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: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.

Background The quality of occupational health technical services is directly linked to the protection of workers' health rights and the efficacy of occupational disease prevention and control. However, the industry still faces critical challenges: sporadic instances of institutional non-compliance and persistent irregularities in professional practice continue to undermine overall service performance. Objective To assess the current quality status of occupational health technical services in Zhejiang Province and propose countermeasures for quality improvement, providing a scientific basis for policy optimization and service delivery quality enhancement. Methods A total of 69 occupational health technical service institutions in Zhejiang Province that obtained formal accreditation as of April 30, 2024, were sampled, including 3 public institutions and 66 private institutions (comprising 3 formerly Class-A, 28 formerly Class-B, 11 formerly Class-C, and 24 newly certified institutions). Following the Technical Protocol for Quality Monitoring of Occupational Health Technical Service in Zhejiang Province and the Technical Protocol for Proficiency Testing of Occupational Health Detection in Zhejiang Province, a quality assessment task force comprising national and provincial experts was established. Evaluation was conducted across four dimensions: qualification maintenance and compliance, standardization of technical services, authenticity of technical services, and proficiency testing, utilizing a combination of document review, on-site inspections, and technical skill assessments. Results The occupational health technical service institutions in Zhejiang Province were predominantly private entities (82.5%), with significant disparities in overall service quality. The pass rates for qualification maintenance and compliance, technical service standardization, technical service authenticity, and the excellence rate for laboratory proficiency testing were 81.5%, 80.7%, 97.3%, and 90.4%, respectively. Regarding qualification maintenance, the pass rates for "environmental conditions" (49.8%, 56.7%) and "instrumentation and equipment" (58.2%、65.6%) were significantly lower for formerly Class-C and newly certified institutions compared to other categories. In terms of technical standardization, "standardized on-site inspections" recorded the lowest pass rate (67.4%), with newly certified institutions at only 48.0%. Regarding technical service authenticity, formerly Class-C institutions exhibited issues such as missing raw chromatograms for blank samples (85.7% pass rate). In laboratory proficiency testing, public and formerly Class-A institutions achieved 100% excellence rates, but the performance of formerly Class-C and newly certified institutions was comparatively weak; specifically, the failure rate for organic analysis in formerly Class-C institutions reached 20%; the failure rate for dust testing items in newly certified institutions was 10.3%. Conclusion The overall quality of occupational health technical services in Zhejiang Province still requires significant improvement, particularly in basic institutional conditions, the standardization of on-site inspections, and laboratory proficiency in organic and dust analysis. Formerly Class-C and newly certified institutions should be the primary focus of quality management efforts. Differentiated regulatory strategies are recommended, alongside strengthening interim and ex-post supervision to gradually enhance the quality of occupational health technical services across all institutions.

By consulting ancient and modern literature， this article systematically reviews and verifies the historical evolution of the herbal medicine known as Baijiang across various dimensions， including name， origin， scientific name verification， medicinal parts， production area， quality， harvesting and processing， as well as its nature， taste， and therapeutic effects， in order to provide a reference for the development and utilization of famous classical formulas containing Patriniae Herba. Patriniae Herba has a long history of use. It derives its name from the distinctive musty odor of its roots， which resembles spoiled soy sauce. However， due to its alias Kucai， there has been much confusion with other plants. Since the Ming dynasty， various plants have been used interchangeably as Baijiang. Herbal textual research showed that Patriniae Herba was first recorded in Shennong Bencaojing， and throughout history， Baijiang has been recognized as its standard name， though it has also been known by alternative names such as Luchang， Lujiang， and Suanyi. The main sources used throughout the ages were Patrinia scabiosaefolia or P. villosa， which is consistent with the 1977 edition of the Pharmacopoeia of the People's Republic of China. However， while the roots were traditionally used medicinally， the whole plant is now more commonly used in modern practice. In addition， the whole plants of Thlaspi arvense from the Cruciferae family and Sonchus brachyotus from the Compositae family are commonly used as regional substitutes for Baijiang. According to ancient records， Patriniae Herba was primarily found in Jiangxia（present-day eastern Hubei province） and Jiangdong（the region south of the lower reaches of the Yangtze River）， but modern literature shows that it is distributed throughout the country without a distinct geographical origin. In ancient times， the roots were harvested in August and sun-dried， today， the whole plant is typically dug up in summer or autumn and sun-dried. In recent times， the quality has been summarized as being best when the roots are long， the leaves are abundant and green， and the aroma is strong. Regarding the processing， ancient methods often involved baking（drying over fire）， while modern methods typically involve removing impurities， washing， and then cutting and drying the segments. The effects of Patriniae Herba are to clear heat and detoxify， eliminate blood stasis and drain pus. During the Han and Northern and Southern dynasties， it was used to treat skin diseases caused by heat， abscesses， postpartum diseases， and rheumatism， during the Five dynasties period， its therapeutic applications expanded to include diseases of the five senses， and by the modern era， conditions such as neurasthenia and insomnia were added. Regarding its properties and taste， it was recorded as bitter and neutral during the Han dynasty. By the Tang dynasty， it was slightly cold， with a taste of acrid and bitter. During the Yuan and Ming dynasties， it was mostly slightly cold and neutral， with a bitter and salty taste. In the Qing dynasty and modern times， it was mostly bitter and neutral， and in contemporary times， it has evolved to a taste of acrid， bitter， and cool. Based on the results of this study， it is recommended that when developing and utilizing famous classical formulas containing Patriniae Herba， one should select the entire herb of the historically mainstream sources， P. scabiosaefolia or P. villosa from the Valerianaceae family， and choose the processing method according to the prescription requirements. It is recommended to use raw products without specific requirements.

By consulting ancient and modern literature， this article systematically reviews and verifies the historical evolution of the herbal medicine known as Baijiang across various dimensions， including name， origin， scientific name verification， medicinal parts， production area， quality， harvesting and processing， as well as its nature， taste， and therapeutic effects， in order to provide a reference for the development and utilization of famous classical formulas containing Patriniae Herba. Patriniae Herba has a long history of use. It derives its name from the distinctive musty odor of its roots， which resembles spoiled soy sauce. However， due to its alias Kucai， there has been much confusion with other plants. Since the Ming dynasty， various plants have been used interchangeably as Baijiang. Herbal textual research showed that Patriniae Herba was first recorded in Shennong Bencaojing， and throughout history， Baijiang has been recognized as its standard name， though it has also been known by alternative names such as Luchang， Lujiang， and Suanyi. The main sources used throughout the ages were Patrinia scabiosaefolia or P. villosa， which is consistent with the 1977 edition of the Pharmacopoeia of the People's Republic of China. However， while the roots were traditionally used medicinally， the whole plant is now more commonly used in modern practice. In addition， the whole plants of Thlaspi arvense from the Cruciferae family and Sonchus brachyotus from the Compositae family are commonly used as regional substitutes for Baijiang. According to ancient records， Patriniae Herba was primarily found in Jiangxia（present-day eastern Hubei province） and Jiangdong（the region south of the lower reaches of the Yangtze River）， but modern literature shows that it is distributed throughout the country without a distinct geographical origin. In ancient times， the roots were harvested in August and sun-dried， today， the whole plant is typically dug up in summer or autumn and sun-dried. In recent times， the quality has been summarized as being best when the roots are long， the leaves are abundant and green， and the aroma is strong. Regarding the processing， ancient methods often involved baking（drying over fire）， while modern methods typically involve removing impurities， washing， and then cutting and drying the segments. The effects of Patriniae Herba are to clear heat and detoxify， eliminate blood stasis and drain pus. During the Han and Northern and Southern dynasties， it was used to treat skin diseases caused by heat， abscesses， postpartum diseases， and rheumatism， during the Five dynasties period， its therapeutic applications expanded to include diseases of the five senses， and by the modern era， conditions such as neurasthenia and insomnia were added. Regarding its properties and taste， it was recorded as bitter and neutral during the Han dynasty. By the Tang dynasty， it was slightly cold， with a taste of acrid and bitter. During the Yuan and Ming dynasties， it was mostly slightly cold and neutral， with a bitter and salty taste. In the Qing dynasty and modern times， it was mostly bitter and neutral， and in contemporary times， it has evolved to a taste of acrid， bitter， and cool. Based on the results of this study， it is recommended that when developing and utilizing famous classical formulas containing Patriniae Herba， one should select the entire herb of the historically mainstream sources， P. scabiosaefolia or P. villosa from the Valerianaceae family， and choose the processing method according to the prescription requirements. It is recommended to use raw products without specific requirements.

Childhood obesity has become a global public health issue. Current research indicates that early life obesogen exposure has emerged as a significant risk factor for childhood obesity. While obesogens have been confirmed to influence the development and progression of childhood obesity through mechanisms such as endocrine disruption and epigenetic programming, controversies remain regarding the establishment of causal relationships, assessment of combined exposures, and validation of transgenerational effects in humans. In recent years, novel approaches including multi-omics technologies, exposome-based analysis, and multigenerational cohort studies have integrated dynamic biomarker monitoring with analyses of social-environmental interactions, offering new perspectives and methodologies for constructing a systematic "exposure-mechanism-outcome" research framework. This article reviews literature from PubMed and Web of Science up to August 2025 on the association between early life obesogen exposure and childhood obesity, summarizing evidence on the health effects of early life obesogen exposure, major exposure pathways and internal exposure assessment, interactions and amplifying effects of social and environmental factors, as well as the biological mechanisms underlying obesogen action. It further examines current research frontiers and challenges, aiming to provide a theoretical foundation for early prevention and precision intervention of childhood obesity.

ObjectiveTo investigate the mechanisms by which Renshentang treats atherosclerosis （AS） in mice， focusing on the regulation of endothelial inflammatory responses mediated by transient receptor potential vanilloid subtype 1 （TRPV1）. MethodsAn AS model was established in apolipoprotein E knockout （ApoE^-/-） mice fed a high-fat diet. The mice were randomly divided into a simvastatin group （0.02 g·kg^-1·d^-1） and low-， medium-， and high-dose Renshentang groups （1.77， 3.54， 7.08 g·kg^-1·d^-1）， with 12 mice in each group. ApoE^-/- mice were fed a high-fat diet and treated simultaneously. C57BL/6J mice fed a normal diet served as the normal group （n=9）. After continuous administration for 12 weeks， mice were anesthetized and the aortas were collected. Oil Red O staining was used to observe lipid plaque formation in the aorta. Hematoxylin-eosin （HE） staining was performed to examine pathological changes in the aortic root. Immunohistochemistry was used to analyze the levels of pro-inflammatory factors tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）， as well as the expression of TRPV1， phosphorylated phosphoinositide 3-kinase （p-PI3K）， and phosphorylated protein kinase B （p-Akt） in the aortic root. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect endothelial nitric oxide synthase （eNOS） mRNA expression in the aorta， and Western blot was used to detect TRPV1 protein expression. ResultsCompared with the normal group， the model group showed a significant increase in aortic plaque formation （P<0.01） and significantly elevated levels of TNF-α and IL-1β in the aortic root （P<0.01）. The expression levels of TRPV1， p-PI3K， and p-Akt were decreased （P<0.05， P<0.01）， and eNOS mRNA expression was reduced （P<0.05， P<0.01）. Compared with the model group， all Renshentang groups significantly reduced aortic plaque formation （P<0.01）， significantly decreased TNF-α and IL-1β levels （P<0.01）， and markedly increased the expression levels of TRPV1， p-PI3K， p-Akt， and eNOS mRNA （P<0.05， P<0.01）. ConclusionRenshentang may inhibit endothelial inflammation and suppress the formation of AS by increasing TRPV1 protein expression and up-regulating the PI3K/Akt/eNOS signaling pathway， which may be one of the molecular mechanisms underlying its therapeutic effect against AS.

Objective: To explore whether the long-term and frequent use of citrate anticoagulants negatively affects the bone metabolism balance of female frequent plateletpheresis donors, so as to better protect their health. Methods: A total of 65 female plateletpheresis donors and 55 female whole-blood donors from Guangzhou Blood Center (May to December 2024) were enrolled as experimental and control groups respectively, stratified into age subgroups (18-39 years and 40-60 years). Serum levels of 25-hydroxyvitamin D [25(OH)D], procollagen type I N-terminal propeptide (PINP), osteocalcin (OC), and type I collagen carboxy-terminal telopeptide (CTX) were measured. Differences in bone metabolism markers between experimental and control groups across age subgroups were compared. ANOVA was used to analyze dose-response relationships between donation age, annual apheresis donation frequency, and biochemical indicators. Results: In the 40-60 age subgroup, 25(OH)D levels were significantly lower in the experimental group (P<0.05), exhibiting a linear increase with age and a linear decrease with annual donation frequency. No significant differences in CTX or PINP levels were observed between experimental and control groups in either age subgroup. Conclusion: High-frequency plateletpheresis donation does not disrupt bone metabolic balance in female donors. However, it is associated with reduced vitamin D levels in female donors aged >40 years, potentially increasing the risk of osteoporosis. Vitamin D supplementation is recommended for high-frequency female plateletpheresis donors in this age group.

ObjectiveBased on the clinical characteristics of chronic gouty arthritis （CGA） in both traditional Chinese and western medicine， this study aims to systematically evaluate the clinical concordance of existing CGA animal models， providing recommendations for establishing animal models that align with the pathological characteristics of CGA and the manifestations of traditional Chinese medicine syndromes. MethodsBy comprehensively retrieving Chinese and international databases such as China National Knowledge Infrastructure， Wanfang， VIP Chinese Science and Technology Periodical Database （VIP）， and PubMed， all relevant literature on CGA animal models was collected. Based on the guidelines， the diagnostic criteria of both traditional Chinese and western medicine were summarized and organized. The evaluation indicators for the CGA model were constructed with reference to existing evaluation modes， and the CGA animal models were analyzed to systematically evaluate the clinical concordance of existing models. ResultsThe current methods used to construct CGA animal models mainly include monosodium urate crystal induction， high-protein diet induction （poultry lack urate oxidase）， and high-fat diet combined with urate oxidase inhibitors and joint injection. Based on 11 pieces of included literature， the traditional Chinese and western medicine scoring data of each model were extracted， and the average scoring values of all models were ultimately calculated. The results show that the average clinical concordances of existing CGA animal models in both traditional Chinese and western medicine are 43.33% and 64.44%， respectively. Among them， the model with the highest clinical concordance rate is the one with a high-fat diet combined with potassium oxonate to induce hyperuricemia plus joint injection， achieving 83.33% clinical concordance in western medicine and 60% in traditional Chinese medicine. This model aligns well with the pathogenic characteristics and pathological changes of clinical CGA. ConclusionAlthough current CGA animal models can simulate some pathological characteristics of CGA， they struggle to comprehensively reflect the complex pathological processes of CGA and the characteristics of traditional Chinese medicine syndromes. Therefore， in the future， it is necessary to establish the CGA animal models that incorporate the clinical disease and syndrome characteristics of traditional Chinese and western medicine and formulate the uniform model evaluation criteria， providing more precise tools for CGA mechanism research and the development of traditional Chinese medicine.

ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.