OBJECTIVE:Postoperative recurrence is a common complication of percutaneous endoscopic lumbar discectomy for lumbar disc herniation,which can significantly increase the risk of reoperation.A well-performing risk prediction model can help identify high-risk groups early and prevent postoperative recurrence.This study systematically evaluated the risk prediction model for postoperative recurrence after percutaneous endoscopic lumbar discectomy to provide a reference for surgical decision-making.METHODS:The PubMed,Embase,Web of Science,CNKI,WanFang Data,VIP,and CBM were electronically searched to collect studies on the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy from inception to July 1,2024.Two reviewers independently screened the literature and extracted data.The models' risk of bias,applicability,and report quality were assessed using prediction model risk of bias assessment tool(PROBAST)and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis(TRIPOD)tools,respectively.Meta-analysis of postoperative recurrence rate of percutaneous endoscopic lumbar discectomy and related predictors was performed using Revman 5.4 software.RESULTS:(1)A total of 15 studies were included,all of which were retrospective studies,including 24 models for predicting the risk of recurrence after percutaneous endoscopic lumbar discectomy.(2)The PROBAST evaluation results indicated that all 15 studies exhibited a high risk of bias.Regarding applicability,two studies demonstrated a low risk,while 13 presented a high risk.(3)Regarding the TRIPOD reporting quality,the overall quality across the 15 studies was low.The primary reasons for this low compliance included the failure to report blinding,a lack of explanation for the sample size calculation method,lack of detailed description of missing data processing methods,and lack of information such as introduction to the model used.(4)Furthermore,the area under the receiver operating characteristic curve for the model ranged from 0.684 to 0.972,with the number of potential predictor variables varying from 15 to 28.(5)The results of meta-analysis showed that the postoperative recurrence rate of lumbar disc herniation patients treated with percutaneous endoscopic lumbar discectomy was 12％(95％CI=9.0％-15.0％),Modic changes(OR=6.72,95％CI=3.90-11.59),body mass index(OR=1.28,95％CI=1.10-1.49),work intensity(OR=3.22,95％CI=1.85-5.59),age(OR=2.28,95％CI=1.50-3.48),and smoking history(OR=2.65,95％CI=1.75-4.00)were independent influencing factors for postoperative recurrence of percutaneous endoscopic lumbar discectomy(all P＜0.05).CONCLUSION:The overall predictive performance of the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy is satisfactory;however,the model exhibits a high overall risk of bias and applicability,coupled with low reporting quality.Additionally,there is a lack of prospective research and external validation.Future,risk prediction models should consider factors such as Modic changes,body mass index,work intensity,age,and smoking history as potential predictors.

OBJECTIVE:Postoperative recurrence is a common complication of percutaneous endoscopic lumbar discectomy for lumbar disc herniation,which can significantly increase the risk of reoperation.A well-performing risk prediction model can help identify high-risk groups early and prevent postoperative recurrence.This study systematically evaluated the risk prediction model for postoperative recurrence after percutaneous endoscopic lumbar discectomy to provide a reference for surgical decision-making.METHODS:The PubMed,Embase,Web of Science,CNKI,WanFang Data,VIP,and CBM were electronically searched to collect studies on the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy from inception to July 1,2024.Two reviewers independently screened the literature and extracted data.The models' risk of bias,applicability,and report quality were assessed using prediction model risk of bias assessment tool(PROBAST)and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis(TRIPOD)tools,respectively.Meta-analysis of postoperative recurrence rate of percutaneous endoscopic lumbar discectomy and related predictors was performed using Revman 5.4 software.RESULTS:(1)A total of 15 studies were included,all of which were retrospective studies,including 24 models for predicting the risk of recurrence after percutaneous endoscopic lumbar discectomy.(2)The PROBAST evaluation results indicated that all 15 studies exhibited a high risk of bias.Regarding applicability,two studies demonstrated a low risk,while 13 presented a high risk.(3)Regarding the TRIPOD reporting quality,the overall quality across the 15 studies was low.The primary reasons for this low compliance included the failure to report blinding,a lack of explanation for the sample size calculation method,lack of detailed description of missing data processing methods,and lack of information such as introduction to the model used.(4)Furthermore,the area under the receiver operating characteristic curve for the model ranged from 0.684 to 0.972,with the number of potential predictor variables varying from 15 to 28.(5)The results of meta-analysis showed that the postoperative recurrence rate of lumbar disc herniation patients treated with percutaneous endoscopic lumbar discectomy was 12％(95％CI=9.0％-15.0％),Modic changes(OR=6.72,95％CI=3.90-11.59),body mass index(OR=1.28,95％CI=1.10-1.49),work intensity(OR=3.22,95％CI=1.85-5.59),age(OR=2.28,95％CI=1.50-3.48),and smoking history(OR=2.65,95％CI=1.75-4.00)were independent influencing factors for postoperative recurrence of percutaneous endoscopic lumbar discectomy(all P＜0.05).CONCLUSION:The overall predictive performance of the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy is satisfactory;however,the model exhibits a high overall risk of bias and applicability,coupled with low reporting quality.Additionally,there is a lack of prospective research and external validation.Future,risk prediction models should consider factors such as Modic changes,body mass index,work intensity,age,and smoking history as potential predictors.

Objective : To investigate the role of enhancer of zeste homolog 2(EZH2)-trimethylated lysine 27 of histone H3(H3K27me3) axis in the dedifferentiation of thyroid cancer and its clinical value as a potential target for the treatment of anaplastic thyroid cancer(ATC). Methods : Immunohistochemical SP method was used to detect the expression of EZH2, H3K27me3, paired box gene 8(PAX8), thyroglobulin(TG) and thyroid transcription factor 1(TTF1) in ATC and papillary thyroid carcinoma(PTC) and their adjacent tissues. The relationship between EZH2 and thyroid differentiation markers(PAX8, TTF1, TG) was further analyzed by gene expression omnibus(GEO) database. ATC cell lines 8305C and BHT-101 were culturedin vitro. Real-time reverse transcription PCR(RT-qPCR) was used to detect the expression of thyroid differentiation markers(TTF1, PAX8) mRNA in ATC cell lines treated with EZH2 inhibitor(GSK126), and evaluate the potential therapeutic effect of GSK126in vitro. The effects of GSK126 and BRAF inhibitor vemurafenib on the proliferation of ATC cell lines were observed by cell proliferation assay. Results : The expression of EZH2 in ATC tissues was significantly higher than that in papillary thyroid carcinoma and adjacent tissues(P<0.05). The expression of H3K37me3 in ATC tissues was significantly lower than that in PTC tissues(P<0.05). EZH2 was negatively correlated with PAX8 and TG expression levels, but not with TTF1 expression level.In vitroexperiments, GSK126 could reverse the expression of thyroid differentiation markers PAX8 and TTF1 in ATC cell lines. GSK126 combined with BRAF inhibitor vemurafenib could significantly inhibit the growth of ATC cell lines. Conclusion The EZH2-H3K27me3 axis plays an important role in regulating thyroid specific markers, and the inhibition of EZH2 by small molecular compounds is a promising target for ATC treatment in the future.

Senile osteoporosis is a growing public health challenge with significant impacts on the daily life of the elderly population as a result of its hidden nature,high prevalence,and high risk of disability.Suitable animal models that simulate senile osteoporosis are crucial for understanding its pathological mechanism and to facilitate the development of anti-osteoporosis drugs and identify new therapeutic targets.This review considers the most commonly used method for creating animal models of senile osteoporosis,analyzes their advantages and limitations,and discusses research progress in animal models in terms of evaluation indicators,to provide references for research using animal models of senile osteoporosis.

Objective To analyze and compare the diagnostic criteria of dampness syndrome in clinical studies and construct a dampness syndrome entry pool,so as to provide theoretical support for the development of dampness-related scales,and then provide reference for clinical research.Methods By searching the literature collected by CNKI,VIP and Wanfang data database from 1960 to 2023,the clinical research literature of dampness syndrome was searched and screened,and the diagnostic criteria of dampness syndrome were summarized and analyzed.Results A total of 7651 articles were retrieved from the above database,and 52 articles were finally included.9 diagnostic criteria were obtained by combining the screening of teaching materials.They are The National Standard of Chinese Medicine Clinical Diagnosis and Treatment of Chinese Medicine(GB/T16751.2-1997),Syndrome element differentiation,Traditional Chinese Medicine Syndrome Standards,Traditional Chinese Medicine Syndrome Differentiation and Diagnosis,Diagnostics of Traditional Chinese Medicine(Fifth edition,sixth edition,second edition of the new century,People's Health Commission edition,and tenth edition of the National Higher Traditional Chinese Medicine College Planning Textbook).The diagnostic methods can be divided into three kinds:narrative method,primary and secondary disease(or see disease)classification diagnosis method,and assigning method.There are a total of 36 symptom components,which can be divided into four groups:the accumulation of muscle and striae group,the obstruction of meridians and joints group,the internal accumulation of organs group,and the dampness and obstruction of orifices group.The National Standard of Chinese Medicine Clinical Diagnosis and Treatment of Chinese Medicine(GB/T16751.2-1997)has been cited for a maximum of 22 times,covering 16 diseases.Conclusion The National Standard of Chinese Medicine Clinical Diagnosis and Treatment of Chinese Medicine is the most widely used in clinical research,and the new version of national standard in 2021 is more suitable for the current diagnosis and clinical research of dampness syndrome.

Senile osteoporosis is a growing public health challenge with significant impacts on the daily life of the elderly population as a result of its hidden nature,high prevalence,and high risk of disability.Suitable animal models that simulate senile osteoporosis are crucial for understanding its pathological mechanism and to facilitate the development of anti-osteoporosis drugs and identify new therapeutic targets.This review considers the most commonly used method for creating animal models of senile osteoporosis,analyzes their advantages and limitations,and discusses research progress in animal models in terms of evaluation indicators,to provide references for research using animal models of senile osteoporosis.

Objective To analyze and compare the diagnostic criteria of dampness syndrome in clinical studies and construct a dampness syndrome entry pool,so as to provide theoretical support for the development of dampness-related scales,and then provide reference for clinical research.Methods By searching the literature collected by CNKI,VIP and Wanfang data database from 1960 to 2023,the clinical research literature of dampness syndrome was searched and screened,and the diagnostic criteria of dampness syndrome were summarized and analyzed.Results A total of 7651 articles were retrieved from the above database,and 52 articles were finally included.9 diagnostic criteria were obtained by combining the screening of teaching materials.They are The National Standard of Chinese Medicine Clinical Diagnosis and Treatment of Chinese Medicine(GB/T16751.2-1997),Syndrome element differentiation,Traditional Chinese Medicine Syndrome Standards,Traditional Chinese Medicine Syndrome Differentiation and Diagnosis,Diagnostics of Traditional Chinese Medicine(Fifth edition,sixth edition,second edition of the new century,People's Health Commission edition,and tenth edition of the National Higher Traditional Chinese Medicine College Planning Textbook).The diagnostic methods can be divided into three kinds:narrative method,primary and secondary disease(or see disease)classification diagnosis method,and assigning method.There are a total of 36 symptom components,which can be divided into four groups:the accumulation of muscle and striae group,the obstruction of meridians and joints group,the internal accumulation of organs group,and the dampness and obstruction of orifices group.The National Standard of Chinese Medicine Clinical Diagnosis and Treatment of Chinese Medicine(GB/T16751.2-1997)has been cited for a maximum of 22 times,covering 16 diseases.Conclusion The National Standard of Chinese Medicine Clinical Diagnosis and Treatment of Chinese Medicine is the most widely used in clinical research,and the new version of national standard in 2021 is more suitable for the current diagnosis and clinical research of dampness syndrome.

OBJECTIVE To conduct a reevaluation of the systematic review(SR)/meta-analysis on the use of Xianling gubao capsules(XLGBC)for knee osteoarthritis(KOA),and provide evidence-based support for the clinical use of the drugs.METHODS Computerized searches including CNKI,Wanfang Data,VIP,China Biomedical Literature Database,the Cochrane Library,PubMed,Embase and Web of Science were conducted to collect systematic reviews(SR)or meta-analyses of XLGBC for the treatment of KOA from the inception to May 31st,2024.The report quality,methodological quality,risk of bias and evidence quality were assessed using the PRISMA 2020 statement,AMSTAR 2 scale,ROBIS tool and GRADE tool,respectively.A comprehensive quality analysis of the quantitative results from the SR/meta-analysis was also performed.RESULTS A total of five SR/meta-analyses were included.The evaluation results based on the PRISMA 2020 statement showed that one study report was relatively complete(21 points),while four studies had deficiencies(18-20 points).The assessment using the AMSTAR 2 scale indicated that the methodological quality of all five studies was rated as very low.According to the ROBIS tool evaluation,the risk of comprehensive bias in all five studies was classified as high.GRADE tool evaluation revealed that among 49 outcome indicators,5(10.2％)were rated as moderate-quality evidence(10.2％),12 as low-quality evidence(24.5％),and 32 as very low-qualitv evidence(65.3％).The results of comprehensive quality analysis showed that the clinical efficacy,visual analogue scale score,pain relief time,comprehensive indexes of knee joint function,the levels of inflammatory factors and the incidence of adverse events in patients with XLGBC combined with conventional treatment were significantly better than conventional treatment alone(P＜0.05).CONCLUSIONS Compared with conventional treatment,XLGBC in combination with conventional treatment for KOA may have some efficacy and safety advantages.However,due to the low quality of evidence for the outcome indicators included in the studies,the conclusions should be interpreted with caution.

Knee osteoarthritis （KOA） is a common degenerative joint disease characterized primarily by the degeneration and damage of knee joint cartilage， accompanied by osteophyte formation and inflammation. In recent years， the prevalence of KOA has been increasing globally， significantly impacting the quality of life patients. However， the pathogenesis of KOA remains not fully understood， and current treatment methods are limited. Therefore， finding new therapeutic strategies is a research hotspot. Previous studies have found that the onset of KOA is related to abnormal mitochondrial regulation. Mitochondria， functioning as secondary messengers， play crucial roles in cellular respiration， reactive oxygen species （ROS） generation， and adenosine triphosphate （ATP） production through oxidative phosphorylation. Mitochondrial quality control is a pivotal mechanism for maintaining the morphology， quantity， and quality of mitochondria. The connection between mitochondrial quality control and the pathogenesis of KOA involves several factors， such as mitochondrial oxidative stress， mitophagy， imbalances in mitochondrial biogenesis， abnormal mitochondrial dynamics （fission and fusion）， and dysregulation of calcium ions. Metabolic abnormalities in the body lead to mitochondrial structural damage， which in turn contributes to the onset and progression of KOA. Traditional Chinese medicine （TCM） has made some progress in intervening in mitochondrial quality control， employing multi-faceted， multi-pathway， and multi-target strategies to treat KOA. Several studies have shown that mitochondrial quality control may be one of the therapeutic targets of TCM in treating KOA. However， there is currently a lack of comprehensive reviews summarizing the TCM interventions in mitochondrial quality control for treating KOA. This paper systematically reviewed the research progress in TCM treatment of KOA based on five aspects of mitochondrial quality control， aiming to provide a theoretical basis for the clinical prevention and treatment of KOA.

BACKGROUND:Based on different algorithms of machine learning,how to carry out clinical research on lumbar disc herniation with the help of various algorithmic models has become a trend and hot spot in the development of intelligent medicine at present. OBJECTIVE:To review the characteristics of different algorithmic models of machine learning in the diagnosis and treatment of lumbar disc herniation,and summarize the respective advantages and application strategies of algorithmic models for the same purpose. METHODS:The computer searched PubMed,Web of Science,EMBASE,CNKI,WanFang,VIP and China Biomedical(CBM)databases to extract the relevant articles on machine learning in the diagnosis and treatment of lumbar disc herniation.Finally,96 articles were included for analysis. RESULTS AND CONCLUSION:(1)Different algorithm models of machine learning provide intelligent and accurate application strategies for clinical diagnosis and treatment of lumbar disc herniation.(2)Traditional statistical methods and decision trees in supervised learning are simple and efficient in exploring risk factors and establishing diagnostic and prognostic models.Support vector machine is suitable for small data sets with high-dimensional features.As a nonlinear classifier,it can be applied to the recognition,segmentation and classification of normal or degenerative intervertebral discs,and to establish diagnostic and prognostic models.Ensemble learning can make up for the shortcomings of a single model.It has the ability to deal with high-dimensional data and improve the precision and accuracy of clinical prediction models.Artificial neural network improves the learning ability of the model,and can be applied to intervertebral disc recognition,classification and making clinical prediction models.On the basis of the above uses,deep learning can also optimize images and assist surgical operations.It is the most widely used model with the best performance in the diagnosis and treatment of lumbar disc herniation.The clustering algorithm in unsupervised learning is mainly used for disc segmentation and classification of different herniated segments.However,the clinical application of semi-supervised learning is relatively less.(3)At present,machine learning has certain clinical advantages in the identification and segmentation of lumbar intervertebral discs,classification and grading of the degenerative intervertebral discs,automatic clinical diagnosis and classification,construction of the clinical predictive model and auxiliary operation.(4)In recent years,the research strategy of machine learning has changed to the neural network and deep learning,and the deep learning algorithm with stronger learning ability will be the key to realizing intelligent medical treatment in the future.