatment of SARS-CoV-2 infections.The infectiousness and pathogenicity of the variants altered remarkably due to mutations in the genome on S protein of the mutant strains,and these emerging variants are more likely to evade immunity and were more infectious than the previous prevalent variants.During the process of combating with the constantly emerging novel variants,drugs showed various effects on treatment of diseases caused by different variants.New drugs and treatment coun-termeasures are constantly updated with the prevalence of various variants.The current status of research on pres-ent drugs for treatment of SARS-CoV-2 and the therapeutic effects on emerging variants are reviewed in the article so as to provide reference for prevention and treatment of the upcoming evolved variants.

The"capitation payment with retained surplus and shared accountability for reasonable overruns"mechanism constitutes a pivotal institutional framework for advancing the high-quality development of County Medical Communities(CMCs).This study addresses two critical operational challenges:identifying the sources of medical insurance fund surplus and optimizing the governance of fund retention processes.Grounded in transaction cost theory,we develop an analytical framework examining the formation of medical insurance fund surplus through the dual lenses of intra-organizational dynamics within CMCs and external medical insurance payment mechanism design.Utilizing Deqing County,Zhejiang Province as an empirical case,this research proposes a five-pronged strategy:Clarifying generation channels of insurance fund surplus,scientifically determining regional medical insurance budgets,implementing bundled payment mechanisms for CMCs,adopting hybrid payment models integrating unified and differentiated approaches,and establishing performance-based incentive systems.These findings elucidate the formative pathways of medical insurance fund surplus while offering theoretical and practical insights for enhancing payment system reforms to support CMC development.