Objective: To understand the epidemiological characteristics of a pertussis outbreak in Guangzhou, so as to provide references for outbreak response and prevention strategies. Methods: From April 5 to June 9, 2024, case screening was conducted among 246 preschool children, 35 staff members, and one full time school nurse in a kindergarten in Guangzhou based on case definition. Field epidemiological investigation methods were employed to collect relevant information, and screening samples were collected from individuals involved in the outbreak. The clinical manifestations, epidemiological characteristics, and risk factors for transmission of the outbreak were analyzed, with rate comparisons performed using the χ 2 test. Results: There were a total of 15 confirmed cases of pertussis in the kindergarten. The main clinical manifestations included intermittent cough in 14 cases ( 93.33 %), sputum production in 5 cases (33.33%), fever in 2 cases (13.33%), paroxysmal spasmodic cough in 1 case (6.67%), and vomiting in 1 case (6.67%). There was no statistically significant difference in the reporting rates of interrupted cough symptoms between pertussis cases (93.33%) and non pertussis cases (92.86%)( χ 2=3.74, P >0.05). The cases were aged 4-5 years, including 5 males and 10 females. The interval between symptom onset and diagnosis ranged from 2 to 25 days, with a median of 10 days. The outbreak involved two classes, with attack rates of 48.28% and 3.45%, respectively. Laboratory testing confirmed 14 close contacts positive for Bordetella pertussisnucleic acid. Among close contacts, only one received prophylactic medication as required. Conclusion The outbreak is a pertussis outbreak in a kindergarten caused by Bordetella pertussis infection, demonstrating distinct temporal and spatial clustering characteristics.

ObjectiveTo analyze the incidence and spatiotemporal distribution of typhus fever in Baoshan City, Yunnan Province from 2005 to 2023, to identify high-risk populations and regions, so as to provide a scientific basis for optimizing the allocation of local prevention and control resources and developing targeted intervention measures. MethodsData of typhus fever cases in Baoshan City from 2005 to 2023 were obtained from the Infectious Disease Information Management System of the Chinese Center for Disease Control and Prevention. Descriptive epidemiological methods were used to analyze the temporal, spatial and demographic distribution of typhus fever cases. Spatial clustering was assessed using spatial dynamic window scan statistics (circular and elliptical windows), flexible spatial scan statistics, and local spatial autocorrelation methods (including local Moran’s I, local Geary’s C, and Getis-Ord G_i*). Retrospective spatiotemporal scan statistics were employed to detect spatiotemporal clusters. ResultsA total of 1 099 typhus fever cases were reported in Baoshan City from 2005 to 2023. The incidence rate peaked at 6.31/ 100 000 in 2007, followed by a decline until reaching its lowest level at 0.21/100 000 in 2015 , and subsequently rebounded during 2016‒2023. The highest proportion of cases was among children under 10 years of age (31.12%), and the top three occupations of cases were farmers, students, and children, accounting for 88.62% of all cases. Cases occurred predominantly between June and September each year. The incidence was relatively high in Jiucheng Town (62.58/100 000), Yaoguan Town (57.15/100 000), and Dianyang Town (46.81/100 000) of Shidian County. Spatial clustering analyses indicated that high-risk areas were mainly located in the southern part of Baoshan City, showing a south-to-north trend. Spatiotemporal scan analyses identified five clusters, with the most likely cluster centered around Yaoguan Town, covering ten towns (subdistricts) during the period 2007‒2010. ConclusionThe incidence of typhus fever in Baoshan City exhibits a clear seasonal and spatial clustering pattern, with peak incidence occurring in summer and autumn. Spatially, cases are primarily distributed in the southern part of Baoshan City, and high-risk clusters exhibit a south-to-north trend. Farmers, students, and children are the high-risk groups.

Background/Aims: The pathophysiology of refractory gastroesophageal reflux disease (RGERD), which differs from proton pump inhibitor dependent gastroesophageal reflux disease (DGERD), remains incompletely elucidated. This study aims to compare esophageal motility patterns, transdiaphragmatic pressure gradients (TPG), and reflux profiles between RGERD and DGERD patients, and to delineate dynamic pressure gradient-esophagogastric junction (EGJ) interactions in these patients. Methods: In this retrospective study, 274 patients who underwent 24-hour impedance-pH monitoring and high-resolution manometry, along with an assessment of proton pump inhibitor responsiveness, were classified as RGERD (32.5%), DGERD (54.4%), or non-GERD (13.1%). Clinical characteristics, TPG, esophageal motility, and reflux metrics were compared between RGERD and DGERD patients. Subgroup analysis excluding hiatal hernia (HH) was conducted to investigate the pathophysiology of RGERD. Results: The RGERD group exhibited a significantly higher proportion of chest pain compared to the DGERD group. Regarding reflux profiles, RGERD patients without HH (RGERDHH- group) experienced increased weakly acidic reflux (P < 0.001) and prolonged bolus exposure (P = 0.006) compared to their counterparts (DGERDHH- group). Mechanistically, the RGERDHH- group showed reduced lower esophageal sphincter basal pressure (P = 0.010) and EGJ contractile integral (P = 0.005). Notably, following a wet-swallow, the RGERDHH- group experienced the significant elevation in gastric pressure and TPG. Correlation analyses revealed weakly acidic reflux and bolus exposure were positively correlated with gastric pressure variation, and inversely correlated with lower esophageal sphincter basal pressure. Conclusions Transient gastric pressure elevation and compromised EGJ barrier function drive weakly acidic reflux and esophageal bolus exposure. This pressure gradient-barrier mismatch underpins the refractoriness of RGERD.

Objective To observe the value of Swin2SR network based on Transformer architecture for reconstructing chest super-resolution CT images.Methods Chest CT data of 218 patients were retrospectively collected.Swin2SR model based on Transformer architecture was adopted to enhance standard 512 matrix(512 × 512)CT images(standard-512 group)into 1 024(SR-1 024 group)and 2 048(SR-2 048 group)matrix SR CT images,respectively.Subjective and objective evaluation of image quality were performed,and the results were compared among groups.Results The subjective scores of overall imaging quality and lesion clarity in SR-1 024 and SR-2 048 groups were both higher than those in standard-512 group(all P＜0.05),while no significant difference was found between the former two(P＞0.05).Meanwhile,no significant difference of objective indexes of imaging quality was observed among 3 groups(all P＞0.05).Conclusion Swin2SR model could reconstruct chest SR CT images without increasing noise and improve imaging quality.

Objective:To analyze the association between healthcare workers mental health,institu-tional supplies and facilities,inter-organizational coordination during infectious disease outbreaks,and the healthcare institution resilience.Methods:An online questionnaire survey was conducted among the healthcare workforce from 146 institutions in Beijing from January 13,2023 to February 9,2023,and a total of 1 434 eligible respondents were included.The sample comprised 408 responses from tertiary hos-pitals,117 from secondary hospitals,and 909 from primary care institutions.The resilience indicator for healthcare institutions was defined as the degree to which medical services met patient demands,with in-fluencing factors including physical factors,such as material shortages and facility space adaptation or ex-pansion,organizational factors such as information sharing and patient referral,and psychological factors were evaluated using job satisfaction(extrinsic satisfaction,intrinsic satisfaction),burnout(emotional exhaustion,depersonalization,reduced personal accomplishment),and depression status.Ordered mul-ticlassification Logistic regression was used to examine the impact of various factors on the degree to which healthcare services met patient needs;additionally,demographic factors that might influence institutional resilience were controlled.Results:During the emergency response phase,93％of hospitals maintained the capacity to meet patient needs,though tertiary hospitals demonstrated significantly higher rates of service inadequacy(21.05％).Material shortages were reported across all institutions,with tertiary hos-pitals experiencing more frequent multi-item shortages.Inter-institutional collaboration patterns revealed substantial variation:87.50％of primary care facilities,42.86％of secondary hospitals,and 31.58％of tertiary hospitals.Healthcare workers across all levels reported mild depressive symptoms and moderate-to-severe burnout levels.Regression analysis showed high satisfaction(overall satisfaction β=0.04,ex-trinsic satisfaction β=0.06,and intrinsic satisfaction β=0.08),low degree of job burnout(emotional exhaustion β=-0.04,depersonalization β=-0.07 and reduced personal accomplishment β=0.01),low degree of depression(β=-0.06)were significantly associated with higher healthcare institution re-silience.In addition,material shortages were significantly associated with lower resilience,and renova-tion and expansion of treatment spaces,and information sharing,were all associated with higher resilience.Demographic factors(age,gender,marital status,educational background,etc.)had no sig-nificant impact on resilience.Conclusion:Mental health status significantly influences healthcare institu-tion resilience.As human resources constitute the core asset of healthcare institutions,strategic optimiza-tion of workforce allocation and psychological support interventions can effectively strengthen resilience.Moreover,healthcare institution resilience is positively impacted by orderly material supply chains,timely resource distribution,and adaptive reconfiguration of clinical spaces.Finally,facilitating information sharing also enhances institutional resilience.

Objective:To develop a scientific and practical questionnaire for general practice residents, and to conduct multidimensional and comprehensive evaluation of the quality of general practice teaching clinics.Methods:A preliminary draft of the questionnaire items was formulated based on a literature review and in-depth interviews. The Delphi method was employed to conduct two rounds of consultation with 14 experts. Following revisions, a convenience sampling method was used to invite general practice residents from three standardized residency training bases to test the reliability and validity of the questionnaire.Results:The questionnaire consisted of 23 items, covering the three dimensions of preparation, implementation process, and comprehensive evaluation of the teaching clinics. The response rates for the two rounds of the expert consultation were both 100.00%, with expert authority coefficients of 0.89 and 0.90, respectively. The overall Cronbach's α coefficient of the questionnaire was 0.93, and the correlation coefficients between each item score and the total score were all >0.30. Structural validity analysis revealed that three common factors were extracted from the questionnaire, with a cumulative variance contribution rate of 77.89%. Conclusions:The General Practice Teaching Clinic Quality Evaluation Questionnaire for Residents developed in this study demonstrates high reliability and validity. The questionnaire provides a scientific basis for the standardized assessment of teaching quality in general practice clinics. By incorporating resident feedback on the teaching process, the questionnaire promotes the development of a teaching clinic quality improvement mechanism focused on residents and plays a significant role in enhancing the teaching capabilities of supervising physicians in clinics.

Objective:To fine-tune the mT5 (massively multilingual pre-trained text-to-text transformer) large language model, automatically generate report conclusions for teaching purposes from chest CT image descriptions, and assess the quality of automatically generated conclusions.Methods:The training set included 3 000 high-quality physical examination chest CT reports from one hospital, and the external validation set consisted of 600 physical examination chest CT reports from two other hospitals. Experienced radiology teaching physicians assessed the consistency between the generated conclusions and the original physician-written conclusions in the external validation set using a 5-point Likert scale across five linguistic indicators (correctness of examination information, correctness of lesion detection, standardization of terminology, applicability of the conclusions, and simplicity of conclusions). Using the original report conclusions as the reference, the accuracy of the conclusions generated based on the external validation set in describing four major thoracic conditions (pulmonary nodules, pneumonia, emphysema, pleural effusion) was evaluated. Perform chi square test using SPSS 25.0.Results:In the external validation set, the mean consistency score between the generated conclusions and the original conclusions given by the radiology teaching physicians was >4 points, indicating agreement with the original conclusions. In the generated conclusions, the description of the four major thoracic conditions demonstrated 0.95-1.00 (95% CI=0.91-1.00) accuracy, 0.76-1.00 (95% CI=0.59-1.00) sensitivity, and 0.97-1.00 (95% CI=0.91-1.00) specificity. Conclusions:The chest CT report conclusion generation system based on the mT5 large language model demonstrated high accuracy and is expected to provide immediate and efficient automated guidance for standardized residency training.

In response to the new requirements for course instruction outlined in the revised training program for medical imaging program, this study integrated medical imaging technology courses based on the principle of outcome-oriented education and by leveraging self-developed digital resources, with imaging methods as the entry point. The core elements of the course teaching were re-optimized and reorganized to transcend the temporal and spatial limitations of course delivery, enabling the rational application of diverse teaching methods. This approach facilitated the integration of knowledge across three specialized courses, namely medical imaging physics, medical imaging equipment, and medical imaging examination techniques, and achieved full-dimensional and whole-process teaching evaluation. While reducing the number of hours allocated to theoretical instruction, the teaching objectives were achieved with high quality, providing a reference for the integration of digital technologies into the teaching of medical imaging and related disciplines.

Perception of recurrence risk in cardiovascular disease (CVD) patients plays a significant role in aspects such as their quality of life and treatment adherence. This paper reviews the theoretical foundations of recurrence risk perception, the conceptual origins and developmental process, measurement tools, influencing factors of recurrence risk perception in CVD patients, and research progress of recurrence risk perception in CVD management. The aim is to provide a basis for developing scientifically effective intervention measures for CVD patients in the future.

Objective To address the challenges of accurately capturing critical features in small-sample diabetic retinopathy(DR)recognition models in real-world applications,and the overly smooth distribution of true and false feature coefficients,we propose an enhanced small-sample DR recognition method based on an improved capsule network.Methods Firstly,the method enhances image feature representation by removing redundant boundary information and employing discrete wavelet transform based on the Haar wavelet function,thereby high-lighting critical pathological features.Secondly,the convolutional layer of the capsule network is optimized through a multi-branch architecture to extract multi-scale features from retinal images,while incorporating a convolutional block attention module that is subsequently fed into the capsule layer.Finally,the sigmoid function replaces the soft-max function in dynamic routing,thereby improving the model's robustness.Result The enhanced neural network model achieved an accuracy of 98.62%on the Kaggle public dataset following a rigorous selection and preprocessing procedure.Conclusion The enhanced capsule network demonstrated superior precision in identifying diabetic reti-nopathy within small sample sizes compared to other state-of-the-art algorithms currently available.