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.

Aim To explore the mechanism of luteolin in alleviating hepatic fibrosis.Methods C57BL/6 mice were randomly divided into the control group,CCl4 group,silybin group(100 mg·kg-1)and luteo-lin group(100 mg·kg-1).After 10-week modeling and 2-week treatment,the serum levels of aminotrans-ferase and liver histopathology were examined.Hepatic fibrosis and autophagy-related gene expression were as-sessed using immunohistochemistry and immunofluores-cence.Human hepatic stellate cell line(LX2)was cultured and divided into control,TGF-β1(10 mg·L-1),TGF-β1+silybin(40 μmol·L-1),TGF-β1+luteolin(40 μmol·L-1).Fibrotic and autophagy-re-lated markers were analyzed using quantitative real-time PCR,Western blot,immunofluorescence and MDC staining.Results Compared with the CCl4 group,the treatment groups showed significantly improved liver function and reduced hepatic fibrosis,with markedly downregulated COL1A1 and α-SMA expression,and luteolin demonstrated superior efficacy.Compared with TGF-β1 group,luteolin treatment significantly de-creased mRNA levels of COL1A1,ACTA2 and MAP1LC3B,while increasing the mRNA level of SQSTM1,the protein levels of COL1A1 and α-SMA de-creased,p62 was enhanced,the LC3Ⅱ/Ⅰ ratio was downregulated,and autophagy was reduced.These effects of luteolin were reversed by autophagy inducer rapamycin.Conclusion Luteolin alleviates liver fi-brosis by decreasing the autophagy of hepatic stellate cells.

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".

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".

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.

The objective of this study was to evaluate the protective effect and possible related mechanisms of Sophora subprostrate polysaccharide(SSP)on intestinal epithelial cell injury in-duced by Tert-Butyl hydroperoxide(TBHP).The optimal dose of TBHP and the safe concentra-tion range of SSP were determined using the MTT method.In this study,IPEC-J2 cells were divid-ed into five groups:the control group,the model group,the SSPL group,the SSPM group and the SSPH group,and the cell morphology,cell survival rate and LDH release rate were observed and measured.The content of intracellular reactive ROS was observed and determined by DCFH-DA staining.The content of MDA in the supernatant and the antioxidant index of cells were determined by the reagent kit.Transcriptome technology was employed to analyze the potential mechanisms by which SSP mitigates oxidative damage in IPEC-J2 cells.The results showed that treatment with 625 μmol/L TBHP for 2 h significantly reduced the activity of IPEC-J2 cells,markedly increased LDH release(P＜0.05),inhibited CAT superoxide SOD and glutathione GPX activities(P＜0.05),and significantly elevated MDA and ROS levels(P＜0.05).Compared to the model group,after SSP treatment,intracellular ROS levels were significantly reduced(P＜0.05),while CAT,SOD,and GPX activities were significantly increased(P＜0.05),and MDA content and LDH re-lease were significantly decreased(P＜0.05)in a dose-dependent manner.Transcriptome analysis revealed that TBHP treatment significantly altered the transcriptional profiles of IPEC-J2 cells,while SSP treatment could restore the transcriptional profiles of the damaged cells to a certain ex-tent.Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)indicated that the differentially expressed genes between the CC and TBHP groups were significantly enriched in oxidative phosphorylation,ribosome,and other pathways.Meanwhile,the differentially expressed genes between the SSP and TBHP groups were mainly enriched in oxidative phosphorylation,ap-optosis,glyoxylate and dicarboxylate metabolism,and other pathways.These results suggest that TBHP may disrupt normal oxidative respiration in IPEC-J2 cells by affecting oxidative phospho-rylation and interfering with metabolism pathways involving glycine,serine,and threonine,leading to oxidative damage in intestinal epithelial cells.Conversely,SSP treatment may potentially restore oxidative phosphorylation processes,alleviate lysosomal damage,reduce cell apoptosis,and miti-gate oxidative damage in intestinal epithelial cells through modulation of oxidative phosphoryla-tion,apoptosis,and lysosomal pathways.This discovery provides a theoretical basis for the clinical application of SSP in alleviating oxidative damage in the porcine intestinal tract.

Aim To explore the mechanism of luteolin in alleviating hepatic fibrosis.Methods C57BL/6 mice were randomly divided into the control group,CCl4 group,silybin group(100 mg·kg-1)and luteo-lin group(100 mg·kg-1).After 10-week modeling and 2-week treatment,the serum levels of aminotrans-ferase and liver histopathology were examined.Hepatic fibrosis and autophagy-related gene expression were as-sessed using immunohistochemistry and immunofluores-cence.Human hepatic stellate cell line(LX2)was cultured and divided into control,TGF-β1(10 mg·L-1),TGF-β1+silybin(40 μmol·L-1),TGF-β1+luteolin(40 μmol·L-1).Fibrotic and autophagy-re-lated markers were analyzed using quantitative real-time PCR,Western blot,immunofluorescence and MDC staining.Results Compared with the CCl4 group,the treatment groups showed significantly improved liver function and reduced hepatic fibrosis,with markedly downregulated COL1A1 and α-SMA expression,and luteolin demonstrated superior efficacy.Compared with TGF-β1 group,luteolin treatment significantly de-creased mRNA levels of COL1A1,ACTA2 and MAP1LC3B,while increasing the mRNA level of SQSTM1,the protein levels of COL1A1 and α-SMA de-creased,p62 was enhanced,the LC3Ⅱ/Ⅰ ratio was downregulated,and autophagy was reduced.These effects of luteolin were reversed by autophagy inducer rapamycin.Conclusion Luteolin alleviates liver fi-brosis by decreasing the autophagy of hepatic stellate cells.

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.