Diabetic retinopathy(DR)remains the leading cause of vision loss in patients with diabetes. Current anti-vascular endothelial growth factor(VEGF)therapies are limited by inadequate response in some patients and the necessity for repeated intravitreal injections, underscoring the urgent need for novel therapeutic targets. Angiopoietin-like protein 4(ANGPTL4), a multifunctional secreted protein, has emerged as a critical regulator in the pathogenesis and progression of DR, positioning it as a promising interventional target. This review systematically elaborates the biological characteristics of ANGPTL4, with a focus on its expression dynamics, molecular mechanisms, and regulatory networks rolesin the development of DR. Furthermore, the prospects of ANGPTL4-targeted therapeutic strategies are discussed, aiming to offer new insights and directions for understanding DR pathogenesis, advancing multi-target drug development, and improving clinical management.

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-quality 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.

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-quality 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.

Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition-and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.
Humans ; Immunoglobulin G4-Related Disease/metabolism* ; Immunoglobulin G/metabolism* ; Machine Learning ; Animals ; Proteomics/methods*

Intracranial atherosclerotic stenosis (ICAS) is a common cause of ischemic stroke. The evaluation of its structure and function is of great significance for formulating clinical intervention strategies. The indications for endovascular treatment of ICAS lesions in the past were mainly based on the degree of luminal stenosis showed by cerebral angiography, which had certain limitations. The translesional pressure ratio (PR), as an important indicator for functional assessment after arterial stenosis, has gradually received attention in the evaluation of ICAS lesions in recent years. This article reviews the evaluation methods and clinical significance of PR in ICAS lesions.

BACKGROUND:In recent years,epidemiological studies have shown that sleep patterns are risk factors for osteoarthritis,but the causal relationship between sleep characteristics and osteoarthritis remains unknown. OBJECTIVE:To investigate the causal relationship between seven sleep phenotypes and osteoarthritis,thereby providing a theoretical foundation for clinical prevention and intervention of osteoarthritis. METHODS:Seven sleep-related features,namely sleep duration,wake-up time,daytime napping,morning/evening preference,snoring,insomnia,and hypersomnia,were selected from published genome-wide association studies.Instrumental variables for these sleep-related features were extracted.Instrumental variables for knee osteoarthritis and hip osteoarthritis were obtained from publicly available genome-wide association studies.Causal relationships between sleep characteristics and outcome risks were evaluated using two-sample and multivariable Mendelian randomization analyses.The inverse variance weighted method was employed as the primary Mendelian randomization approach.Various methods,including weighted median,weighted mode,Mendelian randomization-Egger regression,Mendelian randomization pleiotropy-residual sum and outlier,were utilized to detect and correct for the presence of pleiotropy. RESULTS AND CONCLUSION:The results of the inverse variance-weighted method in the two-sample Mendelian randomization study revealed a detrimental causal association between the duration of sleep and the incidence risk of knee osteoarthritis[odds ratio(OR)=0.621,95%confidence interval(CI):0.470-0.822,P=0.001].Concurrently,insomnia displayed a positive causal connection with hip osteoarthritis risk(OR=2.016,95%CI:1.249-3.254,P=0.005).Sensitivity analysis affirmed the robustness of these causal relationships,and Mendelian randomization-Egger intercept analysis found no evidence of potential horizontal pleiotropy(knee osteoarthritis:P=0.468,hip osteoarthritis:P=0.551).Moreover,the results from the multivariable Mendelian randomization analysis showed that the causal association between insomnia and hip osteoarthritis lacked statistical significance(P=0.715).In contrast,sleep duration exhibited a direct negative causal relationship with the incidence risk of knee osteoarthritis(OR=0.526,95%CI:0.336-0.824,P=0.005).Reverse Mendelian randomization analysis indicated that knee osteoarthritis did not influence sleep duration(P=0.757).These findings indicate a negative correlation between sleep duration and incidence risk of knee osteoarthritis,suggesting that correcting insufficient sleep might mitigate the incidence risk of knee osteoarthritis.

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.

Objective To investigate the causal relationship between gut microbiota and pigmented villonodular synovitis using Mendelian randomization analysis.Methods We conducted a two-sample Mendelian randomization analysis to investigate the causal relationship between 211 gut microbiome taxa and pigmented villonodular synovitis based on GWAS summary data,with inverse variance weighted(IVW)analysis as the primary result and the other methods as supplementary analyses.The reliability of the results was tested using Cochran's Q test,MR-Egger regression,MR-PRESSO method and conditional Mendelian randomization analysis(cML-MA).Results The increased abundance of Barnesiella(OR=3.12,95%CI:1.15-8.41,P=0.025)and Rumatococcaceae UCG010(OR=4.03,95%CI:1.19-13.68,P=0.025)may increase the risk of pigmented villous nodular synovitis,and elevated abundance of Lachnospiraceae(OR=0.33,95%CI:0.12-0.91,P=0.032),Alistipes(OR=0.16,95%CI:0.05-0.53,P=0.003),Blautia(OR=0.20,95%CI:0.06-0.61,P=0.005),and Lachnospiraceae FCS020 group(OR=0.38,95%CI:0.15-0.94,P=0.036)and Ruminococcaceae UCG014(OR=0.36,95%CI:0.14-0.94,P=0.037)were all associated with a reduced risk of pigmented villonodular synovitis,which were supported by the results of sensitivity analyses.Reverse Mendelian randomization analysis did not reveal any inverse causal association.Conclusion Increased abundance of specific intestinal microorganisms is associated with increased or decreased risks of developing hyperpigmented villonodular synovitis,and gut microbiota plays an important role in the pathogenesis of this disease.

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.

Acute kidney injury （AKI） is a clinical syndrome characterized by a rapid decline in renal function over a short period due to various etiologic factors. If left untreated， AKI can progress to chronic kidney disease （CKD） or even end-stage renal disease （ESRD）. Although continuous renal replacement therapy （CRRT） can manage severe AKI， effective pharmacological treatments for AKI remain largely unavailable. Chinese medicine， with its multi-target and multi-pathway approaches， has accumulated substantial theoretical and practical knowledge in treating AKI and related complications. Rehmanniae Radix is a commonly used Chinese medicinal， known for its functions in clearing heat， cooling blood， nourishing yin， and promoting fluid production. The primary active ingredients of Rehmanniae Radix include catalpol， acteoside， and aucubin. In this study， we summarized recent research on the effect of the active ingredients of Rehmanniae Radix in preventing and treating AKI. We found that the key mechanisms underlying its anti-AKI effects include amelioration of inflammation， alleviation of oxidative stress， and inhibition of apoptosis. Additionally， the antifibrotic properties of the active ingredients of Rehmanniae Radix suggest its potential in slowing CKD progression. We reviewed the mechanisms of Rehmanniae Radix in treating AKI and its antifibrotic effects to provide a scientific basis for developing new AKI drugs， promoting the utilization of Rehmanniae Radix resources， and reducing the transition from AKI to CKD.