Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

Objective To investigate the etiology and treatment of dysuria shortly after holmium laser enucleation of the prostate (HoLEP) for benign prostatic hyperplasia (BPH), so as to improve the therapeutic efficacy. Methods A retrospective analysis was conducted on 1361 BPH patients who received HoLEP at our hospital during Mar. 2018 and Mar. 2025. All patients underwent ultrasound, computed tomography (CT), urethral probing or radiography, cystourethroscopy, and urodynamic testing. The causes, treatments and prognosis of dysuria after operation were summarized. Results ninety-eight patients (7.2%) experienced dysuria within 3 months after surgery, and all patients received initial urethral dilation. Among the 62 cases with urethral stricture, 28 were treated with urethral dilation (average of 8 sessions), 16 underwent external urethral incision, 8 underwent anterior urethroplasty, and 10 underwent transurethral plasma incision for urethral stricture. Of the 12 cases with residual prostate tissue, 5 underwent urethral plasma prostatectomy and 7 received medication. All of the 12 cases with bladder neck contracture underwent transurethral plasma incision of the bladder neck and local drug injection. Five cases with intravesical blood clots underwent urethral blood clot irrigation, 2 of which developed active bleeding and then underwent electrocoagulation. Four cases with detrusor dysfunction had catheters re-indwelled for one week followed by medication. Two cases with stone and one case with tissue fragment impacted in the urethra underwent urethral holmium lithotripsy and cystoscopic removal. All patients underwent reoperation successfully and experienced complete resolution or significant improvement of urinary difficulties. Conclusion A certain proportion of patients experience dysuria after HoLEP, with diverse etiologies. The combination of prevention and treatment can reduce the incidence of dysuria.

This paper has summarized current studies on retinitis pigmentosa （RP） from two perspectives， which were disease mechanism insights from western medicine， and etiology， pathogenesis and therapeutic principles in traditional Chinese medicine （TCM）. From the perspective of western medicine， the understanding of RP has evolved from an early single-factor explanation focusing on pathogenic gene mutations to a more comprehensive， multifactorial model involving genetic abnormalities， photoreceptor cell degeneration， retinal pigment epithelium dysfunction， oxidative stress， inflammatory responses， microenvironmental imbalance， and vascular insufficiency. In TCM， RP is considered to be a disorder manifested in the eyes but rooted in dysfunction of the zang-fu （脏腑） organs. Its pathogenesis is primarily associated with congenital insufficiency， liver-kidney depletion， impaired spleen function of transportation and transformation， and stasis obstructing the meridians. The core pathological mechanism can be summarized as "deficiency complicated by stasis"， emphasizing holistic imbalance， dynamic disease progression， and syndrome differentiation and treatment. The differences and complementarities between the two medical systems in understanding disease progression are further discussed. RP is proposed to be more appropriately regarded as a complex disease characterized by long-term progression， stage-dependent evolution， and persistent decompensation. Western medicine contributes to elucidating the molecular mechanisms underlying RP and provides the foundation for targeted therapeutic interventions， whereas TCM offers a holistic perspective for understanding disease evolution through the assessment of overall functional status， disease-stage stratification， and long-term syndrome-based regulation.

This paper has summarized current studies on retinitis pigmentosa （RP） from two perspectives， which were disease mechanism insights from western medicine， and etiology， pathogenesis and therapeutic principles in traditional Chinese medicine （TCM）. From the perspective of western medicine， the understanding of RP has evolved from an early single-factor explanation focusing on pathogenic gene mutations to a more comprehensive， multifactorial model involving genetic abnormalities， photoreceptor cell degeneration， retinal pigment epithelium dysfunction， oxidative stress， inflammatory responses， microenvironmental imbalance， and vascular insufficiency. In TCM， RP is considered to be a disorder manifested in the eyes but rooted in dysfunction of the zang-fu （脏腑） organs. Its pathogenesis is primarily associated with congenital insufficiency， liver-kidney depletion， impaired spleen function of transportation and transformation， and stasis obstructing the meridians. The core pathological mechanism can be summarized as "deficiency complicated by stasis"， emphasizing holistic imbalance， dynamic disease progression， and syndrome differentiation and treatment. The differences and complementarities between the two medical systems in understanding disease progression are further discussed. RP is proposed to be more appropriately regarded as a complex disease characterized by long-term progression， stage-dependent evolution， and persistent decompensation. Western medicine contributes to elucidating the molecular mechanisms underlying RP and provides the foundation for targeted therapeutic interventions， whereas TCM offers a holistic perspective for understanding disease evolution through the assessment of overall functional status， disease-stage stratification， and long-term syndrome-based regulation.

Objective:To develop and validate a clinical warning model for severe pediatric community-acquired pneumonia (CAP) using electronic health records.Methods:A retrospective cohort study was conducted, analyzing clinical data of 15 750 children hospitalized for CAP at Wuhan Children′s Hospital between January 1, 2019, and December 31, 2023. Patient data were randomly split into training and testing sets at a 7∶3 ratio. Six supervised machine learning models were constructed in the training set, optimized using five-fold cross-validation, and evaluated in the testing set. Model performance was assessed using ROC-AUC, sensitivity, specificity, positive predictive value, negative predictive value, calibration curves, and clinical decision curve analysis at optimal thresholds. The best-performing model was selected, and SHapley Additive exPlanations (SHAP) were used to interpret feature importance. A program interface was developed based on the model results, enabling integration into clinical decision support systems for automated early warning.Results:A total of 15 750 participants, ranging in age from 28 days to 18 years, were included in the study. The median age was 2 years [interquartile range (IQR): 0-4 years], with 9 555 males (60.67%) and 6 195 females (39.33%). Among them, 2 211 (14.04%) developed severe pneumonia. In the prediction models, XGB outperformed other models with an ROC-AUC of 0.884 (95% CI: 0.870-0.898), sensitivity (0.803, 95% CI: 0.772-0.832), specificity (0.828, 95% CI: 0.816-0.839). Calibration analysis showed strong agreement between predicted and observed risks (Brier score: 0.081, 95% CI: 0.075-0.086). The analysis based on the SHAP method revealed that respiratory rate, heart rate, T-lymphocyte subsets, and red blood cell volume distribution width-SD are predictive factors for severe progression of community-acquired pneumonia (CAP) in children. Conclusion:An interpretable machine learning model was developed for the early detection and personalized treatment planning of severe CAP in children, providing valuable support to clinicians.

In recent years, the situation of climate change has intensified, posing a threat to public health. There is an urgent need to promote public health adaptation actions to climate change. In January 2025, the National Disease Control and Prevention Administration issued the "Guidelines for Public Health Adaptation Actions to Climate Change" (hereinafter referred to as the "Guidelines"). The Guidelines put forward 20 items of guidance on six categories of public health adaptation actions, including understanding basic concepts, comprehending important policies, learning core knowledge, paying attention to key populations, practicing a low-carbon lifestyle, and mastering protection skills. It elaborates on the key concepts and the latest policies that the public needs to understand, and also provides the behavioral concepts and protection skills that should be mastered to adapt to climate change. This article provides a systematic interpretation of the Guidelines, introducing the background, ideas, connotations, and applications of their compilation, with the aim of enhancing society′s cognitive understanding of the Guidelines.

Objective:This article studies the abilities and quality that medical technical managers should possess and provides a reference for promoting the professional training and development of medical technical managers.Methods:The data were obtained through semi-structured interviews and literature collection. The interview subjects were 20 scientific researchers with transformation projects and 10 management staffs with technical manager certificates in medical colleges. The documents are 6 articles related to ″technical manager capabilities″ collected on open academic platforms. Grounded theory was used to code and analyze above data.Results:After three-level coding and combining with the iceberg competency model, the knowledge, skills, self-awareness, traits and motivation of medical technology managers were sorted out, totalling 5 core categories, 10 main categories, and 50 initial categories, to construct a competency model for medical technology managers.Conclusions:Based on the complex knowledge structure and high occupational requirements of medical technology managers, policy insights such as systematic knowledge training, raising skill requirements in practice, and enriching assessment standards and communication channels are proposed.

Haloacetic acids(HAAs),as a class of disinfection byproducts in drinking water,pose potential threats to human health,so the rapid,accurate and simultaneous detection of HAAs is of great significance for ensuring drinking water safety.Aiming at the challenges in HAAs detection and risk analysis,a novel method for synchronous rapid detection of seven kinds of HAAs in drinking water based on in situ derivatization technology and headspace gas chromatography was developed in this study.Through single-factor optimization experiments,the optimal reaction parameters for in situ derivatization were determined,including the type and dosage of salting-out agent,the acidity of reaction system,the amount of phase transfer catalyst,the dosage of derivatization agent,and the extraction solvent volume.Methodologic validation showed that the seven kinds of HAAs exhibited excellent linear relationships within their respective detection concentration ranges(R2>0.998).The method detection limits(MDLs)ranged from 0.04 to 0.33 μg/L,and the limits of quantification(LOQs)were between 0.14 and 1.34 μg/L.For real water samples,the average spiked recoveries of the seven HAAs ranged from 90.9%to 107.7%,with relative standard deviation(RSDs)between 1.55%and 6.49%,and the HAAs contents in all tested samples were below the limits specified in the Standards for Drinking Water Quality(GB 5749-2022)of China.This method was featured with simple operation,fast analysis speed,high sensitivity,and good accuracy,providing an efficient and reliable technical support for routine monitoring of HAAs contaminants in drinking water and showing promising application value for widespread promotion.

With rapid development of technology,generative artificial intelligence has emerged as a research hotspot in field of artificial intelligence,and demonstrates immense potential to enhance quality of medical education,reduce educational costs,and optimize allocation of educational resources in medical education.Medical Immunology is a complex and continuously evolving disci-pline that not only encompasses a vast amount of basic knowledge but also involves interdisciplinary intersections,which involves extensive foundational knowledge,cross-disciplinary integration and cutting-edge research.Furthermore,rapid updates in research progress,coupled with abstract and complex nature of content,pose significant challenges for teaching.Therefore,analyzing teaching advantages of generative artificial intelligence in empowering Medical Immunology,exploring its practical applications in Medical Immunology education,and innovating theoretical education systems and methodologies are of great significance for improving quality of Medical Immunology education.

In recent decades,the incidence of human papillomavirus(HPV)-associated oropharyngeal squamous cell carcinoma(OPSCC)has shown a marked increase.Significant changes have also occurred in the OPSCC diagnosis and treatment paradigm.Deter-mining HPV status prior to treatment is now essential,and radiotherapy/chemotherapy,immunotherapy,and minimally invasive surgical techniques have progressively emerged as key modalities for managing OPSCC.However,alongside these paradigm shifts,a comprehen-sive technical consensus guiding the entire diagnostic and therapeutic process for OPSCC patients is currently lacking.Given China's large population base and the rising incidence of OPSCC,an expert panel convened to develop a clinical technical consensus on OPSCC diagno-sis and management tailored to China's specific context.This consensus aims to further enhance and standardize understanding of OPSCC management techniques among relevant healthcare professionals.