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.

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.

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.

Objective:To explore the predictive value of systemic immune inflammation index combined with stress response index for postoperative urinary tract infection in patients with complicated kidney stones.Methods:From June 2021 to June 2023,97 patients who underwent treatment for urinary tract infection after complex kidney stone operation in Cangzhou Hospital of Integrated Traditional and Western Medicine were selected as infected group,and 87 patients who did not develop urinary tract infection after complex kidney stone operation were selected as uninfected group.Systemic immunoinflammatory index(SII),neutrophils,lympho-cyte,platelet,malondialdehyde(MDA),superoxide dismutase(SOD),catalase(CAT)levels were detected.Multivariate Logistic regression analysis was performed to analyze risk factors of postoperative urinary tract infection,and ROC curve was drawn to analyze the predictive value of SII,MDA,SOD and CAT alone and combined detection for postoperative urinary tract infection in patients with complex kidney stones.Results:Compared with uninfected group,levels of SII,neutrophils and MDA were increased in infected group,while levels of SOD and CAT were decreased(P<0.05).Levels of lymphocytes and platelets were decreased,the difference was not statistically significant(P>0.05).Multivariate Logistic regression analysis showed that presence of urinary tract history,opera-tion time≥100 min,urinary catheter retention time≥7 d,presence of preoperative urinary tract infection,stone load≥1 000 mm2,combined renal dysfunction,and preoperative blood glucose≥6.15 mmol/L were main risk factors for postoperative urinary tract infection in patients with complex kidney stones.ROC curve showed that combined detection was significantly more effective than single detec-tion of SII,MDA,SOD and CAT in the diagnosis of postoperative urinary tract infection in patients with precomplex kidney stones.Conclusion:Patients with urinary tract infection after complicated kidney stones have increased SII and MDA,decreased SOD and CAT levels,and the abnormal increased or decreased expression level are the predictors of risk of urinary tract infection after compli-cated kidney stones,which may be related with the diagnosis,development and prognosis of the disease.

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.

Gastric carcinoma(GC),a highly prevalent malignant tumor globally,often progresses to advanced stages by the time of diagnosis due to its insidious clinical presentation,thereby significantly reducing therapeutic effectiveness and patient quality of life.Accurate screening and histopathological characterization of early gastric cancer(EGC)are essential for developing individualized treatment approaches.Although endoscopic techniques remain the gold standard for early GC detection,their diagnostic accuracy is largely dependent on the operator's skill,a challenge that current artificial intelligence(AI)-assisted innovations aim to address by stan-dardizing diagnostic procedures.Deep learning(DL)-based computer vision systems have demonstrated remarkable performance in identifying subtle EGC features,not only improving lesion detection sensitivity but also enabling automated assessment of key pathological indicators.These technological advances offer objective,visualized diag-nostic support for clinical decision-making.This review provides a systematic overview of recent developments in DL applications for endoscopic image analysis of EGC and evaluates their potential for clinical integration.

Mussel adhesive protein (MAP) is increasingly utilized in the biomedical field for the development of various bio-inspired products such as adhesives, protective films, and coatings with diverse properties, owing to its excellent biocompatibility, low immunogenicity, broad-spectrum adhesiveness, and biodegradability. The biological effects and mechanisms of MAP include adhesive film formation, cell migration and proliferation, anti-inflammatory and antioxidant activities, and inhibition of pigment formation. In dermatological applications, MAP demonstrates a significant potential in antibacterial and antipruritic effects, wound healing, barrier repair, and scar regeneration. In recent years, advances in understanding the secretion, distribution, and adhesion mechanisms of MAP, along with innovations in extraction methods, have led to the development of various natural MAP, recombinant MAP, mussel-inspired, and mussel-mimetic biomedical products. However, the biosafety, tissue compatibility, and functionality of these products remain subjects of debate. Further investigation is needed to elucidate the interactions between MAP-based products and various organ tissues and cells within complex biological systems, as well as to thoroughly evaluate their safety and efficacy. Such efforts are essential to achieve high-value utilization of MAP and to explore broader application prospects. Future research may focus on integrating MAP with emerging technologies such as nanotechnology and smart materials to further develop its multifunctional applications in the field of dermatology.

Gastric carcinoma(GC),a highly prevalent malignant tumor globally,often progresses to advanced stages by the time of diagnosis due to its insidious clinical presentation,thereby significantly reducing therapeutic effectiveness and patient quality of life.Accurate screening and histopathological characterization of early gastric cancer(EGC)are essential for developing individualized treatment approaches.Although endoscopic techniques remain the gold standard for early GC detection,their diagnostic accuracy is largely dependent on the operator's skill,a challenge that current artificial intelligence(AI)-assisted innovations aim to address by stan-dardizing diagnostic procedures.Deep learning(DL)-based computer vision systems have demonstrated remarkable performance in identifying subtle EGC features,not only improving lesion detection sensitivity but also enabling automated assessment of key pathological indicators.These technological advances offer objective,visualized diag-nostic support for clinical decision-making.This review provides a systematic overview of recent developments in DL applications for endoscopic image analysis of EGC and evaluates their potential for clinical integration.