Methods:This study was a cross-sectional study. A prospective cohort study enrolled 42 patients with clinically suspected acute cerebrovascular disease and those undergoing follow-up examinations after intracranial vascular stenting at the First Affiliated Hospital of Zhengzhou University from June 2024 to May 2025. All patients underwent UHR PCD-CT examinations of the head and neck. Reconstructions were performed based on raw data, yielding conventional standard resolution (SR group) reconstructions and UHR images reconstructed using four distinct convolution kernels (Hv40, Hv48, Hv56, Hv64) in separate groups (Hv40 UHR group, Hv48 UHR group, Hv56 UHR group, Hv64 UHR group). Regions of interest were selected in the anterior cerebral artery, middle cerebral artery, posterior cerebral artery, posterior communicating artery, and anterior communicating artery. CT values and standard deviation (SD) values were measured for each artery, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Additionally, the sharpness of the vessel edges and the full-width-half-maximum (FWHM) of each artery were measured. One-way analysis of variance or the nonparametric Kruskal-Wallis test was used to compare the subjective and objective image quality metrics across the five groups. Pairwise comparisons were performed using the LSD test or Dunn method.Results:Statistically significant differences were observed in the overall comparison of vascular imaging SD, SNR, CNR, vascular edge sharpness, and FWHM among the SR group, Hv40 UHR group, Hv48 UHR group, Hv56 UHR group, and Hv64 UHR group ( P<0.05). No statistically significant differences in CT values were found ( P>0.05). Pairwise comparisons revealed statistically significant differences between all groups ( P<0.05), except that no significant differences were observed in image SD, SNR, CNR, vascular edge sharpness, or FWHM between the Hv56 UHR and Hv64 UHR groups ( P>0.05). Conclusions:UHR PCD-CT provides better image quality for neurovascular imaging. For the display of small intracranial vessels, the Hv64 provides sharper vessel walls and better subjective image quality compared to the less sharp convolutional cores.Objective:To explore the value of ultra-high resolution (UHR) photon-counting detector CT (PCD-CT) to improve the quality of neurovascular images.

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

BACKGROUND: Enzalutamide, a second-generation androgen receptor (AR) pathway inhibitor, is widely used in the treatment of castration-resistant prostate cancer. However, after a period of enzalutamide treatment, patients inevitably develop drug resistance. In this study, we characterized leucine-rich repeated G-protein-coupled receptor 5 (LGR5) and explored its potential therapeutic value in prostate cancer. METHODS: A total of 142 pairs of tumor and adjacent formalin-fixed paraf-fin-embedded tissue samples from patients with prostate cancer were collected from the Pathology Department at Sun Yat-sen Memorial Hos-pital. LGR5 was screened by sequencing data of enzalutamide-resistant cell lines combined with sequencing data of lesions with different Gleason scores from the same patients. The biological function of LGR5 and its effect on enzalutamide resistance were investigated in vitro and in vivo . Glutathione-S-transferase (GST) pull-down, coimmunoprecipitation, Western blotting, and immunofluorescence assays were used to explore the specific binding mechanism of LGR5 and related pathway changes. RESULTS: LGR5 was significantly upregulated in prostate cancer and negatively correlated with poor patient prognosis. Overexpression of LGR5 promoted the malignant progression of prostate cancer and reduced sensitivity to enzalutamide in vitro and in vivo . LGR5 promoted the phosphorylation of glycogen synthase kinase-3β (GSK-3β) by binding heat shock protein 90,000 alpha B1 (HSP90AB1) and mediated the activation of the Wingless/integrated (WNT)/β-catenin signaling pathway. The increased β-catenin in the cytoplasm entered the nucleus and bound to the nuclear AR, promoting the transcription level of AR, which led to the enhanced tolerance of prostate cancer to enzalutamide. Reducing HSP90AB1 binding to LGR5 significantly enhanced sensitivity to enzalutamide. CONCLUSIONS LGR5 directly binds to HSP90AB1 and mediates GSK-3β phosphorylation, promoting AR expression by regulating the WNT/β-catenin signaling pathway, thereby conferring resistance to enzalutamide treatment in prostate cancer.
Male ; Humans ; Phenylthiohydantoin/pharmacology* ; Benzamides ; Receptors, G-Protein-Coupled/genetics* ; Nitriles ; Cell Line, Tumor ; HSP90 Heat-Shock Proteins/metabolism* ; Drug Resistance, Neoplasm/genetics* ; Prostatic Neoplasms/drug therapy* ; beta Catenin/metabolism* ; Receptors, Androgen/genetics* ; Animals ; Mice ; Wnt Signaling Pathway/physiology*

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.

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 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.