OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

In recent years， the incidence of depression among adolescents has increased significantly， becoming a serious global public health problem. There is a close relationship between adolescent depression and lipid metabolism abnormalities. However， the mechanisms underlying this association have not been fully elucidated. This article provides a review of the mechanisms and interventions linking adolescent depression and lipid metabolism abnormalities， aiming to offer new ideas for interventions in adolescent depression. Using computerized searches of the China National Knowledge Infrastructure （CNKI）， Wanfang Data， PubMed， and Web of Science， studies on the mechanisms and interventions related to adolescent depression and lipid metabolism were collected， with the search period extending up to October 10， 2025. Two researchers independently conducted literature screening and extracted basic information from the included studies. A total of 12 original studies were included. The results indicate that adolescent depression is associated with certain lipid metabolites. Neurobiological factors， immune inflammation， oxidative stress， and gut microbiota imbalance play important roles in this association. In addition， lipid metabolites such as omega-3 polyunsaturated fatty acids （ω-3 PUFAs） have a significant ameliorating effect on adolescent depression. ［Funded by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties （number， SZGSP013）； Shenzhen Science and Technology Program （number， 20210617155253001）］

ObjectiveTo investigate the effect of repetitive peripheral magnetic stimulation (rPMS) combined with upper limb intelligent robotic training on muscle tension, motor function and cortical excitability in children with unilateral spastic cerebral palsy (USCP). MethodsFrom March, 2023 to December, 2024, 90 children with USCP admitted to Children's Hospital of Soochow University were selected and randomly divided into control group (n = 30), rPMS group (n = 30) and combined group (n = 30). The control group received conventional occupational therapy. The rPMS group received rPMS intervention followed by conventional occupational therapy. The combined group received rPMS followed by upper limb intelligent robot training, for four weeks. Before and after treatment, muscle tension of biceps brachii was assessed using the modified Ashworth Scale (MAS); upper limb motor function was evaluated using the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and upper limb intelligent parameters; and cortical excitability was measured using transcranial magnetic stimulation (TMS), including resting motor threshold (RMT) and motor-evoked potential (MEP) amplitude of the affected hemisphere. ResultsAfter treatment, MAS grades improved in all groups (|Z| > 3.523, P < 0.001), and the improvement in the combined group was superior to that in the control group (P < 0.05). Significant intra-group (F > 65.21, P < 0.001), inter-group (F > 17.94, P < 0.001) and interaction effects (F > 5.36, P < 0.01) were observed in FMA-UE scores, upper limb intelligent parameters and TMS parameters. Post Hoc analysis showed that the combined group demonstrated significantly greater improvements in FMA-UE scores, upper limb intelligent parameters, and TMS parameters compared with both the control and rPMS groups (all P < 0.01). Except for FMA-UE scores, the rPMS group showed significantly greater improvements than the control group in upper limb intelligent parameters (mechanical feedback, trajectory, and range of motion) and TMS parameters (RMT and MEP amplitude) (P < 0.05). ConclusionrPMS combined with upper limb intelligent robotic training can reduce upper limb muscle tension, improve motor function, and enhance cortical excitability in children with USCP.

ObjectiveThis paper aims to observe the syndrome improvement and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome）. MethodsThrough a multi-center randomized controlled methodology design，confirmed influenza cases were collected from October 2022 to April 2023 in the pediatrics department of eight hospitals，such as Dongfang Hospital of Beijing University of Chinese Medicine. A total of 180 children with influenza and heat accumulation in the lung and stomach syndrome conforming to the standard were recruited through the clinic. The sick children meeting the inclusion criteria were randomly divided into groups by a block-randomized method. The children in the experimental group were treated with Qingxuan Daozhi formula for five days，and those in the control group were treated with Oseltamivir Phosphate Granules for five days. The primary efficacy indicator was the negative conversion rate of influenza antigen detection. Secondary efficacy indicators were the Canadian acute respiratory illness and flu scale （CARIFS） and the incidence of complications，severe cases， and critical cases. Follow-up observation was conducted on the day of enrollment，48 hours after medication，72 hours after medication， and （6+1） d after medication. ResultsOne hundred and eighty participants were randomly assigned to the experimental group （90 cases） or the control group （90 cases）. All participants were followed up during the study. Comparison of influenza antigen detection results in the primary efficacy indicators showed that the average time of negative influenza antigen conversion in the experimental group was （5.29±1.25） d，and that in the control group was （5.40±1.68） d，without a statistically significant difference. After five days of intervention，52 cases in the experimental group and 51 cases in the control group converted to negative，without a statistically significant difference. CARIFS score results in the secondary efficacy indicators showed that during 72 hours after intervention，there were statistically significant differences between the experimental group and the control group in three dimensions， including headache，muscle soreness， and the need for extra care （P<0.05）. On the （6+1） days after the intervention，the differences in both the experimental group and the control group were statistically significant in 10 dimensions， including sore throat，bad sleep，uncomfortable feeling，poor spirit and fatigue，crying more than usual，the need for extra care，symptom，function，influence on parents，and total score （P<0.05）. The comparison results within the group in the dimensional scores of symptom， function， and influence on parents，as well as the CARIFS total score showed that with the delay of follow-up time，scores of both groups decreased significantly，with a statistically significant difference （P<0.01）. Inter-group comparison results showed that the mean score of the experimental group was higher than that of the control group at the time of enrollment. With the progress of intervention，the score of the experimental group was significantly decreased compared with that of the control group. At the end of follow-up，the mean score of the experimental group was lower than that of the control group，with no statistically significant difference. In terms of the incidence of complications，severe cases， and critical cases， there were no complications，severe cases， and critical cases in the two groups，without a statistically significant difference. ConclusionThe symptom improvement effect and negative antigen conversion rate of Qingxuan Daozhi formula in the treatment of influenza in children （heat accumulation in the lung and stomach syndrome） are not inferior to Oseltamivir Phosphate granules， and children's acceptance is better. It can be more widely used in clinical treatment of influenza in children （heat accumulation in the lung and stomach syndrome）.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

The American Heart Association (AHA) and the American College of Cardiology (ACC), in collaboration with multiple professional organizations, jointly released the "Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults" in August 2025. Based on the latest evidence-based medical findings from February 2015 to January 2025, the guideline proposes an individualized treatment strategy grounded in total cardiovascular disease risk stratification, incorporates the novel PREVENT risk assessment model, lowers the medication initiation threshold and control targets for high-risk populations, and provides specific management recommendations for special populations. This article provides an interpretation of these updates and conducts a comparative analysis with the current status of hypertension prevention and treatment in China as well as Chinese guidelines, aiming to offer reference for hypertension control practices in China.

The American Heart Association (AHA) officially released the "2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association" on January 27, 2025. This report systematically compiles the latest statistics on major cardiovascular diseases worldwide, while simultaneously integrating relevant outcome indicators, including quality of care, procedures, and economic costs, and updating the global prevalence patterns and evolving trends of diverse risk factors impacting cardiovascular health, providing essential guidance for the prevention, diagnosis, and treatment of cardiovascular diseases. Synthesizing insights from this pivotal report and other relevant studies, this article highlights key findings concerning the global prevalence and mortality of heart diseases, associated risk factors, and emerging diagnostic and therapeutic technologies.

ObjectiveTo investigate the effect of different visual inputs and task conditions on balance function and lower limb biomechanical characteristics in children with spastic hemiplegia. MethodsFrom March to July, 2025, 30 children aged six to nine years old with spastic hemiplegia (hemiplegia group) and 30 healthy children (control group) were selected. A 2×2×2 mixed experimental design was employed, involving groups (hemiplegia vs. healthy), tasks (single-task vs. dual-task), and vision (eyes open vs. eyes closed). One week before test, they were evaluated with Fugl-Meyer Assessment-Lower Extremities (FMA-LE), Wee Function Independent Measurement (WeeFIM) and Wechsler Intelligence Scale for Children, Fourth Edition (WISC-Ⅳ). A 3D gait analysis system and a plantar pressure testing system were used to collect spatio-temporal parameters of walking speed, stride length, cadence and step width, kinematic parameters of hip/knee/ankle joint angles, kinetic parameters of forefoot loading ratio, center of pressure (CoP) displacement, while dual-task cost (DTC) was caculated. ResultsSpatio-temporal parameters showed that under dual-task and eyes-closed conditions, walking speed and stride length decreased while step width increased in the hemiplegia group. Significant interaction effects among group, task, and vision were observed for speed, stride length, and step width (F > 4.886, P < 0.05). Kinematic parameters indicated that during dual-tasks, the hemiplegia group exhibited increased hip flexion and decreased ankle dorsiflexion; under eyes-closed conditions, knee flexion increased. The interaction of the three factors significantly affected all joint angles (F > 4.876, P < 0.05). Kinetic parameters showed that under dual-task and eyes-closed conditions, the forefoot loading ratio and anteroposterior CoP displacement decreased, while mediolateral CoP displacement increased. The interaction of the three factors significantly affected CoP displacement (F > 4.355, P < 0.05). All the DTC was significantly higher in the hemiplegia group than in the control group, except DTC of the cadence (|t| > 14.393, P < 0.001). Correlation analysis revealed that the score of FMA-LE was strongly negatively correlated with DTC (|r| > 0.731, P < 0.01). The Functional Independence Measure for Children and Working Memory Index showed moderate negative correlations with the DTC of walking speed and cadence (|r| > 0.462, P < 0.05). ConclusionThe gait and balance of children with spastic hemiplegia are concurrently influenced by dual-tasking and visual input. The superposition of visual deprivation and dual-tasks significantly exacerbates gait abnormalities. Furthermore, is strongly correlated with motor function and working memory.

ObjectiveTo investigate the effect of different visual inputs and task conditions on balance function and lower limb biomechanical characteristics in children with spastic hemiplegia. MethodsFrom March to July, 2025, 30 children aged six to nine years old with spastic hemiplegia (hemiplegia group) and 30 healthy children (control group) were selected. A 2×2×2 mixed experimental design was employed, involving groups (hemiplegia vs. healthy), tasks (single-task vs. dual-task), and vision (eyes open vs. eyes closed). One week before test, they were evaluated with Fugl-Meyer Assessment-Lower Extremities (FMA-LE), Wee Function Independent Measurement (WeeFIM) and Wechsler Intelligence Scale for Children, Fourth Edition (WISC-Ⅳ). A 3D gait analysis system and a plantar pressure testing system were used to collect spatio-temporal parameters of walking speed, stride length, cadence and step width, kinematic parameters of hip/knee/ankle joint angles, kinetic parameters of forefoot loading ratio, center of pressure (CoP) displacement, while dual-task cost (DTC) was caculated. ResultsSpatio-temporal parameters showed that under dual-task and eyes-closed conditions, walking speed and stride length decreased while step width increased in the hemiplegia group. Significant interaction effects among group, task, and vision were observed for speed, stride length, and step width (F > 4.886, P < 0.05). Kinematic parameters indicated that during dual-tasks, the hemiplegia group exhibited increased hip flexion and decreased ankle dorsiflexion; under eyes-closed conditions, knee flexion increased. The interaction of the three factors significantly affected all joint angles (F > 4.876, P < 0.05). Kinetic parameters showed that under dual-task and eyes-closed conditions, the forefoot loading ratio and anteroposterior CoP displacement decreased, while mediolateral CoP displacement increased. The interaction of the three factors significantly affected CoP displacement (F > 4.355, P < 0.05). All the DTC was significantly higher in the hemiplegia group than in the control group, except DTC of the cadence (|t| > 14.393, P < 0.001). Correlation analysis revealed that the score of FMA-LE was strongly negatively correlated with DTC (|r| > 0.731, P < 0.01). The Functional Independence Measure for Children and Working Memory Index showed moderate negative correlations with the DTC of walking speed and cadence (|r| > 0.462, P < 0.05). ConclusionThe gait and balance of children with spastic hemiplegia are concurrently influenced by dual-tasking and visual input. The superposition of visual deprivation and dual-tasks significantly exacerbates gait abnormalities. Furthermore, is strongly correlated with motor function and working memory.

Congenital blood group chimerism refers to the coexistence of two or more distinct blood types within an individual, resulting from the presence of hematopoietic cell populations with different genotypes. Consequently, red blood cells in such individuals may express different blood group antigens. Based on the timing and mechanism of formation, blood group chimerism can be classified as either congenital or acquired. Although congenital blood group chimerism is rare and involves complex mechanisms, it holds significant implications in transfusion medicine, transplantation, and obstetrics. This article reviews the formation mechanisms, detection methods, and clinical significance of congenital blood group chimerism in transfusion medicine. Particular emphasis is placed on the principles, advantages, and limitations of various detection techniques. Furthermore, the potential applications of these technologies in clinical diagnosis are discussed, providing a technical foundation for the development of precise transfusion strategies.