Objective: To investigate the current knowledge status of transfusion medicine among pediatricians/postgraduates and the reliability of large language models (LLMs) for assisted learning, and to assess changes in pediatricians' transfusion medicine knowledge before and after the implementation of the "Pediatric Transfusion Guideline" (hereafter referred to as the "Guideline"). Methods: In January 2022 (prior to the implementation of the "Guideline"), a questionnaire was developed based on the "Guideline" content and distributed to pediatricians. Subsequently, in July 2025 (after the implementation of the "Guideline"), the "Pediatric Transfusion Medicine Knowledge Questionnaire" was designed based on the content of the January 2022 questionnaire. This questionnaire survey was conducted on pediatricians/postgraduates and LLMs. We analyzed the level of transfusion medicine knowledge among pediatricians/postgraduates and the reliability of LLMs for assisted learning, and compared the accuracy of pediatricians' responses before and after "Guideline" implementation. Results: The survey results after the implementation of the "Guidelines" revealed that pediatricians/postgraduates achieved response accuracy rates exceeding 80% on the topic of "Patient Blood Management". However, response accuracy rates were below 30% for topics including "Types and Indications of Blood Components/Products" and "E-valuation of Transfusion Efficacy". The pediatricians' accuracy rates for related questions before and after the implementation of the "Guidelines" were 14.7%-68.9% and 3%-38%, respectively, and the comparison of accuracy rates for each question showed significant differences (P<0.001). The accuracy rates of the LLMs on the questionnaire were all below 90%. Among them, Doubao (81.1%) and Kimi (86.4%) achieved relatively higher accuracy rates, while Tencent Yuanbao (Hunyuan) had the lowest accuracy rate at only 59.5%. Conclusion: The implementation of the "Guideline" may have improved pediatricians' knowledge level of pediatric transfusion medicine. However, their knowledge level of pediatric transfusion remains low, and LLMs cannot yet provide absolutely reliable guidance. Systematic training in pediatric transfusion medicine is urgently needed.

Transfusion and Anemia Expertise Initiative-Control/Avoidance of Bleeding developed a strategy for platelet and plasma infusion management in critically ill children based on systematic reviews and consensus meetings of international multidisciplinary experts. One good practice statement and six expert consensus statements were proposed for plasma and platelet transfusions in critically ill children following severe trauma, traumatic brain injury, and/or intracranial hemorrhage. This article introduces the specific methods and basis for the formation of recommendations in this part of the guide.

BackgroundEmotional disorders in adolescents have emerged as a prominent issue in recent years. Current mainstream clinical assessment approaches for such conditions predominantly rely on interviews and rating scales， which are limited by inherent drawbacks such as high subjectivity and recall bias. Accordingly， there exists an urgent clinical need for the development of objective， quantifiable auxiliary diagnostic tools.In previous studies， frontal electroencephalography （EEG） has demonstrated significant value in assessing depressive and anxiety. However， the lack of standardized quantitative metrics and intuitive visual analytical approaches has severely restricted clinical interpretability of EEG data and diminished patient engagement. To address these key limitations， the present study proposes an innovative analytical framework that converts frontal EEG signals into quantifiable visual metrics to enhance clinical comprehension and acceptance. ObjectiveTo explore the value of frontal EEG in assessing anxiety， depression， and sleep quality in adolescents with emotional disorders， with the aim of providing objective auxiliary tools for clinical diagnosis and assessment of adolescents with emotional disorders. MethodsThis cross-sectional study recruited 105 adolescents aged 12-18 years who visited the outpatient department of a specialized mental hospital in Beijing from April 2023 to April 2024. All participants met the diagnostic criteria for mood （affective） disorders or anxiety disorders in the International Classification of Diseases， tenth edition （ICD-10）. Frontal EEG signals were collected within a big data analytics-driven framework and further processed by EEG system to generate six quantitative cerebral function indices， namely brain load， tension and excitement， emotional stress， sleepiness index， cerebral vitality， and cerebral fatigue. In addition， validated standardized scales， including the Self-rating Anxiety Scale （SAS）， the Self-rating Depression Scale （SDS）， and the Pittsburgh Sleep Quality Index （PSQI）， were administered for anxiety， depressive symptoms， and sleep quality， respectively. ResultsIn adolescent patients with emotional disorders， the SAS score exhibited significant positive correlations with brain load （r_s=0.328， P<0.01）， emotional stress （r_s=0.341， P<0.01）， and cerebral fatigue （r_s=0.286， P<0.01）. The SDS score was positively correlated with brain load （r_s=0.275， P<0.01）， emotional stress （r_s=0.241， P<0.05）， and cerebral fatigue （r_s=0.311， P<0.01）， while showing a significant negative correlation with cerebral vitality （r_s=-0.212， P<0.05）. Additionally， the PSQI total score demonstrated positive correlations with brain load （r_s=0.340， P<0.01）， emotional stress （r_s=0.322， P<0.01）， and cerebral fatigue （r_s=0.229， P<0.05）. ConclusionFrontal EEG-derived indices， including brain load， emotional stress， cerebral fatigue and cerebral vitality， may serve as objective markers for reflecting anxiety， depression， and sleep quality in adolescents with emotional disorders. ［Funded by Beijing High level Innovation and Entrepreneurship Talent Support Program （number， 202504841041）； Horizontal Joint Project］

Objective:By analyzing peripheral blood indicators, we constructed and validated a novel lung cancer prediction model using machine learning algorithms for riskassessment of lung cancer.Methods:A retrospective case-control design was conducted on the clinical data of 194 newly diagnosed lung cancer patients [mean age: (66.80±9.09) years, 126 males and 68 females] admitted to Qilu Second Hospital of Shandong University between January 9, 2020, and December 31, 2024, serving as the case group. During the same period, 290 healthy individuals undergoing physical examinations [mean age: (61.18±14.31) years, 155 males and 135 females J were enrolled as the control group. A total of 46 peripheral blood indicators-including routine blood tests, coagulation parameters, liver function markers, and tumor-related indices-along with two basic characteristics (age and sex) were included in the analysis. Eleven machinelearning algorithms including logistic regression, randomforest, support vector classifier, extreme gradient boosting, gradient boosting decision tree, decision tree, multilayer perceptron, linear discriminant analysis, adaptive boosting, Gaussian naive Bayes and light gradient-boosting machine-were trained for early diagnosis of lung-cancer.Model performance was evaluated by the area under the ROC, accuracy, positive predictive value, negative predictive value, F1-score and 95% confidence interval (95% CI). The best performing algorithm was selected, and feature importance was ranked with Shapley Additive Planation(SHAP) values. Results:The support-vector classifier achieved the best performance for predicting lung-cancer risk (AUC=0.974; 95 % CI 0.951-0.989) and was retained for final model establishment. After 20 rounds of stratified 10-fold cross-validation the mean AUC was 0.950; learning-curve, decision-curve and calibration analyses confirmed its superior generalizability, clinical utility and calibration.SHAPley additive explanations and decision-tree feature importance consistently identified neuron-specific enolase, carcinoembryonic antigen, and squamous-cell carcinoma antigen as the three most critical predictors of lung-cancer risk. Conclusion:An SVM-based lung cancer prediction model was successfully established to determine the risk of developing lung cancer.

Home rehabilitation is the main rehabilitation model for elderly patients with hip fractures in China, and the application of digital health technology shows great potential in improving the quality of home rehabilitation for this population. This paper describes the concept of digital health technology, the current application status of different types of digital health technology in home rehabilitation for elderly patients with hip fractures, and discusses existing issues and future prospects, aiming to provide a reference for digital home rehabilitation nursing for elderly hip fracture patients.

Global public health faces substantial challenges from malignant tumors and infectious diseases. Vaccination provides an approach for treating and preventing these diseases. Oral vaccinations are particularly advantageous in disease treatment and prevention due to their non-invasive nature, high patient compliance, convenience, cost-effectiveness, and capacity to stimulate comprehensive and adaptive immune responses. However, the overwhelming majority of oral vaccines remain in experimental development, struggling with clinical and commercial translation due to their suboptimal efficacy. Thus, enhancing scientists' understanding of the interaction between vaccines and gastrointestinal immune system, creating antigen delivery systems suitable for the gut mucosal environment, developing more potent antigenic epitopes, and using personalized combination therapies are critical for advancing the next generation of oral vaccines. This article explores the fundamental principles and applications of current oral anti-tumor and anti-infective vaccines and discusses considerations necessary for designing future oral vaccines.

From the natural-philosophical model of medicine in ancient Greece to the modern bio-psycho-social par-adigm,the evolution of medical models has always been shaped by philosophy.In classical antiquity,medicine and philosophy were closely intertwined;the natural-philosophical model,exemplified by Hippocrates,emphasized the human body as an integrated whole.During the Renaissance,the rise of experimental science challenged traditional philosophical systems and pushed medicine toward empirical investigation.In the late twentieth century,the bio-psycho-social model emerged to address the limitations of the purely biomedical approach.In the twenty-first centu-ry,the advent of artificial intelligence is driving a new transformation in medicine and continually prompting reflec-tion on the future direction of medical models and their underlying philosophical significance.

OBJECTIVE To explore the construction path and effectiveness of dynamic zoning management model during dengue fever pandemic,and to provide evidence for optimizing hospital-acquired infection control strategies.METHODS Retrospective analysis method was conducted,the practical data of dengue fever epidemic prevention and control in a tertiary general hospital in 2024 as the sample,to evaluate the application effect of the"zoned treatment-dynamic allocation-environmental coordination"trinity prevention and control model.Based on the optimized infection prevention and control strategies implemented during the epidemic,such as the"core ward-specialist collaboration"dynamic zoning,flexible ward expansion,hierarchical disinfection,real-time dynamic re-source allocation mechanism,and precise environmental intervention,a comprehensive evaluation of prevention and control efficiency was conducted across key dimensions including infection control,resource utilization,pre-vention and control costs and patient outcomes.RESULTS Through the construction of flexible wards,the number of expanded isolation beds accounted for 44.13％(331/750)of the total beds,including 144 beds(19.20％)in core wards and 187 beds(24.93％)in specialist collaborative wards.The expansion of specialist collaborative wards increased the isolation admission capacity by 129.86％.The two types of wards admitted 57.27％of single-disease dengue patients and 42.73％of isolated patients with combined diagnosis and treatment needs from inter-nal medicine,surgery,obstetrics,gynecology,and pediatrics.The minimum ratio of flexible buffer isolation beds was 6.34％(21/331),with a maximum daily treatment capacity of 310 patients.Data showed:hospital infec-tion incidence rate was 0,peak adult mosquito density was 0.13 mosquitoes/trap·night,prevention and control cost was 95.22 yuan per case,and patient satisfaction increased by 1.98％(95.09 vs.93.24,P=0.014).CONCLUSIONS The"dynamic zoning"model achieves rapid spatial elastic reconstruction of inpatient wards for"peace-epidemic conversion"through the coordination of three links.Based on effectively blocking in-hospital transmission,it ensures the needs of multi-specialty treatment,enabling the hospital to strike a balance between the bottom line of prevention and control safety and the fulfillment of diversified medical service requirements dur-ing the epidemic outbreak period.It can provide standardized prevention and control solutions for medical institu-tions to respond to public health emergencies of vector-borne infectious diseases,and achieve the goal of zero cross infection of hospital-acquired Dengue.

Home rehabilitation is the main rehabilitation model for elderly patients with hip fractures in China, and the application of digital health technology shows great potential in improving the quality of home rehabilitation for this population. This paper describes the concept of digital health technology, the current application status of different types of digital health technology in home rehabilitation for elderly patients with hip fractures, and discusses existing issues and future prospects, aiming to provide a reference for digital home rehabilitation nursing for elderly hip fracture patients.

Objective To study the correlation between expression levels of serum Spexin and Pannexin 1 protein,glucose and lipid metabolism and insulin resistance in elderly patients with diabetic cataract.Methods A total of 118 elderly diabetic cataract patients were selected as the case group,and 103 elderly diabetic non-cataract patients were selected as the control group who were treated in the same hospital during the same period.Serum levels of Spexin and Pannexin1 were detected by enzyme-linked immunosorbent assay(ELISA).Fasting blood glucose(FPG)and glycosylated hemoglobin(HbA1c)were measured.Lipid metabolism related indicators including triacylglycerol(TG),high density lipoprotein cholesterol(HDL-C),total cholesterol(TC)and low density lipoprotein cholesterol(LDL-C)were detected in two groups of patients.Insulin resistance related indexes:fasting insulin(FINS),insulin sensitivity index(ISI),insulin resistance index(HOMA-IR)and islet beta cell function index(HOMA-β)were also detected.The correlation between serum Spexin and Pannexin1 levels,glucolipid metabolism and ISI was detected by Pearson method.Receiver operating characteristic(ROC)curves were used to assess the predictive value of serum Spexin and Pannexin1 levels and their combination in the development of cataract in elderly diabetic patients.Results The level of Spexin was lower in the observation group than that in the control group,and the level of Pannexin1 was higher than that in the control group(P＜0.01).Levels of TG,TC,LDL-C,FPG,HbA1c,FINS and HOMA-IR of the observation group increased compared with the control group,while HDL-C,ISI and HOMA-β levels decreased compared with the control group(P＜0.01).Pearson correlation analysis showed that serum Spexin level was positively correlated with ISI and HOMA-β,and negatively correlated with TG,TC,LDL-C,FPG,HbA1c,FINS and HOMA-IR in elderly patients with diabetic cataract(P＜0.05).Serum Pannexin1 was negatively correlated with ISI and HOMA-β,and positively correlated with TG,TC,LDL-C,FPG,HbA1c,FINS and HOMA-IR(P＜0.05).There was no correlation between the above indexes and HDL-C(P＞0.05).The combination of both serum Spexin and Pannexin1 predicted the development of cataracts in elderly diabetic patients was better than serum Spexin and Pannexin1 assessed individually(Z both combination-Spexin=3.220,P=0.001,Z both combination-Pannexin1=4.838,P＜0.001).Conclusion In elderly patients with diabetic cataract,the expression of serum Spexin decreases and the expression of Pannexin1 increases,and they have a certain correlation with glucose and lipid metabolism and insulin resistance