OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objective To explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods Patients scheduled to undergo on-pump cardiac valve surgery in the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, between January and July 2022 were initially enrolled. Eligible patients were randomly assigned at a 1 : 1 ratio to either the RIPC group or the control group. Relevant indicators of heart rate variability [standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency (LF) component, high frequency (HF) component and LF/HF] at 8 hours in the morning on the surgical day between two groups were compared. Results A total of 118 patients were initially assessed. After screening, 58 patients were excluded, and 60 patients provided written informed consent and were enrolled in the trial, with 30 allocated to the RIPC group and 30 to the control group. Seven patients in the control group and 5 patients in the RIPC group were subsequently excluded due to missing heart rate variability data resulting from cancelled operations. Finally, 23 patients in the control group and 25 patients in the RIPC group were included in the analysis. There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.

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.

Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

Objective:To explore the risk factors of adverse events during the perioperative period of transurethral resection of bladder tumor(TURBT)in bladder cancer patients with coronary atherosclerotic heart disease(CAD).Methods:We retrospectively analyzed the clinical data of bladder cancer patients who underwent TURBT in Beijing Anzhen Hospital from June 2022 to September 2024.All patients with bladder cancer and CAD underwent coronary computed tomography angiography(CCTA)for diagnosis and assessment of CAD before surgery.Based on the CCTA results,the patients with bladder cancer and CAD were divided into two groups:those with mild to moderate coronary stenosis and those with severe coronary stenosis.The severe coronary stenosis group was further divided into two subgroups based on whether they received low-molecular-weight heparin(LMWH)bridging therapy or continued their anti-platelet treatment before surgery.Perioperative anticoagulation and antiplatelet strategies were adjusted according to the opinions of the specialists.The incidence of adverse events within 30 days postoperative-ly was followed up and analyzed.Results:A total of 80 bladder cancer patients with CAD who underwent TURBT were included in the study.Among the 80 patients with CAD,55(68.8％)had mild to moder-ate coronary stenosis,and 25(31.2％)had severe coronary stenosis.Compared with those had mild to moderate coronary stenosis,the patients who had severe coronary stenosis had a higher incidence of post-operative bleeding and pulmonary embolism,although the differences were not statistically significant(P＞0.05).However,the incidence of postoperative myocardial infarction was significantly higher in the patients who had severe coronary stenosis(P=0.034).Among the patients with severe coronary stenosis,8(32.0％)received LMWH bridging therapy before TURBT,and 17(68.0％)continued their previous antiplatelet treatment.Compared with those who continued antiplatelet treatment,the patients who re-ceived LMWH bridging therapy had a higher incidence of postoperative bleeding and pulmonary embo-lism,although the differences were not statistically significant(P＞0.05).However,the incidence of postoperative myocardial infarction was significantly higher in the LMWH bridging group(P=0.032).Conclusion:Patients with mild-to-moderate coronary stenosis demonstrate relatively low perioperative risk during TURBT procedures and may safely undergo TURBT following antiplatelet therapy discontinuation.Conversely,those with severe coronary stenosis exhibit significantly higher perioperative risk and require intensive monitoring.In bladder cancer patients with concomitant severe coronary stenosis,perioperative LMWH bridging therapy is associated with increased myocardial infarction risk,whereas continued anti-platelet therapy does not elevate postoperative bleeding risk.Current evidence therefore supports maintai-ning antiplatelet therapy in these patients,with appropriate bleeding risk assessment.

Objective To evaluate the clinical efficacy of hip-knee-ankle active and passive exercise therapy in patients with early-to mid-stage knee osteoarthritis(KOA).Methods A total of 180 patients with early to mid-stage knee osteoarthritis(KOA)were recruited from the First Affiliated Hospital of Hebei University of Tradi-tional Chinese Medicine between March 2023 and March 2024.Patients were randomly assigned to one of four groups:active movement group,passive movement group,combined movement group,and control group,with 45 patients in each group.The active movement group received hip-knee-ankle active movement therapy daily until the end of follow-up.The passive movement group underwent hip-knee-ankle passive movement therapy three times per week for two weeks.The combined movement group received both active and passive therapies.The control group was administered oral celecoxib capsules(200 mg once daily for two weeks).Joint function was assessed in all four groups before treatment,at two weeks post-treatment,and at 14 weeks post-treatment.The primary outcome measure was the WOMAC joint function score,while secondary outcomes included the WOMAC pain score,stiffness score,and quality of life score(SF-12).Results A total of 160 patients completed the trial,with 39 in the active group,42 in the passive group,40 in the combined group,and 39 in the control group.There were no significant differences in baseline characteristics among the groups(P>0.05).Compared to baseline,the WOMAC scores for function,pain,and stiffness in the passive,combined,and control groups decreased significantly at both 2 and 14 weeks post-treatment(P<0.05),while the SF-12 scores increased significantly(P<0.05).Between 2 and 14 weeks post-treat-ment,the active and combined groups showed further significant decreases in WOMAC function,pain,and stiffness scores(P<0.05)and increases in SF-12 scores(P<0.05).At 2 weeks post-treatment,compared to the control group,the passive and combined groups exhibited significantly lower WOMAC function scores(P<0.05),with no significant difference between the passive and combined groups(P>0.05).By 14 weeks post-treatment,the active and combined groups demonstrated significantly lower WOMAC function scores(P<0.05),with the combined group showing a significantly lower score than the active group(P<0.05).Conclusion The four therapeutic approaches demonstrate a certain degree of efficacy in improving joint function for patients with early and mid-stage KOA.The passive therapy group exhibits superior short-term outcomes,while the active therapy group shows better long-term benefits.The combined therapy group presents notable advantages in both short-term and long-term effi-cacy,although its short-term effectiveness does not surpass that of the passive therapy group.It is recommended for patients with early and mid-stage KOA who have underlying gastrointestinal and cardiovascular conditions.

Bone repair remains an important target in tissue engineering, making the development of bioactive scaffolds for effective bone defect repair a critical objective. In this study, β-tricalcium phosphate (β-TCP) scaffolds incorporated with processed pyritum decoction (PPD) were fabricated using three-dimensional (3D) printing-assisted freeze-casting. The produced composite scaffolds were evaluated for their mechanical strength, physicochemical properties, biocompatibility, in vitro pro-angiogenic activity, and in vivo efficacy in repairing rabbit femoral defects. They not only demonstrated excellent physicochemical properties, enhanced mechanical strength, and good biosafety but also significantly promoted the proliferation, migration, and aggregation of pro-angiogenic human umbilical vein endothelial cells (HUVECs). In vivo studies revealed that all scaffold groups facilitated osteogenesis at the bone defect site, with the β-TCP scaffolds loaded with PPD markedly enhancing the expression of neurogenic locus Notch homolog protein 1 (Notch1), vascular endothelial growth factor (VEGF), bone morphogenetic protein-2 (BMP-2), and osteopontin (OPN). Overall, the scaffolds developed in this study exhibited strong angiogenic and osteogenic capabilities both in vitro and in vivo. The incorporation of PPD notably promoted the angiogenic-osteogenic coupling, thereby accelerating bone repair, which suggests that PPD is a promising material for bone repair and that the PPD/β-TCP scaffolds hold great potential as a bone graft alternative.
Calcium Phosphates/chemistry* ; Animals ; Bone Regeneration ; Rabbits ; Tissue Scaffolds ; Printing, Three-Dimensional ; Humans ; Human Umbilical Vein Endothelial Cells ; Neovascularization, Physiologic ; Osteogenesis ; Tissue Engineering/methods* ; Biomimetic Materials ; Cell Proliferation ; Angiogenesis

Objective:To explore the clinical, imaging, and pathological features of glottic squamous cell carcinoma of the larynx and their relationship with prognosis. Methods:A retrospective analysis was conducted on the clinical, imaging, and pathological data of 130 patients with glottic squamous cell carcinoma of the larynx who were treated at the First People's Hospital of Yinchuan and the General Hospital of Ningxia Medical University from January 2018 to March 2023. Imaging examinations （CT and MRI） were used to evaluate the lesion boundary clarity, density, enhancement nature, and enhancement degree. Postoperative pathological examination was used to determine the pathological nature, immunohistochemistry, etc. Statistical methods such as χ² test, Spearman correlation analysis, multivariate logistic regression analysis, and Kaplan-Meier method were used to analyze the data. Results:Among the 130 patients, 127 were male and 3 were female, with an average age of （61.92±9.595） years. There was a correlation between clinical, imaging, and pathological features. Multivariate analysis showed that heterogeneous MRI density （OR=12.414;P=0.019） and squamous cell carcinoma as a subtype were correlated. The initial symptom of non-hoarseness （HR=6.045;P=0.010） and unclear MRI boundary （HR=12.559; P=0.029） were independent risk factors for poor prognosis in patients with glottic squamous cell carcinoma of the larynx. Conclusion:There is a correlation between the clinical, imaging, and pathological features of patients with glottic squamous cell carcinoma of the larynx, and they can affect prognosis. The initial symptom of non-hoarseness and unclear MRI boundary of the tumor are independent risk factors for poor prognosis.
Humans ; Laryngeal Neoplasms/diagnosis* ; Prognosis ; Male ; Female ; Retrospective Studies ; Middle Aged ; Carcinoma, Squamous Cell/diagnosis* ; Magnetic Resonance Imaging ; Glottis/pathology* ; Tomography, X-Ray Computed ; Aged

Stearoyl-coenzyme A desaturase 1 (SCD1) catalyzes the rate-limiting step of de novo lipogenesis and modulates lipid homeostasis. Although numerous SCD1 inhibitors were tested for treating metabolic disorders both in preclinical and clinic studies, the tissue-specific roles of SCD1 in modulating obesity-associated metabolic disorders and determining the pharmacological effect of chemical SCD1 inhibition remain unclear. Here a novel role for intestinal SCD1 in obesity-associated metabolic disorders was uncovered. Intestinal SCD1 was found to be induced during obesity progression both in humans and mice. Intestine-specific, but not liver-specific, SCD1 deficiency reduced obesity and hepatic steatosis. A939572, an SCD1-specific inhibitor, ameliorated obesity and hepatic steatosis dependent on intestinal, but not hepatic, SCD1. Mechanistically, intestinal SCD1 deficiency impeded obesity-induced oxidative stress through its novel function of inducing metallothionein 1 in intestinal epithelial cells. These results suggest that intestinal SCD1 could be a viable target that underlies the pharmacological effect of chemical SCD1 inhibition in the treatment of obesity-associated metabolic disorders.

Objective To explore the effect of young plasma intraperitoneal injection on the fertility and ovarian function of aging mice and analyze its potential molecular mechanism.Methods Fifty-four-week-old female C57BL/6 mice and 8-week-old male C57BL/6 mice were selected.Among them,the female mice were randomly divided into three groups:the young plasma group,the aging plasma group and the normal saline group.The young plasma group and the aging plasma group received intraperitoneal injection of plasma from young(25-29 years old)and elderly(45-49 years old)female donors,respectively.Each injection was 500 μL,administered every other day for 2 weeks.The saline group received an equal volume of saline.After the last injection,mating experiments were conducted to evaluate fertility.Ovarian histopathological changes were observed by HE staining.Oocytes and fertilized eggs were collected after superovulation and cultured in vitro to assess oocyte quality and embryo developmental potential.Transcriptomic analysis of ovarian tissue was performed,followed by KEGG and GO enrichment analysis.Results Compared with the normal saline group and the aging plasma group,the number of offspring increased in the young plasma group,which reflected higher extrusion rate of first polar body(PB1),decreased fragmentation rate of oocytes and increased conversion rate of two-cell embryos and increased formation rate of blastocysts.There were no significant differences in these indicators between the aging plasma group and the normal saline group.Transcriptomic sequencing revealed that the differentially expressed genes in ovarian tissue of the young plasma group were mainly involved in steroid biosynthesis and metabolic pathways.Among them,the expression level of steroid sulfatase protein was significantly upregulated.Conclusion Systemic infusion of young plasma enhances the reproductive potential of aging ovaries in elderly mice.The sulfated steroid metabolites in plasma may be key substances in restoring ovarian function and delaying the process of ovarian aging.