ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

Objective To systematically sort out the current situation of animal model construction and evaluation of damp-heat syndrome of spleen and stomach under the pattern of combining disease and evidence,references for optimizing the standardized research of this syndrome were provided.Methods The references in the past 20 years from CNKI,Wanfang,VIP,PubMed and other databases were searched to summarize and analyze the modeling ideas,evaluation systems and problems of the animal models of spleen-stomach damp-heat syndrome.Results Most of the existing models used high-fat and high-sugar feeds combined with hot and humid environment exposure,compound pathogenic microbial infection or chemical stimulation to construct the combined disease and evidence model.The model validity was evaluated by macroscopic signs,gastrointestinal function indexes,inflammatory factors,and intestinal bacterial flora,etc.However,there were still problems of insufficient stability of the model,and inconsistencies in the criteria for evaluation of the evidence.Conclusion The combination of disease and evidence model can better simulate the clinical characteristics of spleen and stomach damp-heat syndrome,but it is necessary to strengthen the construction of multimodal evaluation system and molecular mechanism research.The standardization of animal models can be promoted through the integration of multidisciplinary technology,which can provide a more accurate method for the modernization of traditional Chinese medicine research.

One patient who underwent mechanical aortic valve replacement was in good condition after surgery,and was discharged with warfarin(2.25 mg·d-1 and 3 mg·d-1 alternatively)and INR 1.91 under the guidance of the pharmacist.After discharge,in addition to taking warfarin and other medication for heart failure treatment,he purchased Huangjing(astragalus)o-ral liquid at the pharmacy and took it everyday.Twelve days later,he developed recurrent gingival bleeding and INR was 4.95.Warfarin and astragalus oral liquid were stopped immediately,and warfarin was restarted 3 days later.INR was monitored and the dose was adjusted.The maintenance dose of warfarin was 3 mg·d-1 and the INR was around 2.0.It was considered that the abnor-mally elevated INR was caused by the interaction between warfarin and astragalus extract oral solution.

Objective:To investigate impact of farrerol(Far)on LPS-induced microglial inflammatory injury by regulating cyclic guanosine monophosphate-adenosine monophosphate synthase(cGAS)-stimulator of interferon genes(STING)signal pathway.Methods:Mice BV2 microglial cell lines were grouped into control group(normal culture),LPS group(1 μg/ml),Far low,medium and high doses groups(1,5,10 μmol/L),activator group(10 μmol/L Far+75 μg/ml cGAS-STING signal pathway activator DMX-AA);proliferation and apoptosis of BV2 cells were detected by MTT,plate cloning assay and flow cytometry;qRT-PCR and ELISA were applied to detect levels of IL-6,IL-1β and TNF-α in cells and supernatants;NO content in cell supernatant was detected by nitrate reductase method;Western blot was applied to detect expressions of proliferating cell nuclear antigen(PCNA),Bcl-2 associated X protein(Bax),anti-apoptotic factor B cell lymphoma protein-2(Bcl-2)and cGAS-STING pathway protein in BV2 cells.Results:Compared with control group,A490 of BV2 cells,number of cloned cells,expressions of PCNA and Bcl-2 proteins in LPS group were decreased,apoptosis rate,mRNA expressions of IL-6,IL-1β,TNF-α,contents of IL-6,IL-1β,TNF-α,NO,and protein expres-sions of Bax,cGAS and STING were increased(P<0.05);compared with LPS group,A490 of BV2 cells,number of cloned cells,expressions of PCNA and Bcl-2 proteins in Far low,medium and high dose groups were increased,apoptosis rate,mRNA expressions of IL-6, IL-1β, TNF-α, contents of IL-6, IL-1β, TNF-α, NO, and protein expressions of Bax, cGAS and STING were decreased (P<0.05); compared with Far high-dose group, A490 of BV2 cells, number of cloned cells, expressions of PCNA and Bcl-2 proteins in activator group were decreased, apoptosis rate, mRNA expressions of IL-6, IL-1β, TNF-α, contents of IL-6, IL-1β, TNF-α, NO, and protein expressions of Bax, cGAS and STING were increased (P<0.05).Conclusion:Far may inhibit apoptosis and inflammatory injury of BV2 cells and promote proliferation of BV2 cells by inhibiting cGAS-STING pathway, and thus alleviate inflammatory injury of BV2 cells induced by LPS.

Objective:To investigate impact of farrerol(Far)on LPS-induced microglial inflammatory injury by regulating cyclic guanosine monophosphate-adenosine monophosphate synthase(cGAS)-stimulator of interferon genes(STING)signal pathway.Methods:Mice BV2 microglial cell lines were grouped into control group(normal culture),LPS group(1 μg/ml),Far low,medium and high doses groups(1,5,10 μmol/L),activator group(10 μmol/L Far+75 μg/ml cGAS-STING signal pathway activator DMX-AA);proliferation and apoptosis of BV2 cells were detected by MTT,plate cloning assay and flow cytometry;qRT-PCR and ELISA were applied to detect levels of IL-6,IL-1β and TNF-α in cells and supernatants;NO content in cell supernatant was detected by nitrate reductase method;Western blot was applied to detect expressions of proliferating cell nuclear antigen(PCNA),Bcl-2 associated X protein(Bax),anti-apoptotic factor B cell lymphoma protein-2(Bcl-2)and cGAS-STING pathway protein in BV2 cells.Results:Compared with control group,A490 of BV2 cells,number of cloned cells,expressions of PCNA and Bcl-2 proteins in LPS group were decreased,apoptosis rate,mRNA expressions of IL-6,IL-1β,TNF-α,contents of IL-6,IL-1β,TNF-α,NO,and protein expres-sions of Bax,cGAS and STING were increased(P<0.05);compared with LPS group,A490 of BV2 cells,number of cloned cells,expressions of PCNA and Bcl-2 proteins in Far low,medium and high dose groups were increased,apoptosis rate,mRNA expressions of IL-6, IL-1β, TNF-α, contents of IL-6, IL-1β, TNF-α, NO, and protein expressions of Bax, cGAS and STING were decreased (P<0.05); compared with Far high-dose group, A490 of BV2 cells, number of cloned cells, expressions of PCNA and Bcl-2 proteins in activator group were decreased, apoptosis rate, mRNA expressions of IL-6, IL-1β, TNF-α, contents of IL-6, IL-1β, TNF-α, NO, and protein expressions of Bax, cGAS and STING were increased (P<0.05).Conclusion:Far may inhibit apoptosis and inflammatory injury of BV2 cells and promote proliferation of BV2 cells by inhibiting cGAS-STING pathway, and thus alleviate inflammatory injury of BV2 cells induced by LPS.

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.

Objective Exploring the components of complex scenarios of healthcare surges triggered by major epidemics to provide a theorical basis for building resilience in healthcare organizations.Methods A hybrid analysis method is used to summarize macro-meso-micro multi-level and multi-source heterogeneous information,extract the elements of complex scenarios of medical surge and evaluate the rationality.Fault Tree Analysis method is used to clarify the logical relationship between various scenario elements and construct scenario reasoning paths.Results 10 scenario states,11 disaster-bearing,24 emergency management and 23 scenario results are summarized and extracted to form the key elements of complex surge scenarios.Among them,M4 expansion and coordinated scheduling of key positions,B2 conventional drug inventory emergency/insufficient core treatment drugs,B emergency medical material transportation breakage,S3 disease symptom spectrum shift to severe disease,R13 prevention and control awareness laxity,and M5 media information dissemination management are the key driving factors that promote a major turning point in the scenario.The most positive scenario result is the orderly operation of the medical service system,and the most negative scenario result is the paralysis of the medical service system.Conclusion Medical institutions need to improve emergency plans based on the complex evolution scenarios of medical surges and agile governance capabilities targeting key turning points,focus on dynamically expanding and scheduling personnel in key positions,strengthen material rotation and reserve mechanisms,maintain smooth emergency logistics channels,and improve efficient management of media and public opinion,so as to comprehensively improve overall resilience.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.

Objective To define the conceptual connotation of network dynamic collaboration capacity in medical surge response and construct its theoretical model.Methods A mixed concept analysis method was employed,integrating multidisciplinary literature and collecting empirical evidence through semi-structured expert interviews to extract the concept of network dynamic collaboration capacity in medical surge response.By integrating complex systems,network science,synergetics,and dynamic capability theory,and combining the interview results,the study used the analogy of flood control in hydraulic engineering to develop a"network-dynamic-collaboration"triangular capacity theoretical model.Results It reveals one antecedents(sudden external shocks have led to an abnormal and continuous surge in medical demand),six core attributes(information interconnection accessibility,dynamic resource adaptability,risk perception responsiveness,multi-party collaborative interactivity,service process adaptability elasticity,and learning iterative evolution),and four consequences(mitigation of crowding risk,protection of service continuity,minimization of crisis spillover,and enhancement of system resilience)for the network dynamic collaboration capacity in medical surge response.The theoretical model elucidates the coupling mechanisms among network structural resilience,dynamic regulation processes,and collaborative co-evolution in resisting medical surge.Conclusion The new concept and theoretical model proposed in this study deepen the understanding of medical surge response system mechanisms and offer a theoretical framework and practical guidance for strengthening the full-chain resilience of health emergency systems.