ObjectiveTo explore the potential mechanism of Xiaoqinglongtang（XQL） in the prevention and treatment of high altitude pulmonary edema（HAPE） by network pharmacology， molecular docking， and molecular dynamics simulation， and to verify it by in vivo animal model. MethodsIn this study， the active ingredients， drug targets， and HAPE-related targets of XQL were collected from BATMAN-TCM， GeneCards， and Online Mendelian Inheritance in Man（OMIM） databases. The protein-protein interaction（PPI） network was constructed by using intersection targets， and the core targets were screened and visualized by Cytoscape software. Functional annotation and pathway analysis of the intersection targets were performed by gene ontology （GO） and Kyoto encyclopedia of genes and genomes （KEGG） functional enrichment. AutoDock and GROMACS were used to evaluate the binding ability of active ingredients to key targets. In the experimental verification part， a mouse model of HAPE induced by hypobaric hypoxia（simulated 6 000 m altitude for 48 h） was established. The control effect was evaluated by hematoxylin-eosin（HE） staining， lung tissue water content， lung tissue wet/dry weight ratio， real-time quantitative polymerase chain reaction（Real-time PCR） detection of gene expression levels， and immunohistochemistry and Western blot detection of key protein expression. ResultsA total of 355 active ingredients of XQL， 2 142 targets， 716 HAPE-related targets， and 236 intersection targets were obtained by network pharmacology analysis. Key core targets such as interleukin （IL）-6， tumor necrosis factor （TNF）， protein kinase B1 （Akt1）， and hypoxia-inducible factor-1α （HIF-1α） were screened. The results of GO analysis of common targets involved 738 biological processes（BP）， 72 cellular components（CC）， and 135 molecular functions（MF）. KEGG analysis effectively enriched two important signaling pathways： Phosphoinositol 3-kinase （PI3K）/Akt and HIF-1α. The results of molecular docking and molecular dynamics simulation showed that the screened active ingredients had good binding ability with key targets. In the HAPE model induced by hypobaric hypoxia（6 000 m， 48 h）， the lung tissue water content， lung tissue wet/dry weight ratio， and pathological injury score of the model group were significantly increased（P<0.01）， accompanied by exudation of a large number of red blood cells in the alveoli and alveolar interstitium， a significant increase in inflammatory cells， a significant widening of the alveolar septum， and mutual fusion between the alveoli. The XQL administration group significantly improved the above pathological changes（P<0.01）. The results of inflammatory factor expression showed that compared with the control group， the model group showed significantly up-regulated expression of TNF-α， IL-6， and IL-1β in the lung tissue（P<0.01）. Compared with the model group， the XQL administration group had significantly decreased expression of inflammatory factors（P<0.05， P<0.01）. The mRNA expression of key pathway related genes PI3K， Akt1， mammalian target of rapamycin（mTOR）， and HIF-1α was significantly increased in the model group（P<0.01）， and decreased in a concentration-dependent manner after XQL administration（P<0.05， P<0.01）. The expression levels of key proteins PI3K， phosphorylation（p）-PI3K， Akt1， p-Akt1， mTOR， p-mTOR， and HIF-1α in lung tissue were analyzed by immunohistochemistry and Western blot. Compared with the blank group， the model group showed increased expression of key proteins（P<0.05， P<0.01）. Compared with the model group， the XQL administration group exhibited decreased expression of key proteins（P<0.05， P<0.01）. ConclusionXQL can reduce lung inflammation and improve HAPE. The mechanism may be related to the regulation of PI3K/Akt/mTOR and HIF-1α pathways. This study provides a new idea and a theoretical basis for the treatment of HAPE with XQL.

ObjectiveTo explore the potential mechanism of Xiaoqinglongtang（XQL） in the prevention and treatment of high altitude pulmonary edema（HAPE） by network pharmacology， molecular docking， and molecular dynamics simulation， and to verify it by in vivo animal model. MethodsIn this study， the active ingredients， drug targets， and HAPE-related targets of XQL were collected from BATMAN-TCM， GeneCards， and Online Mendelian Inheritance in Man（OMIM） databases. The protein-protein interaction（PPI） network was constructed by using intersection targets， and the core targets were screened and visualized by Cytoscape software. Functional annotation and pathway analysis of the intersection targets were performed by gene ontology （GO） and Kyoto encyclopedia of genes and genomes （KEGG） functional enrichment. AutoDock and GROMACS were used to evaluate the binding ability of active ingredients to key targets. In the experimental verification part， a mouse model of HAPE induced by hypobaric hypoxia（simulated 6 000 m altitude for 48 h） was established. The control effect was evaluated by hematoxylin-eosin（HE） staining， lung tissue water content， lung tissue wet/dry weight ratio， real-time quantitative polymerase chain reaction（Real-time PCR） detection of gene expression levels， and immunohistochemistry and Western blot detection of key protein expression. ResultsA total of 355 active ingredients of XQL， 2 142 targets， 716 HAPE-related targets， and 236 intersection targets were obtained by network pharmacology analysis. Key core targets such as interleukin （IL）-6， tumor necrosis factor （TNF）， protein kinase B1 （Akt1）， and hypoxia-inducible factor-1α （HIF-1α） were screened. The results of GO analysis of common targets involved 738 biological processes（BP）， 72 cellular components（CC）， and 135 molecular functions（MF）. KEGG analysis effectively enriched two important signaling pathways： Phosphoinositol 3-kinase （PI3K）/Akt and HIF-1α. The results of molecular docking and molecular dynamics simulation showed that the screened active ingredients had good binding ability with key targets. In the HAPE model induced by hypobaric hypoxia（6 000 m， 48 h）， the lung tissue water content， lung tissue wet/dry weight ratio， and pathological injury score of the model group were significantly increased（P<0.01）， accompanied by exudation of a large number of red blood cells in the alveoli and alveolar interstitium， a significant increase in inflammatory cells， a significant widening of the alveolar septum， and mutual fusion between the alveoli. The XQL administration group significantly improved the above pathological changes（P<0.01）. The results of inflammatory factor expression showed that compared with the control group， the model group showed significantly up-regulated expression of TNF-α， IL-6， and IL-1β in the lung tissue（P<0.01）. Compared with the model group， the XQL administration group had significantly decreased expression of inflammatory factors（P<0.05， P<0.01）. The mRNA expression of key pathway related genes PI3K， Akt1， mammalian target of rapamycin（mTOR）， and HIF-1α was significantly increased in the model group（P<0.01）， and decreased in a concentration-dependent manner after XQL administration（P<0.05， P<0.01）. The expression levels of key proteins PI3K， phosphorylation（p）-PI3K， Akt1， p-Akt1， mTOR， p-mTOR， and HIF-1α in lung tissue were analyzed by immunohistochemistry and Western blot. Compared with the blank group， the model group showed increased expression of key proteins（P<0.05， P<0.01）. Compared with the model group， the XQL administration group exhibited decreased expression of key proteins（P<0.05， P<0.01）. ConclusionXQL can reduce lung inflammation and improve HAPE. The mechanism may be related to the regulation of PI3K/Akt/mTOR and HIF-1α pathways. This study provides a new idea and a theoretical basis for the treatment of HAPE with XQL.

ObjectiveTo observe the activation patterns and functional connectivity in the prefrontal cortex of patients with post-stroke anxiety (PSA) using functional near-infrared spectroscopy, in order to explore the underlying neural mechanism. MethodsFrom December, 2024 to September, 2025, 120 stroke patients were selected in Changzhou De'an Hospital. They were divided into PSA group (n = 60) and non-PSA group (n = 60) according to the score of Hamilton Anxiety Scale (HAMA). All patients wore an 18-channel fNIRS acquisition cap for detection. The differences in resting-state functional connectivity between the frontopolar cortex (FPC) and dorsolateral prefrontal cortex (DLPFC) were examined in both groups, as well as task-related activation in these brain regions. ResultsResting-state functional connectivity analysis revealed no statistically significant difference in network connectivity between two groups in the FPC and DLPFC regions (|t| < 1.301, P > 0.05). Task-related activation results revealed significantly reduced activation in the contralateral FPC of PSA group compared to the non-PSA group (Z = -2.063, P < 0.05). Activation levels in this region showed a negative correlation with the scores of HAMA (ρ = -0.201, P = 0.028). ConclusionActivation decreased in the contralateral frontal pole during the task state for patients with PSA, and the activation levels negatively correlates with anxiety severities.

ObjectiveTo observe the activation patterns and functional connectivity in the prefrontal cortex of patients with post-stroke anxiety (PSA) using functional near-infrared spectroscopy, in order to explore the underlying neural mechanism. MethodsFrom December, 2024 to September, 2025, 120 stroke patients were selected in Changzhou De'an Hospital. They were divided into PSA group (n = 60) and non-PSA group (n = 60) according to the score of Hamilton Anxiety Scale (HAMA). All patients wore an 18-channel fNIRS acquisition cap for detection. The differences in resting-state functional connectivity between the frontopolar cortex (FPC) and dorsolateral prefrontal cortex (DLPFC) were examined in both groups, as well as task-related activation in these brain regions. ResultsResting-state functional connectivity analysis revealed no statistically significant difference in network connectivity between two groups in the FPC and DLPFC regions (|t| < 1.301, P > 0.05). Task-related activation results revealed significantly reduced activation in the contralateral FPC of PSA group compared to the non-PSA group (Z = -2.063, P < 0.05). Activation levels in this region showed a negative correlation with the scores of HAMA (ρ = -0.201, P = 0.028). ConclusionActivation decreased in the contralateral frontal pole during the task state for patients with PSA, and the activation levels negatively correlates with anxiety severities.

ObjectiveTo evaluate the public health governance capacity in Jiangsu Province and provide an optimized pathway for the construction of a “strong, rich, beautiful, and high-quality” new Jiangsu. MethodsA total of 806 policy documents, 658 public information reports, and 148 research literatures related to public health governance capacity in Jiangsu Province from January 1995 to December 2023 were collected. The status of current public health goverance was assessed based on the evaluation criteria suitable for public health systems, and the strengths and the weaknesses of the system were identified. ResultsThe public health governance capability of Jiangsu Province was scored at 738.3 points, ranking 3rd nationally. Maternal health care and emergency response capacities achieved leading positions nationwide, both ranking 2nd. Jiangsu had exhibited a standardized guidance in the strategic level, a well-established management mechanism, an extensive coverage in information collection, and a scientifically established health targets setting. However, bottlenecks remained, including an unclear division of responsibilities across organizational departments, an insufficient public-health workforce, the absence of a stable growth mechanism for government funding investment, and difficulties in promptly identifying public needs. ConclusionJiangsu’s public-health system demonstrates leading nationally, yet several components remain underdeveloped. Future efforts should consolidate advantages while addressing weaknesses, further diversify content and forms, establish a stable funding increase mechanism, and clarify departmental functions, thereby providing solid health support for realizing the developmental goals of a “strong, rich, beautiful and high-quality” new Jiangsu.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

ObjectiveTo observe the activation patterns and functional connectivity in the prefrontal cortex of patients with post-stroke anxiety (PSA) using functional near-infrared spectroscopy, in order to explore the underlying neural mechanism. MethodsFrom December, 2024 to September, 2025, 120 stroke patients were selected in Changzhou De'an Hospital. They were divided into PSA group (n = 60) and non-PSA group (n = 60) according to the score of Hamilton Anxiety Scale (HAMA). All patients wore an 18-channel fNIRS acquisition cap for detection. The differences in resting-state functional connectivity between the frontopolar cortex (FPC) and dorsolateral prefrontal cortex (DLPFC) were examined in both groups, as well as task-related activation in these brain regions. ResultsResting-state functional connectivity analysis revealed no statistically significant difference in network connectivity between two groups in the FPC and DLPFC regions (|t| < 1.301, P > 0.05). Task-related activation results revealed significantly reduced activation in the contralateral FPC of PSA group compared to the non-PSA group (Z = -2.063, P < 0.05). Activation levels in this region showed a negative correlation with the scores of HAMA (ρ = -0.201, P = 0.028). ConclusionActivation decreased in the contralateral frontal pole during the task state for patients with PSA, and the activation levels negatively correlates with anxiety severities.

ObjectiveTo analyze the changes in the degree of coordination of China's major epidemic prevention and control efforts before and after the outbreak of the Corona Virus Disease 2019 (COVID-19), so as to explore the impact of epidemic prevention and control measures on coordination dynamics. MethodsA total of 3 864 policy documents related to epidemic prevention and control from January 2000 to December 2020 across 31 provinces (autonomous regions, and municipalities) in China were systematically collected. Contents specific to collaborative and cooperative efforts were extracted, and the extent of interdepartmental coordination were quantified to assess the effectiveness of epidemic prevention and control efforts. Wilcoxon signed-rank test was adopted to statistically analyze the differences between the indicators before and after the epidemic. ResultsThe average overall coordination level for major epidemic prevention and control in 31 provinces (autonomous regions, and municipalities) increased from 43.06% to 97.62%, and the average coordination levels in the eastern, central, and western China soared from 42.29%, 37.50%, and 47.46%, to 98.81%, 96.20%, and 97.46%, respectively, with statistically significant differences (all P<0.05). In terms of department categorization, coordination levels in the professional departments and the key support departments peaked at 100.00%, while other support departments rose to 95.43%, with an increase of 77.15%, 181.85%, and 139.89%, respectively, exhibiting noteworthy statistically significant differences (all P<0.001). ConclusionThe scope of coordination departments of China’s major epidemic prevention and control exists a remarkable surge following the COVID-19 outbreak, notable heightened coordination is particularly observed among the key support departments. Future endeavors should prioritize the roles played by diverse departments in epidemic prevention and control, enhancing both the clarity of departmental responsibilities and the effectiveness of interdepartmental coordination.

ObjectiveTo systematically evaluate the public health governance capacity of 40 cities in Jiangsu, Zhejiang, and Anhui Provinces, providing a scientific evaluation basis for building a "Healthy Yangtze River Delta". MethodsA comprehensive collection of policy documents, public information reports, and research literature related to public health governance capacity in Jiangsu, Zhejiang, and Anhui Provinces was conducted, totaling 6 920 policy documents, 1 720 information reports, and 1 200 literature pieces. Based on the evaluation standards for an appropriate public health system established by the research team, the basic status of public health governance capacity was assessed to identify the strengths and weaknesses of the 40 cities. ResultsIn 2022, the public health governance capacity score for the 40 cities in Jiangsu, Zhejiang, and Anhui Provinces was (562.5±38.0) points. In terms of specific areas, the emergency response field received the highest score of (791.4±49.7) points, while the chronic disease prevention and control field received the lowest score of (368.2±29.6) points. The Jiangsu-Zhejiang-Anhui region has largely achieved the strategic priority of health, gradually improved public health legal regulations, and established a basic organizational framework with a solid foundation for information and data infrastructure. However, challenges still need to be addressed, such as unstable government funding for public health, unclear departmental responsibilities, and barriers to information interoperability. ConclusionThe public health governance capacity of the 40 cities in Jiangsu, Zhejiang, and Anhui Province has been at a moderate level, but disparities have still existed across regions and fields. In the future, while continuing to deepen existing advantages, it is essential to accurately identify the causes of problems, establish a long-term and stable investment mechanism, enhance information connectivity mechanisms, further clarify departmental responsibilities, and promote the achievement of the "Healthy Yangtze River Delta" goal.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.