Objective To explore the effectiveness of a hospital-to-home transitional health management program for caregivers of children with severe encephalitis,aiming to provide a reference for optimizing transitional care models for these patients.Methods A convenience sampling method was used to select 84 children with severe encephalitis and their caregivers admitted to the neurology department of a tertiary hospital in Zhengzhou between March 2023 and June 2024.According to the admission time,they were divided into an experimental group and a control group,with 42 cases in each group.The experimental group received a hospital-to-home transitional health management program in addition to routine care,while the control group received standard care and discharge instructions.Differences in caregivers' perceived benefits,caregiver burden,disease management ability,and post-intervention outcomes of the children were compared between the 2 groups before and after the intervention.Results All 42 participants in both groups completed the study.After the intervention,the experimental group reported higher perceived benefits of(91.29±9.76)compared to(84.81±12.86)in the control group,lower caregiver burden of(48.55±7.15)compared to(54.71±11.23)in the control group,greater disease management ability of(41.83±4.97)than(37.79±5.23)in the control group,and lower difficulty in disease management of(31.52±7.82)compared to(34.55±3.96)in the control group,with all differences being statistically significant(P＜0.05).No statistically significant difference was found in the prognosis of the children between the 2 groups(P=0.500).Conclusion The hospital-to-home transitional health management program can effectively enhance caregivers'perceived benefits and disease management capabilities,reduce their caregiving burden,and provide scientific evidence for optimizing transitional care for children with severe encephalitis.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective:To investigate the longitudinal mutual predictive relationship between college students' psychological capital and meaning in life across time.Methods:A total of 604 college students were selected for a one-year two-stage longitudinal tracking(T1 and T2),using the Psychological Capital Questionnaire for Adolescent Students(PC-QAS)and Chinese Meaning in Life Questionnaire(C-MLQ)for measurement.Results:The T1 PC-QAS scores were positively correlated with the T1 C-MLQ scores and T2 PC-QAS scores(r=0.61,0.51,Ps＜0.001).The T2 C-MLQ scores were positively correlated with the T2 PC-QAS scores and T1 C-MLQ scores(r=0.58,0.57,Ps＜0.001).The results of cross lagged regression analysis indicated that the T1 PC-QAS scores posi-tively predicted the T2 C-MLQ scores(β=0.15,P＜0.001),and T1 C-MLQ scores positively predicted T2 PC-QAS scores(β=0.19,P＜0.01).Conclusion:The psychological capital and meaning in life of college students have a certain degree of stability,and the two could predict each other.

BACKGROUND:Understanding the characteristics and influencing factors of the gut microbiota in athletes can help determine the optimal gut microbial composition for relevant sport events.Further investigation in this area could provide important insights for improving athletic performance and recovery as well as developing personalized nutrition prescriptions.OBJECTIVE:To summarize the characteristics of gut microbiota in athletes,and to elucidate the important factors influencing the gut microbiota characteristics of athletes from the perspectives of exercise training and dietary intake.METHODS:A literature search was conducted using the PubMed,ScienceDirect,CNKI,WanFang and VIP databases for publications from 2004 to 2024.The search terms included"microbiota,microbiome,athlete,exercise,training,diet,nutrition,dietary fiber,protein,ketogenic,fat"in English and Chinese.After excluding studies of poor quality and irrelevant content,a total of 65 articles were included for review and analysis.RESULTS AND CONCLUSION:(1)The gut microbiota of elite athletes differs from that of the general population,characterized by increased α-diversity,elevated Firmicutes/Bacteroidetes ratio,increased abundance of beneficial bacteria,and enrichment of functional pathways contributing to athletic performance.(2)The type of sport and training load are closely related to the species structure and functional expression of the gut microbiota in athletes.(3)The bidirectional communication between the host and gut microbiota mediated by metabolites is an important mechanism by which exercise influences the gut microbiota.(4)Phase training typically induces adaptive changes in the gut microbiota,and alterations in the structure or function of the microbiota have lasting effects.(5)The type,quantity,and combination of macronutrients intake can significantly influence the structure and function of the gut microbiota,and interact synergistically or antagonistically with exercise training.(6)In the future,it is important to continue the exploration of the gut microbiota in athletes,clarify causal relationships,and establish new targets for exercise training interventions.

Objective:To explore the relationship between trait mindfulness and nomophobia in middle school students.Methods:A total of 942 middle school students were selected to use the Mindfulness Attention Awareness Scale and the Nomophobia Scale for Chinese for two data collection intervals of 12 months(T1 and T2,respective-ly).Results:The MAAS scores were higher at T1 than at T2(P＜0.001).The simultaneous(r=-0.11,-0.21,Ps＜0.01)and sequential(r=-0.14,-0.15,Ps＜0.001)correlations between MAAS scores and NSC scores were significant.The MAAS scores at T1 negatively predicted the NSC scores at T2(β=-0.09),and the NSC scores at T1 also negatively predicted the MAAS scores at T2(β=-0.10).Conclusion:It suggests that trait mind-fulness and nomophobia could predict each other in middle school students.

Objective:To investigate the longitudinal mutual predictive relationship between college students' psychological capital and meaning in life across time.Methods:A total of 604 college students were selected for a one-year two-stage longitudinal tracking(T1 and T2),using the Psychological Capital Questionnaire for Adolescent Students(PC-QAS)and Chinese Meaning in Life Questionnaire(C-MLQ)for measurement.Results:The T1 PC-QAS scores were positively correlated with the T1 C-MLQ scores and T2 PC-QAS scores(r=0.61,0.51,Ps＜0.001).The T2 C-MLQ scores were positively correlated with the T2 PC-QAS scores and T1 C-MLQ scores(r=0.58,0.57,Ps＜0.001).The results of cross lagged regression analysis indicated that the T1 PC-QAS scores posi-tively predicted the T2 C-MLQ scores(β=0.15,P＜0.001),and T1 C-MLQ scores positively predicted T2 PC-QAS scores(β=0.19,P＜0.01).Conclusion:The psychological capital and meaning in life of college students have a certain degree of stability,and the two could predict each other.

Objective:To explore the relationship between trait mindfulness and nomophobia in middle school students.Methods:A total of 942 middle school students were selected to use the Mindfulness Attention Awareness Scale and the Nomophobia Scale for Chinese for two data collection intervals of 12 months(T1 and T2,respective-ly).Results:The MAAS scores were higher at T1 than at T2(P＜0.001).The simultaneous(r=-0.11,-0.21,Ps＜0.01)and sequential(r=-0.14,-0.15,Ps＜0.001)correlations between MAAS scores and NSC scores were significant.The MAAS scores at T1 negatively predicted the NSC scores at T2(β=-0.09),and the NSC scores at T1 also negatively predicted the MAAS scores at T2(β=-0.10).Conclusion:It suggests that trait mind-fulness and nomophobia could predict each other in middle school students.

Objective To explore the effectiveness of a hospital-to-home transitional health management program for caregivers of children with severe encephalitis,aiming to provide a reference for optimizing transitional care models for these patients.Methods A convenience sampling method was used to select 84 children with severe encephalitis and their caregivers admitted to the neurology department of a tertiary hospital in Zhengzhou between March 2023 and June 2024.According to the admission time,they were divided into an experimental group and a control group,with 42 cases in each group.The experimental group received a hospital-to-home transitional health management program in addition to routine care,while the control group received standard care and discharge instructions.Differences in caregivers' perceived benefits,caregiver burden,disease management ability,and post-intervention outcomes of the children were compared between the 2 groups before and after the intervention.Results All 42 participants in both groups completed the study.After the intervention,the experimental group reported higher perceived benefits of(91.29±9.76)compared to(84.81±12.86)in the control group,lower caregiver burden of(48.55±7.15)compared to(54.71±11.23)in the control group,greater disease management ability of(41.83±4.97)than(37.79±5.23)in the control group,and lower difficulty in disease management of(31.52±7.82)compared to(34.55±3.96)in the control group,with all differences being statistically significant(P＜0.05).No statistically significant difference was found in the prognosis of the children between the 2 groups(P=0.500).Conclusion The hospital-to-home transitional health management program can effectively enhance caregivers'perceived benefits and disease management capabilities,reduce their caregiving burden,and provide scientific evidence for optimizing transitional care for children with severe encephalitis.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

BACKGROUND:Understanding the characteristics and influencing factors of the gut microbiota in athletes can help determine the optimal gut microbial composition for relevant sport events.Further investigation in this area could provide important insights for improving athletic performance and recovery as well as developing personalized nutrition prescriptions.OBJECTIVE:To summarize the characteristics of gut microbiota in athletes,and to elucidate the important factors influencing the gut microbiota characteristics of athletes from the perspectives of exercise training and dietary intake.METHODS:A literature search was conducted using the PubMed,ScienceDirect,CNKI,WanFang and VIP databases for publications from 2004 to 2024.The search terms included"microbiota,microbiome,athlete,exercise,training,diet,nutrition,dietary fiber,protein,ketogenic,fat"in English and Chinese.After excluding studies of poor quality and irrelevant content,a total of 65 articles were included for review and analysis.RESULTS AND CONCLUSION:(1)The gut microbiota of elite athletes differs from that of the general population,characterized by increased α-diversity,elevated Firmicutes/Bacteroidetes ratio,increased abundance of beneficial bacteria,and enrichment of functional pathways contributing to athletic performance.(2)The type of sport and training load are closely related to the species structure and functional expression of the gut microbiota in athletes.(3)The bidirectional communication between the host and gut microbiota mediated by metabolites is an important mechanism by which exercise influences the gut microbiota.(4)Phase training typically induces adaptive changes in the gut microbiota,and alterations in the structure or function of the microbiota have lasting effects.(5)The type,quantity,and combination of macronutrients intake can significantly influence the structure and function of the gut microbiota,and interact synergistically or antagonistically with exercise training.(6)In the future,it is important to continue the exploration of the gut microbiota in athletes,clarify causal relationships,and establish new targets for exercise training interventions.