Objective To construct an evaluation index system of organizational resilience of county medical alliance,and to propose countermeasures and suggestions for the enhancement of organizational resilience and operational effectiveness of county medical alliance.Methods The initial evaluation index system was constructed based on literature research and semi-structured interviews.Two rounds of Delphi expert consultation combined with critical value method refined the evaluation indexs.Evaluation index weights were determined via analytic hierarchy process and multiplicative weighting.The system was empirically tested using Zhejiang Province data.Results 21 experts completed consultations.Then the evaluation index system of county medical alliance organizational resilience was formed,which contains 5 first-level indexes,15 second-level indexes and 43 third-level indexes,containing 19 subjective and 24 objective evaluation indexs.The overall evaluation score of the organizational resilience of county medical alliance in Zhejiang Province was 73.97,which was at the average level of resilience.Conclusion The evaluation index system of organizational resilience of county medical alliance fits in with the national development plan to promote the construction of county high-quality,efficient and integrated medical and healthcare service system,and can provide effective measurement tools and theoretical references for the organizational capacity of county medical alliance and their maturity evaluation.

With the rapid evolution of artificial intelligence,machine learning has become widely applied in the realm of acupuncture clinical research.However,in comparison to the extensive utilization of supervised and unsupervised learning,attempts at employing reinforcement learning in the field of acupuncture clinical research remain relatively limited.In this article,we briefly introduce the concept and process of reinforcement learning,elucidating distinctions from both supervised and unsupervised learning.Then we discuss the potential applications of reinforcement learning in acupuncture clinical research,and summarizes the challenges that may arise during its implementation.Furthermore,a preliminary discussion on potential solutions to these issues is presented.Finally,a contemplation is offered on the combination of reinforcement learning with acupuncture clinical research,anticipating a meaningful reference for the advancement of reinforcement learning in the exploration and application within acupuncture clinical research and practice.

Objective:This study used data-independent acquisition (DIA) proteomics to analyze plasma protein expression in sepsis-induced coagulopathy (SIC), identify key biomarkers, and develop a diagnostic model.Methods:This prospective study included 46 adult sepsis patients from the intensive care unit. Patients were categorized into a general sepsis group ( n=26) and an SIC group ( n=20) based on established SIC criteria. Plasma samples underwent proteomic and bioinformatics analyses to identify differentially expressed protein (DEP) using LASSO regression and Random Forest. A diagnostic model was constructed and assessed via receiver operating characteristic (ROC) curve analysis. Results:The baseline data revealed that SIC patients exhibited longer prothrombin times, lower platelet counts, and higher D-dimer, fibrin degradation products, blood lactate, SOFA scores, and APACHE Ⅱ scores compared with general sepsis patients ( P<0.05). DIA proteomics identified 2 637 proteins, with 240 DEP meeting the criteria (fold change >1.5, P<0.05), including 81 upregulated and 159 downregulated DEP. Subcellular localization analysis revealed that DEPs were predominantly extracellular and nuclear. Gene ontology (GO) annotation showed that DEP were mainly involved in cellular physiology, biological regulation, and stress response processes in biological processes. Domain annotation revealed a predominance of immunoglobulin V regions in DEP, which are crucial for antigen recognition and binding. KEGG enrichment analysis showed significant enrichment of DEP in pathways related to natural killer cell-mediated cytotoxicity, glycosylphosphatidylinositol anchor biosynthesis, tumor necrosis factor signaling, and NF-κB signaling. LASSO regression identified angiogenin and C-type lectin domain family 10 member A as key DEP. The SIC diagnostic nomogram showed an area under the curve of 0.896, with 0.731 specificity and 0.900 sensitivity. Conclusion:The nomogram incorporating angiogenin and C-type lectin domain family 10 member A provides an accurate tool for SIC diagnosis.

Objective To extract the core elements and connotation definitions of the concept of organizational resilience of County Medical Community (CMC),in order to explore effective ways to enhance the organizational resilience of CMC.Methods Based on the Atomic spectrum,as of October 31,2023,52 articles related to organizational resilience in the health system were included for concept images extraction,calculating the frequency and occurrence rate of concept images,extracting the concept of organizational resilience in the health system,and deducing the core elements and definitions of the concept of organizational resilience in CMC were derived through the comparison of similarities and differences.Results Although both the healthcare system and the CMC are composite organizations,there are differences in environmental pressure,organizational size,and resource reserves.The concept of organizational resilience of CMC is defined as the advanced process of resilience functions exhibited by CMC in response to emergencies,internal and external threats,and social pressures that constrain the sustainable development of the organization,including redundant preparation,stable recovery,and response to growth,as well as their ability to absorb,adapt,and transform disruptive events.This not only maintains the basic structure and functions of the organization,but also enables the organization to grow against the trend.Conclusion Breaking through the traditional hierarchical structure in terms of structural resilience,achieving a deep integration and coordination mechanism of the medical community horizontally and vertically.Breaking through traditional static planar functions in terms of functional resilience,enhancing the driving force within the medical community through dynamic growth mechanisms.

Objective:This study used data-independent acquisition (DIA) proteomics to analyze plasma protein expression in sepsis-induced coagulopathy (SIC), identify key biomarkers, and develop a diagnostic model.Methods:This prospective study included 46 adult sepsis patients from the intensive care unit. Patients were categorized into a general sepsis group ( n=26) and an SIC group ( n=20) based on established SIC criteria. Plasma samples underwent proteomic and bioinformatics analyses to identify differentially expressed protein (DEP) using LASSO regression and Random Forest. A diagnostic model was constructed and assessed via receiver operating characteristic (ROC) curve analysis. Results:The baseline data revealed that SIC patients exhibited longer prothrombin times, lower platelet counts, and higher D-dimer, fibrin degradation products, blood lactate, SOFA scores, and APACHE Ⅱ scores compared with general sepsis patients ( P<0.05). DIA proteomics identified 2 637 proteins, with 240 DEP meeting the criteria (fold change >1.5, P<0.05), including 81 upregulated and 159 downregulated DEP. Subcellular localization analysis revealed that DEPs were predominantly extracellular and nuclear. Gene ontology (GO) annotation showed that DEP were mainly involved in cellular physiology, biological regulation, and stress response processes in biological processes. Domain annotation revealed a predominance of immunoglobulin V regions in DEP, which are crucial for antigen recognition and binding. KEGG enrichment analysis showed significant enrichment of DEP in pathways related to natural killer cell-mediated cytotoxicity, glycosylphosphatidylinositol anchor biosynthesis, tumor necrosis factor signaling, and NF-κB signaling. LASSO regression identified angiogenin and C-type lectin domain family 10 member A as key DEP. The SIC diagnostic nomogram showed an area under the curve of 0.896, with 0.731 specificity and 0.900 sensitivity. Conclusion:The nomogram incorporating angiogenin and C-type lectin domain family 10 member A provides an accurate tool for SIC diagnosis.

With the rapid evolution of artificial intelligence,machine learning has become widely applied in the realm of acupuncture clinical research.However,in comparison to the extensive utilization of supervised and unsupervised learning,attempts at employing reinforcement learning in the field of acupuncture clinical research remain relatively limited.In this article,we briefly introduce the concept and process of reinforcement learning,elucidating distinctions from both supervised and unsupervised learning.Then we discuss the potential applications of reinforcement learning in acupuncture clinical research,and summarizes the challenges that may arise during its implementation.Furthermore,a preliminary discussion on potential solutions to these issues is presented.Finally,a contemplation is offered on the combination of reinforcement learning with acupuncture clinical research,anticipating a meaningful reference for the advancement of reinforcement learning in the exploration and application within acupuncture clinical research and practice.

Objective To extract the core elements and connotation definitions of the concept of organizational resilience of County Medical Community (CMC),in order to explore effective ways to enhance the organizational resilience of CMC.Methods Based on the Atomic spectrum,as of October 31,2023,52 articles related to organizational resilience in the health system were included for concept images extraction,calculating the frequency and occurrence rate of concept images,extracting the concept of organizational resilience in the health system,and deducing the core elements and definitions of the concept of organizational resilience in CMC were derived through the comparison of similarities and differences.Results Although both the healthcare system and the CMC are composite organizations,there are differences in environmental pressure,organizational size,and resource reserves.The concept of organizational resilience of CMC is defined as the advanced process of resilience functions exhibited by CMC in response to emergencies,internal and external threats,and social pressures that constrain the sustainable development of the organization,including redundant preparation,stable recovery,and response to growth,as well as their ability to absorb,adapt,and transform disruptive events.This not only maintains the basic structure and functions of the organization,but also enables the organization to grow against the trend.Conclusion Breaking through the traditional hierarchical structure in terms of structural resilience,achieving a deep integration and coordination mechanism of the medical community horizontally and vertically.Breaking through traditional static planar functions in terms of functional resilience,enhancing the driving force within the medical community through dynamic growth mechanisms.

Objective To construct an evaluation index system of organizational resilience of county medical alliance,and to propose countermeasures and suggestions for the enhancement of organizational resilience and operational effectiveness of county medical alliance.Methods The initial evaluation index system was constructed based on literature research and semi-structured interviews.Two rounds of Delphi expert consultation combined with critical value method refined the evaluation indexs.Evaluation index weights were determined via analytic hierarchy process and multiplicative weighting.The system was empirically tested using Zhejiang Province data.Results 21 experts completed consultations.Then the evaluation index system of county medical alliance organizational resilience was formed,which contains 5 first-level indexes,15 second-level indexes and 43 third-level indexes,containing 19 subjective and 24 objective evaluation indexs.The overall evaluation score of the organizational resilience of county medical alliance in Zhejiang Province was 73.97,which was at the average level of resilience.Conclusion The evaluation index system of organizational resilience of county medical alliance fits in with the national development plan to promote the construction of county high-quality,efficient and integrated medical and healthcare service system,and can provide effective measurement tools and theoretical references for the organizational capacity of county medical alliance and their maturity evaluation.

Objective To develop and empirically validate the County Medical and Health Community Organizational Resilience Evaluation Scale to verify its reliability and validity,and to provide a reference for the management practice of organizational resilience in medical and health communities.Methods The health system resilience framework and the dual resilience analysis framework were used as the content framework of this scale,and the initial scale was formed by literature review reference and interviews,and two rounds of correspondence to 21 experts were conducted to form the County Medical and Health Community Organizational Resilience Evaluation Scale.In October-November 2023,the medical staff of six medical and health communities in Zhejiang Province were sampled to conduct the survey,and SPSS 25.0 software was applied to process the data.Results The internal consistency Cronbach's alpha coefficient of the scale was 0.97,and the Cronbach's alpha coefficients of the five dimensions ranged from 0.67～0.92(P＜0.001);the folded half reliability was 0.95,and the alpha coefficients of the two parts were 0.94.The results of the exploratory factor analysis showed that the KMO was 0.95,and that each dimension of the constructed Organizational Resilience Scale and the total score of the scale were positively correlated with the total score of the Strategic Flexibility Scale(P＜0.01).Conclusion The Organizational Resilience Measurement Scale designed has good reliability and reference value,and may provide a tool for assessing organizational resilience in county medical and health communities in the future.

Objective To develop and empirically validate the County Medical and Health Community Organizational Resilience Evaluation Scale to verify its reliability and validity,and to provide a reference for the management practice of organizational resilience in medical and health communities.Methods The health system resilience framework and the dual resilience analysis framework were used as the content framework of this scale,and the initial scale was formed by literature review reference and interviews,and two rounds of correspondence to 21 experts were conducted to form the County Medical and Health Community Organizational Resilience Evaluation Scale.In October-November 2023,the medical staff of six medical and health communities in Zhejiang Province were sampled to conduct the survey,and SPSS 25.0 software was applied to process the data.Results The internal consistency Cronbach's alpha coefficient of the scale was 0.97,and the Cronbach's alpha coefficients of the five dimensions ranged from 0.67～0.92(P＜0.001);the folded half reliability was 0.95,and the alpha coefficients of the two parts were 0.94.The results of the exploratory factor analysis showed that the KMO was 0.95,and that each dimension of the constructed Organizational Resilience Scale and the total score of the scale were positively correlated with the total score of the Strategic Flexibility Scale(P＜0.01).Conclusion The Organizational Resilience Measurement Scale designed has good reliability and reference value,and may provide a tool for assessing organizational resilience in county medical and health communities in the future.