Abstract To improve adolescent health behavior, the study summarizes and analyzes the performance, pathways of transmission, and influencing factors of the intergenerational transmission of adolescent health behaviors from the perspective of intergenerational transmission. The study emphasizes the need to deepen research on the intergenerational transmission of adolescent health behaviors, promote multidisciplinary and cross team collaboration, and shift adolescent health care from individual focused care to a holistic approach that prioritizes family and community culture. Simultaneously, an action framework should be established to block the intergenerational transmission of health risk behaviors, with a focus on childhood and adolescence. Additionally, parent-child participatory health education and health promotion activities should be carried out under a tripartite coordinated intervention model involving the community, school, and family, collectively fostering the development of healthy behaviors among adolescents.

Objective To provide a reference for the establishment, development and application of the adverse drug reaction （ADR） automated monitoring system, through verifying and quantifying the research hotspots and advantages of the system by CiteSpace software and systematic review. Methods Literature on ADR automated monitoring up to December 2023 were retrieved and screened from CNKI and web of science databases. CiteSpace 6.4.R1 software was used to conduct co-occurrence, clustering and emergence analysis, and to visualize and comparatively analyze the research hotspots, rules and distribution in the field of automated monitoring of ADR at home and abroad. In compliance with the preferred reporting items for systematic reviews and Meta-analyses （PRISMA）, literature covering publications in English and Chinese including detection rates of ADR collected using Incident Reporting Systems （IRSs） and/or automated monitoring systems were retrieved and screened. The advantages and disadvantages of automated monitoring systems were analyzed by comparing the differences between these two systems in terms of the number of ADR reports and the types of positive signals. Results A total of 56 articles in English and 80 articles in Chinese were indexed by CiteSpace. The research hotspots in recent years included data mining, deep learning, text classification techniques, machine learning and so on. A total of seven studies compiled with the inclusion criteria for the systematic evaluation, all of which were completed between 1991 and 2021 in hospitals in four countries. 150 526 medical records were reviewed from 15 institutions. A total of 194 ADR reports were collected by IRSs. A total of 2 090 ADR reports were collected by the automated monitoring system over the same period, indicating a 977% increase in the number of ADR reports （P=0.0156） compared with the IRSs. Conclusion The ADR automatic monitoring system had significantly improved the level of drug risk identification and reduced costs, but it was necessary to optimize the algorithm, expand the data source and carry out standardization construction to overcome the current limitations.

Objective:To explore the clinical and biochemical characteristics of patients with hyperuricemia according to different pathophysiological subtypes. This may facilitate rapid identification of each subtype in clinical settings and provide evidence for personalized urate-lowering treatment.Methods:Patients diagnosed with hyperuricemia at the Department of Endocrinology and Metabolism, Tenth People′s Hospital of Tongji University between October 2015 and January 2024 were included. Based on 24-h urinary uric acid excretion(UUE) and the fractional excretion of uric acid(FEUA), patients were classified into four subtypes: renal uric acid underexcretion type(RUE), renal uric acid overload type(ROL), combined type and renal normal type. Clinical and biochemical variables-including sex, age, BMI, smoking history, comorbidities, blood glucose, and serum uric acid-were analyzed. Binary logistic regression was used to identify factors associated with each subtype.Results:Among 2 073 patients with hyperuricemia, 55.8% were RUE type, 6.9% were ROL type, 31.3% were combined type and 6.0% were renal normal type. RUE type had lower blood glucose levels and fewer cases of diabetes [ OR=0.685(95% CI 0.478-0.980), P<0.05]. ROL type showed a higher incidence of tophi, positively correlated with smoking history [ OR=1.672(95% CI 1.009-2.771), P<0.05], and negatively correlated with serum uric acid levels [ OR=0.994(95% CI 0.990-0.998), P=0.001]. Combined type had the youngest onset age, shortest disease duration, and the fewest comorbidities, and was associated with higher BMI [ OR=1.035(95% CI 1.001-1.070), P<0.05]. Renal normal type had the oldest age of onset, the highest proportion of female patients and comorbidities, and was associated with lower serum uric acid levels[ OR=0.994(95% CI 0.989-0.998), P=0.007], higher BMI[ OR=1.064(95% CI 1.003-1.129), P<0.05], and increased tophi incidence[ OR=2.261(95% CI 1.206-4.237), P=0.011]. Conclusion:Each pathophysiological subtype of hyperuricemia exhibits distinct clinical and biochemical characteristics, which may serve as useful references for subtype identification and personalized management in clinical practice.

Human Respiratory Stem Cells (RSCs) play a crucial role in the maintenance, repair and regeneration of the respiratory system. As a novel therapeutic method, stem cell therapy is a popular research direction in the medical field. And with the in-depth research on the mechanism of pneumonia caused by respiratory infections in recent years, the use of RSCs to explore pneumonia caused by respiratory infections and its therapeutic strategies has become a hot topic. In this paper, we firstly outlined the types of RSCs, summarized the mechanism of pneumonia caused by respiratory tract infections, discussed the advantages of RSCs application and the progress of culture differentiation, and elaborated the therapeutic exploration of RSCs in pneumonia caused by respiratory tract infections.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective To analyze the association between eye-related behaviors and myopia among preschool chil-dren in Ningxia Hui Autonomous Region,and to develop a predictive nomogram model.Methods Using stratified cluster random sampling,36 062 preschool children from 400 randomly selected kindergartens in Ningxia Hui Autonomous Region were enrolled between October and December 2023.Primary caregivers of participants completed structured question-naires.Data were randomly split into a training set(n=25 243,70％)and a validation set(n=10 819,30％)in a ratio of 7∶3.The training set was used for model construction,and the validation set for external validation.Calibration plots and the Hosmer-Lemeshow(H-L)goodness-of-fit test assessed agreement between predicted and observed risks.Decision curve analysis(DCA)and clinical impact curve analysis(CICA)evaluated clinical utility.Results The myopia preva-lence among preschool children in Ningxia Hui Autonomous Region was 3.8％.A nomogram model based on binary Logistic regression showed area under the curve(AUC)values of 0.88(95％CI:0.87-0.89)for the training set and 0.89(95％CI:0.87-0.90)for the validation set,indicating strong discriminative ability.Calibration curves and H-L tests revealed good model fit(training set:x2=4.92,P=0.766;validation set:x2=5.52,P=0.961),with all P＞0.05.DCA and CICA confirmed clinical utility.The nomogram identified parental myopia,academic pressure,frequency of eye fatigue,daily screen time,and regular vision checks as the top five predictors of myopia.Conclusion The nomogram demonstrates promising potential for predicting myopia risk in preschool children in Ningxia Hui Autonomous Region,serving as a robust tool for clinical and educational myopia risk assessment.

Objective:To explore the application of nursing intervention based on the capability, opportunity, motivation-behavior model (COM-B) in stroke patients during the rehabilitation period.Methods:Using the convenience sampling method, 146 stroke patients admitted to the First Affiliated Hospital of Zhengzhou University from February 2021 to February 2024 were selected as the research objects. According to the admission time sequence, they were divided into the COM-B group and the routine group, with 73 cases in each group. The routine group received routine nursing measures, and the COM-B group was intervened with the COM-B intervention, both for 3 months. The scores of the Health Education Self-Management Scale for Stroke Patients (HES-SP), the Exercise of Self-Care Agency Scale (ESCA), the Fugl-Meyer Assessment (FMA), and the Barthel Index of the two groups before and after the intervention were compared.Results:Finally, 72 patients in the COM-B group and 71 patients in the routine group completed the study. After 3 months of intervention, the scores of ESCA, HES-SP, FMA, and the Barthel Index of the two groups were all higher than those before the intervention, and the scores of COM-B group were all higher than those of the routine group, and the differences were statistically significant ( P<0.05) . Conclusions:The application of the COM-B intervention in stroke patients can improve patients' healthy behaviors and self-care abilities, and enhance their limb motor function and activities of daily living abilities, with a good application effect.

Rare diseases pose significant diagnostic and therapeutic challenges,carrying a high disease burden,their management critically reflects a nation's public health resilience.Currently,China faces key challenges such as scarce treatments,fragmented services,and low drug accessibility in rare disease care,which urgently require systemic solutions.Digital-intelligent technology as a key breakthrough are expected to resolve the challenges in this field.Although its application in the field of rare diseases is gradually expanding,there is a lack of systematic compilation of studies to elucidate how to precisely enhance the precision,synergy and sustainability of diagnosis and treatment.The key challenges in rare disease care concentrate in four areas:inefficiency in prenatal screening,uneven distribution of medical resources,low efficiency in social organization collaboration,and ineffective information dissemination.The"4C"strategy,based on digital-intelligent technology,can address these issues:①coordination,boost prenatal screening awareness and capacity via digital-intelligent platforms to strengthen prevention;②cooperation,deepen collaboration within specialist networks,empowering institutions to enhance diagnostic capacity;③co-creation,empower support organizations to optimize resources,efficiency;④cognition,minimize information dissipation through efficient platforms,improving patient and family quality of life.This establishes an integrated digital-intelligent rare disease model encompassing"screening-diagnosis-treatment-care".

Based on the core collection retrieval of the Web of Science database,researches related to the medicinal field of plant-derived vesicles(PDVs)were retrieved.The research hotspots and their changes in the pharmaceutical field of PDVs are visually analyzed by using bibliometric software VOSviewer and CiteSpace.A detailed discussion is held around the author,institution,country,key research hotspots and annual develop-ment hotspots,revealing the current research status of PDVs in the pharmaceutical field and predicting future trends,which provides valuable perspectives for researchers to understand the current research status of PDVs in the pharmaceutical field and discover possible unexplored areas in this field.