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.

ObjectiveTo investigate the value of predictive models established based on machine learning methods in predicting the risk of metabolic associated fatty liver disease （MAFLD）， and to analyze its key risk factors. MethodsA retrospective analysis was performed for the 50 variables of 2 168 healthy individuals who underwent physical examination in Department of Health Assessment， Xiyuan Hospital， China Academy of Chinese Medical Sciences， from January 2021 to December 2024， including body composition， past history， and laboratory tests， and according to whether they were diagnosed with MAFLD or not， they were divided into MAFLD group with 265 individuals and non-MAFLD group with 1 903 individuals. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Randomly split the research data into a training set and a validation set in a 70% to 30% ratio. Predictive factors were screened from the training set data using univariate analysis， LASSO regression， and multivariate Logistic regression analysis. Predictive models were then constructed using seven machine learning methods： Logistic regression， decision tree， random forest （RF）， eXtreme gradient boosting， light gradient boosting machine， support vector machine， and artificial neural network. Model performance was evaluated by plotting receiver operating characteristic curve for the validation set and calculating the area under the curve （AUC）， sensitivity， specificity， and Youden index for each model. Furthermore， the SHapley Additive exPlanation （SHAP） method was used to analyze the contribution of variables in the optimal model. ResultsThe prevalence rate of MAFLD among the 2 168 subjects was 12.22% （265/2 168）. Smoking， diastolic blood pressure， phase angle， visceral fat area， muscle fat ratio， waist-to-hip ratio， aspartate aminotransferase， non-HDL-C/HDL-C ratio， triglyceride-glucose index， and gallstones were independent risk factors for MAFLD （all P<0.05）. The seven predictive models of support vector machine， eXtreme gradient boosting， decision tree， light gradient boosting machine， artificial neural network， RF， and Logistic regression had an AUC of 0.738， 0.754， 0.757， 0.786， 0.795， 0.796， and 0.815， respectively， in the validation set， among which the RF model had the best discriminatory ability （AUC=0.796， 95% confidence interval： 0.754 — 0.839）， with a sensitivity of 81.01%， a specificity of 63.16%， and a Youden index of 44.17%. The SHAP analysis showed that visceral fat area， waist-to-hip ratio， and diastolic blood pressure were the top three predictive factors in terms of importance. ConclusionThe RF model， constructed based on body composition and clinical indicators， has a good performance in predicting the risk of MAFLD， and its interpretability can help to identify high-risk individuals in the early stage in clinical practice.

Background Amid global climate change, extreme environmental events are occurring more frequently, and it is imperative to investigate the impacts of combined exposure to fine particluate matter (PM_2.5) and cold spells (CS) on population mortality. Objective To analyze the association between sequential extreme PM_2.5-cold spell (EP-CS) events and non-accidental mortality among residents in Zigong City from 2016 to 2021. Methods Using time-series study design, meteorological data in Zigong were collected from the Zigong Meteorological Bureau for the period from January 1, 2016 to December 31, 2021, while daily non-accidental mortality data were obtained from the mortality surveillance system of the Zigong Center for Disease Control and Prevention. We adopted the percentile method to define extreme PM_2.5 events and cold spells. We analyzed the risk effect of EP-CS events on non-accidental mortality among residents in this city and explored the potential amplification of damage resulting from different patterns of consecutive extreme events by using distributed lag nonlinear model (DLNM). We also conducted stratified analyses based on age, gender, education level, and marital status. Results The EP-CS events demonstrated a significant impact on non-accidental mortality among the local residents, exhibiting a certain lagged effect. The effects on the overall residents lasted from lag0 (RR=1.030, 95%CI: 1.013, 1.048) to lag14 (RR=1.035, 95%CI: 1.019, 1.052). Notably, the effects were more pronounced among females, individuals aged 65 years and above, and those who were never married, divorced, or widowed. Different patterns of EP-CS events all associated with adverse effects, the health impact of EP-CS events was significantly greater than that of individual PM_2.5 pollution or CS events. The analysis of lag effects across different event patterns revealed that the overall effect of EP-CS events with shorter intervals (0–7 d) had a stronger effect compared to EP-CS with longer intervals (8–14 d), and the RR values of lag14 were 1.034 (95%CI: 1.015, 1.054) and 1.017 (95%CI: 1.007, 1.027), suggesting that the damaging effect of compound events occurring in the short term was more significant. Conclusion All sequential extreme EP-CS events have an impact on non-accidental mortality among residents in this city, with compound events demonstrating a stronger effect. Females, individuals aged ≥65 years, and those who were never married, divorced, or widowed are more sensitive to EP-CS events.

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.

LIU Wansu， as the foremost of the four great masters of the Jin-Yuan period， established the "theory of fire-heat'' and extended the fire-heat pathogenesis framework to the field of stroke， thereby forming the theory of ''fire-heat inducing stroke''. This achieved a paradigmatic shift in stroke etiology from ''exogenous wind inducing stroke'' to ''fire-heat inducing stroke''. This paper systematically reviews the developmental trajectory of LIU Wansu's ''fire-heat inducing stroke'' theory and explores the social background， academic origins， and core connotations of its theoretical construction. The study found that， based on the ''Nineteen Pathomechanisms'' in the Huangdi's Internal Classic （Huang Di Nei Jing） and combined with clinical practice， LIU Wansu proposed that fire-heat is the fundamental cause of stroke， and that the Six Climatic Factors and the Five Zhi-Emotions can all transform into fire. He further constructed a stratified syndrome differentiation and therapeutic system centered on clearing heat and purging fire， emphasizing differentiated treatment of exterior and interior syndromes， Six Meridians syndrome differentiation， and seasonally adjusted medication. This theory not only resolved the diagnostic and therapeutic dilemmas of febrile epidemic diseases during the Jin-Yuan period， but also exerted a profound influence on later physicians such as ZHANG Zihe and ZHU Danxi， thereby promoting the pluralistic development of stroke theory in traditional Chinese medicine （TCM）. Modern pharmacological research provides solid scientific evidence， confirming that the ''fire-heat'' pathological state is highly associated with key mechanisms such as excessive inflammatory responses， oxidative stress， and excitatory amino acid toxicity following cerebral ischemia. Heat-clearing and fire-purging prescriptions and agents， such as Huanglian Jiedu Tang and baicalin， can exert multi-target neuroprotective effects by regulating inflammatory signaling， enhancing antioxidant enzyme activity， and balancing neurotransmitters. This not only verifies the scientific basis of the ''fire-heat inducing stroke'' theory from a modern biological perspective but also provides conclusive evidence for the clinical application of heat-clearing and fire-purging therapy. LIU Wansu's ''fire-heat inducing stroke'' theory represents a major milestone in the historical understanding of stroke pathogenesis， and its academically transitional insights continue to hold core guiding value for the pattern identification and treatment of ischemic stroke today.

This article systematically analyzes the historical evolution of the name， origin， medicinal parts， producing area， harvesting and processing， nature， flavor and efficacy of Piperis Longi Fructus by referring to the materia medica， medical books， and prescription books of past dynasties， combined with the relevant modern literature， in order to provide a basis for the development and utilization of famous classical formulas containing this herb. According to the herbal textual research， the name of Piper longum first appeared in Nanfang Caomuzhuang， and it also has other aliases such as Biboli， Halou， and Hujiaohua. Historically， the origin of Piperis Longi Fructus has been P. longum of the Piperaceae family. In ancient times， both the fruit and root were used as medicine， and since the Republic of China， the fruit has been mainly used as medicine. The medicinal part is the dried， nearly ripe or ripe fruit spikes. Piperis Longi Fructus is native to India and has been introduced into China since the Tang dynasty. In the Ming dynasty， Bencao Pinhui Jingyao clearly stated that the genuine producing area was "Duanzhou"， present-day Zhaoqing in Guangdong province. Nowadays， it is planted in Guangdong， Guangxi， Hainan， Yunnan and other regions. Historically and currently， harvesting occurs in autumn. The ancient processing method uniformly involved removing the stems， soaking in the sourest vinegar overnight， baking， and scraping off the peels and grains with a knife until clean. In modern times， impurities are removed， and it is dried in the sun and crushed when used. The properties， functions and applications of P. longum are basically the same in ancient and modern times. It tastes pungent， is warm in nature， and non-toxic. It has the effects of warming the middle-jiao to dispel cold， lowering Qi and relieving pain， and is used for cold pain in the epigastrium and abdomen， vomiting， diarrhea， chest pain， headache， and toothache. Based on the research results， it is recommended that when developing famous classical formulas containing Piperis Longi Fructus， the dried nearly ripe or ripe fruit spikes of P. longum should be used. If there are no clear processing requirements， it is recommended to use the raw products for medicinal use， and the specific processing methods can refer to the relevant requirements under Piperis Longi Fructus in the 2025 edition of the Pharmacopoeia of the People's Republic of China. If processing requirements such as soaking in vinegar and peeling are clearly specified， it is recommended to follow the ancient methods.

LIU Wansu， as the foremost of the four great masters of the Jin-Yuan period， established the "theory of fire-heat'' and extended the fire-heat pathogenesis framework to the field of stroke， thereby forming the theory of ''fire-heat inducing stroke''. This achieved a paradigmatic shift in stroke etiology from ''exogenous wind inducing stroke'' to ''fire-heat inducing stroke''. This paper systematically reviews the developmental trajectory of LIU Wansu's ''fire-heat inducing stroke'' theory and explores the social background， academic origins， and core connotations of its theoretical construction. The study found that， based on the ''Nineteen Pathomechanisms'' in the Huangdi's Internal Classic （Huang Di Nei Jing） and combined with clinical practice， LIU Wansu proposed that fire-heat is the fundamental cause of stroke， and that the Six Climatic Factors and the Five Zhi-Emotions can all transform into fire. He further constructed a stratified syndrome differentiation and therapeutic system centered on clearing heat and purging fire， emphasizing differentiated treatment of exterior and interior syndromes， Six Meridians syndrome differentiation， and seasonally adjusted medication. This theory not only resolved the diagnostic and therapeutic dilemmas of febrile epidemic diseases during the Jin-Yuan period， but also exerted a profound influence on later physicians such as ZHANG Zihe and ZHU Danxi， thereby promoting the pluralistic development of stroke theory in traditional Chinese medicine （TCM）. Modern pharmacological research provides solid scientific evidence， confirming that the ''fire-heat'' pathological state is highly associated with key mechanisms such as excessive inflammatory responses， oxidative stress， and excitatory amino acid toxicity following cerebral ischemia. Heat-clearing and fire-purging prescriptions and agents， such as Huanglian Jiedu Tang and baicalin， can exert multi-target neuroprotective effects by regulating inflammatory signaling， enhancing antioxidant enzyme activity， and balancing neurotransmitters. This not only verifies the scientific basis of the ''fire-heat inducing stroke'' theory from a modern biological perspective but also provides conclusive evidence for the clinical application of heat-clearing and fire-purging therapy. LIU Wansu's ''fire-heat inducing stroke'' theory represents a major milestone in the historical understanding of stroke pathogenesis， and its academically transitional insights continue to hold core guiding value for the pattern identification and treatment of ischemic stroke today.

This article systematically analyzes the historical evolution of the name， origin， medicinal parts， producing area， harvesting and processing， nature， flavor and efficacy of Piperis Longi Fructus by referring to the materia medica， medical books， and prescription books of past dynasties， combined with the relevant modern literature， in order to provide a basis for the development and utilization of famous classical formulas containing this herb. According to the herbal textual research， the name of Piper longum first appeared in Nanfang Caomuzhuang， and it also has other aliases such as Biboli， Halou， and Hujiaohua. Historically， the origin of Piperis Longi Fructus has been P. longum of the Piperaceae family. In ancient times， both the fruit and root were used as medicine， and since the Republic of China， the fruit has been mainly used as medicine. The medicinal part is the dried， nearly ripe or ripe fruit spikes. Piperis Longi Fructus is native to India and has been introduced into China since the Tang dynasty. In the Ming dynasty， Bencao Pinhui Jingyao clearly stated that the genuine producing area was "Duanzhou"， present-day Zhaoqing in Guangdong province. Nowadays， it is planted in Guangdong， Guangxi， Hainan， Yunnan and other regions. Historically and currently， harvesting occurs in autumn. The ancient processing method uniformly involved removing the stems， soaking in the sourest vinegar overnight， baking， and scraping off the peels and grains with a knife until clean. In modern times， impurities are removed， and it is dried in the sun and crushed when used. The properties， functions and applications of P. longum are basically the same in ancient and modern times. It tastes pungent， is warm in nature， and non-toxic. It has the effects of warming the middle-jiao to dispel cold， lowering Qi and relieving pain， and is used for cold pain in the epigastrium and abdomen， vomiting， diarrhea， chest pain， headache， and toothache. Based on the research results， it is recommended that when developing famous classical formulas containing Piperis Longi Fructus， the dried nearly ripe or ripe fruit spikes of P. longum should be used. If there are no clear processing requirements， it is recommended to use the raw products for medicinal use， and the specific processing methods can refer to the relevant requirements under Piperis Longi Fructus in the 2025 edition of the Pharmacopoeia of the People's Republic of China. If processing requirements such as soaking in vinegar and peeling are clearly specified， it is recommended to follow the ancient methods.

Objective To systematically analyze the characteristics of the disease burden of hypertensive heart disease (HHD) in the elderly (≥60 years) globally and in China from 1990 to 2021, and to predict its future trends from 2022 to 2040, with the aim of providing data support for optimizing comprehensive prevention and control strategies for HHD. Methods Based on the Global Burden of Disease (GBD) 2021 database, the number of prevalent cases and disability-adjusted life years (DALYs) of HHD in the elderly were extracted for the world, China, and five regions categorized by sociodemographic index (SDI). Joinpoint regression was used to analyze the temporal trends of age-standardized prevalence rate and age-standardized DALYs rate of HHD in the elderly. A three-factor decomposition method was applied to evaluate the relative contributions of aging, population growth, and epidemiological changes to the variations in the elderly HHD burden. Additionally, a Bayesian age-period-cohort model was used to predict the elderly HHD burden from 2022 to 2040. Results In 2021, the number of prevalent elderly HHD cases reached 10 283 000 globally and 3 412 400 in China, representing increases of 179.20% and 159.20% respectively, compared with 1990. The DALYs of elderly HHD were 18 812 700 person-years globally and 4 731 400 person-years in China, rising by 76.08% and 29.45% respectively from 1990. Meanwhile, the growth rates of the number of prevalent cases and DALYs of elderly HHD varied across different SDI regions. From 1990 to 2021, the age-standardized prevalence rate of elderly HHD in China, as well as the age-standardized DALYs rate of elderly HHD both globally and in China, showed significant downward trends (all average annual percentage changes<0, all P<0.001). In 2021, the 70-74 years age group accounted for the highest proportion of prevalent cases and DALYs of elderly HHD, both globally and in China. Decomposition analysis revealed that population growth was the dominant factor driving the increase in the elderly HHD burden across all regions. The prediction model results indicated that the number of prevalent cases and DALYs of elderly HHD would continue to rise globally and in China from 2022 to 2040, with the growth rate of the elderly HHD burden in China between 2021 and 2040 expected to exceed the global average. Conclusion Over the past 32 years, although the age-standardized disease rates of elderly HHD have mainly shown a downward trend globally and in China, the absolute number of the disease burden has increased substantially. The projection model indicates a continued upward trajectory, with the growth rate in China higher than the global average. Therefore, there is an urgent need to implement precise prevention and control strategies to effectively mitigate the disease burden of elderly HHD.

Objective: To characterize longitudinal trajectories of testicular development in boys and breast development in girls, so as to provide reference data for understanding patterns of pubertal sexual maturation. Methods: Based on the Shanghai Pudong New Area Cohort Study on Growth, Development and Health in Children and Adolescents, a baseline survey was conducted in 2020 using a mult stage cluster random sampling method. A total of 2 184 children who completed all follow ups during the primary school period from 13 elementary schools in Pudong New Area,Shanghai,with annual follow ups during 2021-2025. Testicular volume and Tanner stage of breast development were assessed by professional physicians using standardized visual inspection and palpation. The age distribution of testicular volume and breast development was fitted by using cumulative link mixed models and Turnbull s nonparametric maximum likelihood estimation method. Results: Median ages for testicular volumes of 2, 3, 4 and 5 mL in boys were 7.07, 9.24, 10.29, and 11.57 years old, respectively. Median ages for Tanner breast stages Ⅱ, Ⅲ, Ⅳ, and Ⅴ in girls were 8.55 , 10.17, 11.18, and 13.78 years old, respectively. Based on overweight and obesity, stratified analysis showed that earlier pubertal onset among overweight/obesity children, and the key milestones for pubertal initiation were testicular volume reaching 4 mL in boys and breast Tanner II in girls for 10.29, 10.83; 8.18, 9.00 years. Conclusion Overweight and obesity are associated with earlier pubertal initiation,but there are certain gender and developmental stage specific patterns.