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.

Objective: To explore the association between warning signs of psychological and behavioral development problems with emotional and behavioral problems in preschool children in Bao an District, Shenzhen, so as to provide an empirical basis for optimizing psychological screening strategies in kindergartens. Methods: From September 2023 to August 2024, a total of 49 804 preschool children aged 4-6 years from all 401 kindergartens in Bao an District were enrolled as study subjects. The Warning Signs Checklist for Screening Psychological, Behavioral and Developmental Problems of Children and the parent version of the Strengths and Difficulties Questionnaire (SDQ) were used to assess children s developmental status and emotional and behavioral problems, respectively. Multivariable Logistic regression analysis was used to examine the association between warning signs and emotional and behavioral problems, stratified by sex. Results: The overall positive screening rate for developmental warning signs was 1.5%, and the detection rate for high risk in the SDQ total difficulties score was 6.3%. Multivariable Logistic regression analysis revealed that after adjusting for age, children who screened positive for warning signs exhibited a significantly higher risk of elevated SDQ total difficulties and subscale scores compared to those who screened negative, across both sexes (a OR boys =1.66-13.42, a OR girls =2.04-22.15, all P <0.01). The only exceptions were gross motor skills and conduct problems in boys, and personal social skills and conduct problems in girls. Notably, abnormalities in the personal social domain demonstrated the strongest association with emotional/behavioral problems (a OR boys =7.72-13.42, a OR girls =3.88-22.15), followed by the language domain (a OR boys =4.63-9.23, a OR girls =3.78-14.41) (all P <0.01). Conclusion A positive screening result for warning signs, particularly in the personal social and language domains, serves as a strong indicator of emotional and behavioral problems in preschool children.

BackgroundSchizophrenia， as a common chronic mental disorder， although second-generation antipsychotic drugs have shown significant efficacy in alleviating positive symptoms， the widespread cognitive dysfunction among patients remains a challenge in clinical treatment. Traditional Chinese medicine has unique advantages in the treatment of mental disorders. However， the current clinical research on the combination of Wendan Decoction and antipsychotic drugs for schizophrenia varies in quality， and there is a lack of systematic reviews evaluating its effects on cognitive improvement and safety. ObjectiveTo systematically evaluate the effects of modified Wendan Decoction combined with antipsychotic drugs on schizophrenia symptoms and cognitive improvement， providing evidence-based support for the clinical application of Wendan Decoction. MethodsLiterature searches were conducted in China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP Information， China Biomedical Literature Service System， China Clinical Trial Registry， PubMed， Web of Science， the Cochrane Library， and Embase to collect randomized controlled trials （RCTs） of modified Wendan Decoction combined with antipsychotic drugs for the treatment of schizophrenia. The search period was from the establishment of the databases to March 19， 2026. The quality of the included literature was evaluated using the Cochrane 6.3. Meta-analysis was performed using RevMan 5.3 software. ResultsA total of 11 studies were included， involving 1 130 patients with schizophrenia. Among them， 566 cases were in the study group （receiving modified Wendan Decoction combined with antipsychotic drugs）， and 564 cases were in the control group （receiving antipsychotic drugs only）. Meta-analysis showed that the effective rate of improvement in psychotic symptoms in the study group was higher than that in the control group （RR=1.21，95% CI： 1.15–1.27， P<0.01）. In terms of psychotic symptoms， the Positive and Negative Symptom Scale （PANSS） positive symptom score （MD=-3.69， 95% CI： -5.87–-1.51， P<0.01） and PANSS total score （MD=-9.20， 95% CI： -11.80–-6.59， P<0.01） of the study group were lower than those of the control group. In cognitive function assessments， the Mini-Mental State Examination （MMSE） score （MD=2.51， 95% CI： 1.33–3.68， P<0.01） and the Loewenstein Occupational Therapy Cognitive Assessment （LOTCA） score （MD=11.85， 95% CI： 2.55–21.15， P=0.010） of the study group were higher than those of the control group， and the Wisconsin Card Sorting Test （WCST） score was lower than that of the control group （MD=-9.34， 95% CI： -12.57–-6.11， P<0.01）. The levels of brain-derived neurotrophic factor （BDNF） （SMD=1.34， 95% CI： 0.63–2.05， P<0.01） and nerve growth factor （NGF） （MD=6.94， 95% CI： 4.00–9.89， P<0.01） of the study group were higher than those of the control group. In terms of safety， there was no statistically significant difference in the incidence of adverse reactions between the two groups （RR=0.60， 95% CI： 0.31–1.18， P=0.14）. ConclusionThe modified Wendan Decoction combined with antipsychotic drugs may be more effective than antipsychotic drugs alone in improving positive symptoms and cognitive function in patients with schizophrenia， and it also exerts a favorable neurotrophic regulatory effect. ［Funded by Postgraduate Education Reform and Quality Improvement Project of Henan Province （number， YJS2023AL060）； Key Scientific Research Projects of Higher Education Institutions in Henan Province （number， 24B320018， 25B310004）］

Objective To investigate the relationship between the comorbidity of dyslipidemia, hypertension and diabetes and lifestyle of adult residents in Hebei Province in 2018, and to provide reference for the development of chronic diseases prevention measures. Methods Using data from the surveillance of chronic diseases and their risk factors among adults in Hebei in 2018, 7 711 permanent residents aged ≥18 years were selected as the research subjects. Multivariate logistic regression analysis was used to analyze the relationship between the comorbidity of dyslipidemia, hypertension, and diabetes and lifestyle in adult residents. Results A total of 7 711 subjects aged ≥18 years were included in 2018. The comorbidity rates of hypertension and dyslipidemia, diabetes and dyslipidemia, and hypertension and diabetes were 16.88%, 7.91%, and 8.13%, respectively. The comorbidity rate of hypertension, diabetes, and dyslipidemia was 22.65%. These comorbidity rates increased with age and BMI. Multivariate logistic regression results showed that male, age, overweight/obesity, physical inactivity, daily sedentary behavior time ≥5 hours, and sleep problems were risk factors for the comorbidity of the "three highs". Conclusion The prevalence of the "three highs" comorbidities is relatively high in Hebei Province, and there are multiple common risk factors. Maintaining a healthy lifestyle and implementing comprehensive prevention and control measures are the key to improving the health level of residents.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveTo investigate the role and mechanism of ribosomal DNA （rDNA） transcription and ribosome biogenesis in muscle atrophy induced by acute kidney injury （AKI）. MethodsEight male C57BL/6 mice were randomly divided into a control group （Ctrl） and a model group （AKI）. An AKI model was established via intraperitoneal injection of cisplatin. Muscle atrophy was phenotypically assessed by measuring muscle mass， myofiber cross-sectional area （HE staining）， and mRNA expression levels of atrophy-related genes （Murf-1， Atrogin-1， Igf-1） using qRT-PCR. In vivo protein synthesis rates were determined via the SUnSET assay （puromycin incorporation）. Ribosome biogenesis was evaluated by assessing rRNA content and 47S pre-rRNA expression levels. Myotubes differentiated from mouse skeletal muscle cell lines （C2C12 myotubes） were treated with serum from AKI mice， and the effects on rDNA transcription， ribosome biogenesis， and protein metabolism were analyzed using chromatin immunoprecipitation followed by quantitative polymerase chain reaction （ChIP-qPCR） and Western blot. ResultsAKI successfully induced muscle atrophy， as evidenced by a significant reduction in skeletal muscle mass. The most pronounced decrease occurred in the extensor digitorum longus muscle （21.0%， P 0.01）， along with a trend toward reduced myofiber cross-sectional area. At the molecular level， AKI inhibited muscle protein synthesis （83.14% reduction in puromycin incorporation， P 0.000 1） and impaired ribosome biogenesis， manifested by suppressed rDNA transcription elongation （52.62% decrease in 47S pre-rRNA ITS-1 levels， P 0.01） and reduced total rRNA content （65.29%， P 0.000 1）. In contrast， serum from AKI mice promoted rDNA transcription initiation and protein synthesis in C2C12 myotubes in vitro. ConclusionAKI induces muscle atrophy by suppressing rDNA transcription and ribosome biogenesis in skeletal muscle， leading to impaired protein synthesis. Dysregulated ribosome biogenesis may play a critical role in AKI-induced muscle atrophy.

Objective:To investigate the main causes of failed endoscopic retrograde cholangiopancreatography (ERCP) in children and the remedial treatment strategies.Methods:This retrospective cohort study analyzed the clinical data of 21 children who experienced failed ERCP at Beijing Children′s Hospital, Capital Medical University between January 2021 and December 2024. Data was collected included demographic information, clinical diagnoses, and ERCP outcomes. The annually trend in the ERCP failure rate was analyzed. Patients were categorized by etiology into the following groups: chronic pancreatitis, post-surgical status, pancreatic trauma, pancreas divisum, non-neoplastic pancreatic lesions, and ulcerative colitis. The relationship between etiologies and ERCP failure was analyzed by Fisher exact test.Results:A total of 175 ERCP procedures were included, of which 21 procedures failed (12.0%). The failure rate decreased annually from 2021 to 2024: 18.2% (4/22), 13.2% (5/38), 11.4% (5/44), and 9.9% (7/71), respectively. The etiological distribution among the 21 failed cases was as follows: chronic pancreatitis 28.6% (6/21), pancreatic duct stenosis following pancreatic trauma 23.8% (5/21), post-surgical status 14.3% (3/21), pancreas divisum 9.5% (2/21), acute pancreatitis 9.5% (2/21), non-neoplastic pancreatic lesions 9.5% (2/21), and pancreatic duct stenosis associated with ulcerative colitis 4.8% (1/21). Remedial treatments included surgical intervention (2 cases), ultrasound-guided percutaneous drainage (3 cases), and repeat ERCP (2 cases). For post-surgical and pancreatic trauma patients, failure was primarily due to bile duct stenosis, pancreatic duct stenosis, or pancreaticobiliary duct disruption. Active surgical interventions such as cholangiojejunostomy and pancreaticojejunostomy, and ultrasound-guided drainage following ERCP failure led to significant clinical improvement.Conclusions:A stratified management approach guided by etiology is essential following failed ERCP in children. Early and active remedial treatment, particularly surgical interventions, is recommended for post-surgical and pancreatic trauma cases.

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.

[摘 要] 目的：开发新型溶瘤呼肠孤病毒（Reo）递送系统，以克服中和抗体对Reo的中和作用并提升其肿瘤靶向性。方法：通过切向流过滤联合超高速离心法制备自然杀伤细胞外泌体（NKexo），叶酸（FA）修饰后采用挤压法包载Reo，构建FA-NKexo-Reo递送系统；通过透射电镜（TEM）、纳米粒径分析、蛋白质印迹（WB）法、核磁共振氢谱及流式细胞术等技术表征其理化性质；采用CCK-8、流式细胞术、Transwell实验及激光共聚焦显微镜评估FA-NKexo-Reo递送系统体外细胞毒性及细胞摄取能力；通过人卵巢癌裸鼠皮下移植瘤模型评价FA-NKexo-Reo的肿瘤靶向性、疗效及安全性。结果：FA-NKexo-Reo粒径为（94.0 ± 28.5）nm，Zeta电位为（-21.26 ± 1.57）mV，包封率达（49.7 ± 15.6）%；在中和抗体的存在下，FA-NKexo-Reo对卵巢癌细胞SKOV3和A2780仍可表现出显著的细胞毒性（P < 0.01）；荷瘤鼠活体成像显示FA-NKexo-Reo肿瘤靶向性显著优于NKexo组，肿瘤抑制率提升60%（P < 0.001）。结论：成功制备FA-NKexo-Reo递送系统，在中和抗体的存在下，FA-NKexo-Reo可保护并靶向递送Reo到高表达叶酸受体的卵巢癌细胞，从而增强Reo的抗肿瘤作用。