AIM: To explore the factors influencing the efficacy of diabetic macular edema(DME)with conbercept and to construct a nomogram prediction model. METHODS:Retrospective analysis. Patients with DME admitted from January 2021 to October 2025 were selected. All patients received intravitreal conbercept injection once monthly for three consecutive months. Subjects were divided into a good response group and a poor response group according to the reduction in central macular thickness(CMT)at 3 mo after treatment. The clinical data of the patients were collected to analyze factors associated with poor response to conbercept therapy in patients with DME, and to construct a nomogram prediction model for identifying poor responders to conbercept treatment among DME patients.RESULTS:A total of 400 DME patients(400 eyes)were included in this study. The response group consisted of 312 patients(312 eyes), with 200 males and 112 females, with an average age of 64.85±5.13 y. The non-response group consisted of 88 patients(88 eyes), with 54 males and 34 females, with an average age of 65.14±4.93 y. There were no differences between the two groups in terms of gender, age, alcohol consumption, smoking status, duration of diabetes, edema classification, disease classification, low-density lipoprotein, HRF, ELM and/or EZ continuity interruption, and SRF(all P>0.05). However, there were differences in DR disease duration, pre-treatment BCVA, high-density lipoprotein, glycated hemoglobin, DRIL, and VMIA(all P<0.05). Multivariate Logistic regression analysis showed that DR disease duration ≥1 y(OR=2.351, 95%CI: 1.360-4.065), poor pre-treatment BCVA(OR=3.432, 95%CI: 1.581-6.988), high glycated hemoglobin(OR=1.656, 95%CI: 1.346-2.038), presence of DRIL(OR=3.115, 95%CI: 1.532-6.333), and presence of VMIA(OR=2.526, 95%CI: 1.445-4.415)were all risk factors for poor response to conbercept treatment in DME patients, while elevated high-density lipoprotein(OR=0.025, 95%CI: 0.004-0.163)was a protective factor for poor response to conbercept treatment in DME patients(P<0.05). ROC analysis showed that the area under the curve(AUC)of the nomogram model for predicting poor response to conbercept treatment in DME patients was 0.817(95%CI: 0.776-0.854), with a sensitivity of 73.86% and a specificity of 78.21%. When the threshold probability was >3%, the nomogram model for predicting poor response to conbercept treatment in DME patients was more favorable for all patient treatment plans. CONCLUSION:The nomogram prediction model constructed in this study is helpful for the clinical early identification of patients with DME who have a high risk of poor response to conbercept treatment, and can provide a reference for the early prevention of poor response to conbercept treatment.

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 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.

Objective To empirically analyze multiple pathways for enhancing medical surge response capacity and provide useful references for improving the resilience of health systems.Methods A comprehensive theoretical analysis framework for improving medical surge response capacity was constructed based on the 4S theory and collaborative governance theory.68 interview texts on medical surge response capacity conducted in July 2024 were selected as analysis samples.Using fuzzy-set Qualitative Comparative Analysis(fsQCA),7 conditional variables were selected from four dimensions:management system,information system,materials,and personnel to analyze their impact on medical surge response capacity.Results(1)A single conditional variable does not constitute a necessary condition for improving medical surge response capacity;(2)After the combination of conditions,8 specific configuration paths for capacity improvement were identified.Through systematic and comprehensive refinement,they were summarized into three modes of comprehensive configuration capacity improvement paths,namely:rapid response and collaborative operation mode,information empowerment and precise response mode,and resource conditions and resilience construction mode.Conclusion It is necessary to explore and construct systematic,combined,modularized and path-oriented capacity building strategies,refine the operational implementation paths for improving China's medical surge response capacity,target the linkage and configuration modes of different conditional variables,promote the formulation and implementation of modular construction schemes oriented by key capacity,and make efforts from multiple aspects to enhance the resilience of the health system.

Objective:To explore the efficacy and feasibility of hypomethylating agent (HMA) as preventive therapy in children with high-risk acute myeloid leukemia (AML) after allogeneic hematopoietic stem cell transplantation (allo-HSCT).Methods:A retrospective cohort study. Data from 173 children who underwent allo-HSCT for high-risk AML at Children′s Hospital of Soochow University between August 2019 and April 2023 were analyzed. Participants were categorized into a trial group receiving HMA and a control group. Further classification was based on HMA courses:≥4 and <4 courses. The efficacy and safety of HMA preventive treatment after allo-HSCT were evaluated. Survival analysis was performed using the Kaplan-Meier method with Log-Rank testing, the Fine-Gray model was used to assess cumulative relapse rates and Cox regression was used to identify prognostic factors. Adverse events during HMA were descriptively analyzed.Results:Among 173 patients, there were 100 males (57.8%) and 73 females (42.2%), with the age of 81 (34,127) months. The starting time of HMA was 123 (91, 191) d post-transplant, continuing 4.0 (3.0, 6.5) courses and the follow-up period was 24 (13, 32) months. The trial group (53 cases) showed better 2-year overall survival (OS) rate ((88.6±5.6)% vs. (76.6±4.3)%, χ 2=5.00, P=0.025) and relapse-free survival (RFS) rate ((89.2±4.7)% vs. (56.2±4.8)%, χ 2=15.75, P<0.001) than control group (120 cases). The 2-year OS rates and RFS rates were similar between ≥4 courses group (31 cases) and <4 courses group (22 cases)(both P>0.05). The cumulative relapse rate in the trial group was significantly lower ((10.8±0.2)% vs. (35.2±0.2)%, χ 2=10.84, P=0.001) than control group. Among children with molecular relapse, 8 cases (8/30, 26.7%) in the control group had hematological relapse compared to 1 case (1/2) in the trial group ( χ 2=0.81, P=0.369). The differences in incidence of acute and chronic graft-versus-host disease (GVHD) were not statistically significant (all P>0.05). Cox regression analysis revealed that minimal residual disease (MRD) positivity detected by flow cytometry before allo-HSCT and chronic GVHD were independent risk factors for OS (both P<0.05).The HMA preventive treatment was an independent protective factor for RFS, while age ≥10 years and MRD positivity detected by PCR before allo-HSCT were independent risk factors for RFS (all P<0.05). In trial group, 38 cases experienced grade 3 to 4 adverse events (71.7%). Conclusion:HMA is safe as preventive treatment in post-transplant children with high-risk AML, which can reduce the relapse risk and doesn't increase the risk of GVHD.

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 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.

Objective To empirically analyze multiple pathways for enhancing medical surge response capacity and provide useful references for improving the resilience of health systems.Methods A comprehensive theoretical analysis framework for improving medical surge response capacity was constructed based on the 4S theory and collaborative governance theory.68 interview texts on medical surge response capacity conducted in July 2024 were selected as analysis samples.Using fuzzy-set Qualitative Comparative Analysis(fsQCA),7 conditional variables were selected from four dimensions:management system,information system,materials,and personnel to analyze their impact on medical surge response capacity.Results(1)A single conditional variable does not constitute a necessary condition for improving medical surge response capacity;(2)After the combination of conditions,8 specific configuration paths for capacity improvement were identified.Through systematic and comprehensive refinement,they were summarized into three modes of comprehensive configuration capacity improvement paths,namely:rapid response and collaborative operation mode,information empowerment and precise response mode,and resource conditions and resilience construction mode.Conclusion It is necessary to explore and construct systematic,combined,modularized and path-oriented capacity building strategies,refine the operational implementation paths for improving China's medical surge response capacity,target the linkage and configuration modes of different conditional variables,promote the formulation and implementation of modular construction schemes oriented by key capacity,and make efforts from multiple aspects to enhance the resilience of the health system.

ObjectiveTo assess the level of health literacy and its influencing factors among middle school students aged 12‒18 years in Jing’an District, Shanghai, so as to provide a solid scientific foundation for further developing more targeted intervention measures. MethodsA stratified cluster random sampling method was used to randomly select 4 middle schools in Jing’an District, Shanghai from November to December 2023, and conducted a health literacy questionnaire survey on non-graduating middle and high school students, respectively. The2023 Survey on the Status of Health Literacy among Middle School Students in Jing’an District, Shanghai was adopted, which consisted of two parts: health literacy and basic information. Health literacy was divided into three dimensions: health knowledge and concept literacy, healthy lifestyle and behavior literacy, and health skill literacy. Three dimensions could be categorized into six types of health literacy issue: scientific health literacy, infectious disease prevention and control literacy, chronic disease prevention and control literacy, safety and first aid literacy, basic health literacy, and health information literacy. ResultsA total of 1 161 middle school students were enrolled into this study, including 571 males and 570 females. The overall health literacy level of middle school students was 33.51%, with 34.81% among middle school students and 31.69% among high school students, respectively. Results of logistic regression analysis showed that health knowledge acquisition and awareness, as well as application frequency of health knowledge, were the influencing factors for the overall health literacy level among middle school students (P<0.05). The degree of family attention to health maintenance, health knowledge acquisition and awareness, and application frequency of health knowledge were the main influencing factors for the three dimensions and literacy of six types of health issues among middle school students (P<0.05). ConclusionThe levels of different types of health literacy among middle school students in Jing’an District are uneven, with the highest being safety and first aid literacy and the lowest being basic health literacy. It is recommended to take targeted measures to comprehensively improve the health literacy level of middle school students.