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:H-type vessels(CD31hiEmcnhi)possess unique functionalities that offer new perspectives and entry points for comprehensively understanding the mechanisms of vascular-mediated bone metabolism regulation.This has triggered a significant paradigm shift in bone science research from a"bone-centric"approach to an"integrative bone-vascular"framework.OBJECTIVE:To perform a visual analysis based on literature data in the field of H-type vessels,aiming to identify research hotspots and emerging trends.METHODS:A systematic search was conducted in the CNKI and Web of Science databases for English and Chinese publications related to H-type vessels,covering the period from 2014 to 2024.The CiteSpace 6.2.R4 information visualization software was utilized to analyze and visualize data on publication countries,authors,institutions,keywords,and timeline views.RESULTS AND CONCLUSION:(1)A total of 59 Chinese and 185 English publications were included in the analysis.Since the introduction of the H-type vessel concept in 2014,the number of related research publications has been steadily increasing,accompanied by a significant growth in academic output.China leads the research in this domain,with major institutions including Southern Medical University,Sichuan University,and Shanghai Jiao Tong University.Keyword analysis indicated that current research hotspots primarily focus on the generation and regulatory mechanisms of H-type vessels,their roles in bone formation and remodeling,and their associations with bone metabolic diseases.Additionally,emerging keywords such as"induced membrane,""cartilage repair,"and"traditional Chinese medicine"suggest that research is progressively moving towards diversification and multidisciplinary integration.(2)H-type vessels play a crucial role in the development and progression of bone diseases,and relevant studies are pivotal for a deeper understanding of the physiological and pathological processes of bone tissue.Future research should further elucidate the specific mechanisms of H-type vessels in various disease contexts and promote the clinical translation of basic research findings.These efforts will provide innovative ideas and strategies for the prevention and treatment of bone-related diseases.

BACKGROUND:During bone remodeling,bone formation and bone resorption are spatially and temporally coordinated,involving intricate interactions between osteoclasts and osteoblasts.Isoginkgetin,a flavonoid found in Ginkgo biloba,has a wide range of anticancer activity and anti-reactive oxygen species activity;however,the effect of isoginkgetin on osteoclast differentiation is unknown.OBJECTIVE:To study the effect and mechanism of action of isoginkgetin on osteoclastogenesis.METHODS:In vitro studies were performed on mouse bone marrow-derived macrophages,and cell counting kit-8 cytotoxicity assay was used to detect the effect of isoginkgetin on cell viability of bone marrow-derived macrophages.Macrophage colony-stimulating factor and receptor activator of nuclear factor kappa-B ligand were used to induce the differentiation of bone marrow-derived macrophages to osteoclasts.Network pharmacology and molecular docking and molecular dynamics simulations were used to predict the processes and targets of the effects of isoginkgetin on the differentiation of osteoclasts.Tartrate-resistant acid phosphatase staining and F-actin staining were used to detect the effects of isoginkgetin on the differentiation and function of osteoclasts.Western blot and RT-PCR were used to detect the effects of isoginkgetin on the expression of genes and proteins related to osteoclast differentiation,reactive oxygen species,and PI3K/AKT pathways.Fluorescent probes were used to detect cellular and mitochondrial reactive oxygen species levels.Flow cytometry technology was used to detect reactive oxygen species levels in cells.RESULTS AND CONCLUSION:(1)Network pharmacology results showed that isoginkgetin affected osteoporosis mainly through the PI3K-AKT pathway and cellular response to drugs and hypoxia,and GSK3β,ESR1,MCL1 and CCNA2 were the key targets.(2)Cell counting kit-8 and tartrate-resistant acid phosphatase staining results showed that isoginkgetin at 8 μmol/L had the most significant inhibitory effect on osteoclastogenesis in vitro,and F-actin results showed that isoginkgetin inhibited osteoclast cytoskeletal actin ring formation in a concentration-dependent manner.(3)Molecular dynamics simulations showed that isoginkgetin bound well to osteoclastogenesis marker proteins(NFATc1,c-Fos,CTSK,and MMP9).Western blot and RT-PCR results indicated that isoginkgetin inhibited the expression of osteoclastogenesis marker proteins and genes(NFATc1,c-Fos,CTSK,and MMP9).(4)Western blot results showed that isoginkgetin inhibited the phosphorylation level of PI3K/AKT/GSK3β and suppressed osteoclastogenesis by activating the PI3K-AKT-GSK3β pathway.(5)The results of reactive oxygen species assay showed that isoginkgetin significantly reduced receptor activator of nuclear factor kappa-B ligand-induced cellular and mitochondrial reactive oxygen species production,and inhibited the differentiation of bone marrow-derived macrophages to osteoclasts.

BACKGROUND:β-Caryophyllene has a variety of pharmacological activities such as antioxidant,anti-inflammatory and anti-apoptotic,which may have a better therapeutic effect on osteoarthritis.OBJECTIVE:To investigate the effect and mechanism of β-caryophyllene on mouse osteoarthritis.METHODS:Forty C57BL/6J mice were randomly divided into sham group,model group,low-dose β-caryophyllene group and high-dose β-caryophyllene group,with 10 mice in each group.Hulth method was used to construct an osteoarthritis model in the latter three groups.Four weeks after modeling,70 and 140 mg/kg/d β-caryophyllene was intragastrically given in the low-and high-dose β-caryophyllene groups,respectively,and normal saline was given by gavage in the sham group and the model group,once a day,for 4 weeks.After administration,knee joint morphological changes were observed by hematoxylin-eosin staining,serum levels of inflammatory factors(tumor necrosis factor-α,interleukin-1β,interleukin-6,and interleukin-10)were detected by ELISA,and oxidative stress indexes(glutathione peroxidase,superoxide dismutase,and malondialdehyde)were detected by chemiluminescence.The expression levels of key proteins in the Sonic hedgehog(Shh)/glioma associated oncogene homolog 1(Gli1)signaling pathway were detected by immunohistochemistry and western blot.RESULTS AND CONCLUSION:(1)Compared with the sham group,a large number of inflammatory cells infiltrated in the knee joint of mice in the model group,cartilage tissue was seriously damaged,serum levels of tumor necrosis factor-α,interleukin-1β,interleukin-6,interleukin-10 and malondialdehyde were significantly increased(P＜0.01),the activities of glutathione peroxidase and superoxide dismutase were significantly decreased(P＜0.01),and the relative expression levels of Shh and Gli1 in the knee joint were significantly increased(P＜0.01).(2)Compared with the model group,in the low-and high-dose β-caryophyllene groups,inflammatory cell infiltration in the mouse knee joint was decreased,cartilage tissue injury was alleviated,serum levels of tumor necrosis factor-α,interleukin-1 β,interleukin-6 and malondialdehyde were significantly decreased(P＜0.05),the activities of glutathione peroxidase and superoxide dismutase were significantly increased(P＜0.01),and the expression levels of Shh and Gli1 in the knee joint were significantly decreased(P＜0.01).The above-mentioned improvements were more significant in the high-dose β-caryophyllene group than the low-dose β-caryophyllene group.To conclude,β-caryophyllene can improve osteoarthritis,and its mechanism may be related to reducing inflammation and oxidative stress damage by regulating the Shh/Gli1 signaling pathway.

BACKGROUND:Oral submucous fibrosis is a chronic progressive disease that is prone to malignant transformation.Traditional treatment methods are not ideal and have limitations.As an emerging discipline,tissue engineering has opened up a new path for the treatment of oral submucous fibrosis.OBJECTIVE:To review the latest progress in the pathogenesis and treatment of oral submucous fibrosis,and to summarize and analyze the role and research progress of mesenchymal stem cells,bioscaffold materials,and tissue-engineered oral mucosa in oral submucous fibrosis,thereby providing ideas for the research and clinical application of tissue engineering in the treatment of oral submucous fibrosis.METHODS:In October 2024,the first author used computers to search for relevant literature from January 1970 to October 2024 in PubMed and CNKI databases.The search terms were"oral submucous fibrosis,tissue engineering,mesenchymal stem cells,bioscaffold materials"in English and Chinese,respectively.A total of 166 articles were finally included for analysis.RESULTS AND CONCLUSION:(1)The pathogenesis of oral submucous fibrosis is complex,and many factors are closely related to oral submucous fibrosis,but ultimately they promote the development of oral submucous fibrosis by promoting collagen deposition and accelerating fibroblast proliferation.(2)Traditional treatment methods for oral submucous fibrosis have problems such as low patient compliance and unsatisfactory results,and new treatment strategies are urgently needed.(3)Mesenchymal stem cells regulate the pathological microenvironment,reduce inflammation and inhibit the process of fibrosis due to their immunomodulatory and antioxidant properties,providing a new idea for the treatment of oral submucous fibrosis.(4)Biomass materials,as drug and cell delivery carriers,regulate the pathological microenvironment and are used in various fibrotic diseases,providing a new solution for the treatment of oral submucous fibrosis.(5)Tissue-engineered oral mucosa can be used as an autologous mucosa substitute to promote tissue repair,and also provides a basis for the establishment of disease models.(6)Tissue engineering treatment strategy has great potential for achieving comprehensive treatment of oral submucous fibrosis,but its role in the treatment of oral submucous fibrosis has not yet been verified.It is of great significance to explore tissue engineering-based treatment strategies for oral submucous fibrosis in the future.

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:H-type vessels(CD31hiEmcnhi)possess unique functionalities that offer new perspectives and entry points for comprehensively understanding the mechanisms of vascular-mediated bone metabolism regulation.This has triggered a significant paradigm shift in bone science research from a"bone-centric"approach to an"integrative bone-vascular"framework.OBJECTIVE:To perform a visual analysis based on literature data in the field of H-type vessels,aiming to identify research hotspots and emerging trends.METHODS:A systematic search was conducted in the CNKI and Web of Science databases for English and Chinese publications related to H-type vessels,covering the period from 2014 to 2024.The CiteSpace 6.2.R4 information visualization software was utilized to analyze and visualize data on publication countries,authors,institutions,keywords,and timeline views.RESULTS AND CONCLUSION:(1)A total of 59 Chinese and 185 English publications were included in the analysis.Since the introduction of the H-type vessel concept in 2014,the number of related research publications has been steadily increasing,accompanied by a significant growth in academic output.China leads the research in this domain,with major institutions including Southern Medical University,Sichuan University,and Shanghai Jiao Tong University.Keyword analysis indicated that current research hotspots primarily focus on the generation and regulatory mechanisms of H-type vessels,their roles in bone formation and remodeling,and their associations with bone metabolic diseases.Additionally,emerging keywords such as"induced membrane,""cartilage repair,"and"traditional Chinese medicine"suggest that research is progressively moving towards diversification and multidisciplinary integration.(2)H-type vessels play a crucial role in the development and progression of bone diseases,and relevant studies are pivotal for a deeper understanding of the physiological and pathological processes of bone tissue.Future research should further elucidate the specific mechanisms of H-type vessels in various disease contexts and promote the clinical translation of basic research findings.These efforts will provide innovative ideas and strategies for the prevention and treatment of bone-related diseases.

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.

Objective To evaluate the radiation dose levels induced by secondary neutrons at different locations inside proton therapy treatment rooms, and analyze the distribution characteristics of neutron energy spectra by combining experimental measurements with simulations, and to provide a theoretical basis and technical support for radiation protection design and management in proton therapy. Methods Multiple representative measurement points were established in the treatment rooms of two hospital-based proton therapy centers. The DIAMON neutron spectrometer was employed to perform in-situ measurements of secondary neutron doses and energy spectra. Three-dimensional simulation models of treatment rooms were constructed using the FLUKA code to simulate the generation and transport of secondary neutrons. Results Measurements showed that the neutron dose was highest near the target region, reaching up to 4293.750 µSv/h. The doses at the maze entrance were the next highest, ranging from 422.091 to 489.268 µSv/h, while the doses in the middle section of the maze ranged from 2.860 to 6.727 µSv/h. The neutron energy spectra exhibited a triple-peak pattern. Conclusion Significant accumulation of secondary neutrons was observed near the target and in the maze regions during proton therapy, with intermediate-energy neutrons being the dominant dose contributor. The consistency between measurements and simulations validated the reliability of the model, which can provide a basis for optimizing treatment room shielding and controlling occupational exposure for staff.

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.