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: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 application value of different Trocar layouts in retroperitoneal laparoscopic partial nephrectomy for localized renal cell carcinoma, so as to provide reference for the layout of Trocar during operation. Methods The data of 137 patients with localized renal cell carcinoma who underwent retroperitoneal laparoscopic partial nephrectomy in our hospital during Sep. 2021 and Sep. 2024 were retrospectively collected, including 68 patients with umbrella layout(umbrella group)and 69 with trapezoidal layout(trapezoidal group). The operation-related indexes, renal function(glomerular filtration rate, serum creatinine), traumatic stress indexes including C-reactive protein(CRP), cortisol, and interleukin-6(IL-6), complications and recurrence rate were compared between the two groups. Results There were no significant differences in pathological results, negative rate of surgical margins, hospitalization time, complications, 1-year recurrence rate, 6-month glomerular filtration rate and serum creatinine between the two groups(P＞0.05). Compared with the trapezoidal group, the umbrella group had less intraoperative blood loss [(72.11±18.30)mL vs.(87.59±20.63)mL], shorter operation time [(88.15±16.92)min vs.(99.40±20.78)min], renal warm ischemia time [(14.68±4.60)vs.(19.27±6.11)min], bowel sound recovery time [(21.00±5.75)h vs.(25.78±4.89)h] and ambulation time [(1.72±0.48)d vs.(2.04±0.55)d](P＜0.05). The umbrella group had lower CRP [(10.02±2.78)mg/ L vs.(12.00±3.15)mg/ L] on the first day after surgery, lower CRP [(7.89±0.76)vs.(8.77±1.25)mg/L] on the third day after surgery, lower cortisol [(0.67±0.20)μmol/L vs.(0.89±0.28)μmol/L] on the first day after surgery, lower cortisol [(0.58±0.15)μmol/L vs.(0.69±0.12)μmol/L] on the third day after surgery, lower IL-6 [(28.20±6.74)ng/L vs.(35.16±7.30)ng/L] on the first day after surgery, and lower IL-6[(21.25±5.39)ng/L vs.(29.78±4.26)ng/L] on the third day after surgery(P＜0.05). Conclusion Compared with the trapezoidal Trocar layout, the umbrella Trocar layout for retroperitoneal laparoscopic partial nephrectomy in patients with localized renal cell carcinoma offers advantages such as minimal surgical trauma, shorter operation and renal warm ischemia time, and faster postoperative recovery.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.

Objective To investigate the safety and efficacy of combined confluence microdissection or balloon dilatation followed by laparoscopic transcystic common bile duct exploration (LTCBDE) in day surgery laparoscopic cholecystectomy. Methods The clinical data of 203 patients with day surgery laparoscopic cholecystectomy combined with LTCBDE from June 2021 to June 2024 in Beijing Friendship Hospital, Capital Medical University were retrospectively analyzed. They were divided into an observation group (59 cases, including 42 cases of confluent microdissection and 17 cases of balloon dilatation) and a conventional group (144 cases) according to the surgical technique used. Baseline characteristics, intraoperative exploration results, operation time, postoperative recovery and complications were compared between the two groups. Results The history of preoperative cholangitis or pancreatitis (P<0.001) was more common in the observation group. And total bilirubin level was significantly higher in the observation group than in the conventional group (P=0.035). The observation group had a longer operative time (P=0.014) and higher hospitalization costs (P=0.001), but there was no difference in intraoperative bleeding and postoperative discharge time. There were no serious postoperative complications in either group. Conclusions Under the premise of strict patient screening, day surgery LTCBDE combined with confluence microdissection or balloon dilatation can effectively solve the problem of difficult choledochoscopic access, with high safety and thoroughness of exploration. Both techniques provide a feasible minimally observation solution for day surgery biliary exploration.

Objective:To investigate the correlation between metabolic syndrome (MS), its different components and the risk of cholecystolithiasis and gallbladder polyp in Uygur population in rural areas of southern Xinjiang.Methods:This study was a prospective cohort study. A baseline survey was conducted in August 2016. A typical sampling method was used to select 10 476 Uygur people in rural areas of southern Xinjiang as the research objects. Baseline clinical data were collected, including demographic data such as age, gender, and education level, and laboratory examination indicators such as blood glucose and triglyceride levels. According to the MS diagnostic criteria of the relevant guidelines, 10 476 subjects were divided into the MS group (3 475 cases) and the non-MS group (7 001 cases). The incidence of cholecystolithiasis and gallbladder polyp was followed up in 2019, 2021 and 2023, respectively. Cox regression was used to analyze the correlation between MS, its different components and the risk of cholecystolithiasis and gallbladder polyp. Chi-square test and independent sample t test were used for statistical analysis. Results:The median follow-up time was 6.43 years in 10 476 subjects, and the overall cumulative incidence of cholecystolithiasis and gallbladder polyp was 5.43% (569/10 476). The cumulative incidence of cholecystolithiasis and gallbladder polyp in the MS group was 10.73% (373/ 3 475), which was significantly higher than that in the non-MS group (2.80% (196/7 001)); χ2= 284.62, P<0.001). The results of multivariate Cox regression analysis showed that, 41 to 59 years old ( HR=1.26, 95% confidence interval (95% CI): 1.03 to 1.54, P=0.025), ≥60 years old ( HR=1.88, 95% CI: 1.45 to 2.45, P<0.001), female ( HR=1.34, 95% CI: 1.13 to 1.60, P=0.001), MS ( HR=2.19, 95% CI: 1.59 to 3.01, P<0.001), hypertriglyceridemia ( HR=1.47, 95% CI: 1.18 to 1.83, P=0.001), hypertension ( HR=1.30, 95% CI: 1.04 to 1.62, P=0.023), and hyperglycemia ( HR=1.24, 95% CI: 1.01 to 1.52, P=0.041) were independent risk factors for cholecystolithiasis and gallbladder polyp. After the adjustment of age and gender, MS ( HR=3.39, 95% CI: 2.82 to 4.07, P<0.001), hypertriglyceridemia ( HR=2.37, 95% CI: 2.00 to 2.81, P<0.001), hypertension ( HR=2.00, 95% CI: 1.66 to 2.41, P<0.001), and hyperglycemia ( HR=1.86, 95% CI: 1.55 to 2.23, P<0.001) were still correlated with cholecystolithiasis and gallbladder polyp, and there was the srtongest correlation between MS and cholecystolithiasis and gallbladder polyp. The results of univariate Cox regression analysis showed that along with the increase of accumulated of MS components, the risk of cholecystolithiasis and gallbladder polyp significantly increased (1 to 5 components corresponding HR (95% CI) were 1.92 (1.13 to 3.24), 2.21 (1.32 to 3.69), 6.91 (4.22 to 11.30), 8.56 (5.15 to 14.22), and 10.73 (5.66 to 20.33); P=0.015, =0.002, <0.001, <0.001, and <0.001); after age and gender were adjusted, this trend still existed (1 to 5 components corresponding HR (95% CI) were 1.81(1.07 to 3.06), 1.95(1.16 to 3.27), 5.64(3.42 to 9.32), 6.69(3.97 to 11.25), and 7.76(4.04 to 14.91); P=0.028, =0.012, <0.001, <0.001, and <0.001). Conclusion:MS and its components can increase the risk of cholecystolithiasis and gallbladder polyp, and the risk of cholecystolithiasis and gallbladder polyp significantly increases along with the increase of accumulated of MS components.

Liver fibrosis is a vital cause of morbidity in patients with liver diseases and developing novel anti-fibrotic drugs is imperative. Isovalerylspiramycin I (ISP I) as a major component of carrimycin applied to upper respiratory infections, was first found to possess anti-fibrotic potential. The present study aims to evaluate the functions and mechanisms of ISP I in protecting against liver fibrosis. According to our results, ISP I not only reduced the expressions of fibrogenic markers in LX-2 cells but also appeared great protective effects on liver injury and liver fibrosis in bile duct ligation (BDL) rats and carbon tetrachloride (CCl₄) mice. We proved that nucleotide-binding protein 2 (NUBP2) was the direct target of ISP I. ISP I through targeting NUBP2, increased the amount of vascular non-inflammatory molecule-1 (VNN1) on the cell membrane, which will inhibit oxidative stress and fibrosis. Simultaneously, the original carrimycin's protective effect on liver damage and fibrosis was verified. Therefore, our study provides potential agents for patients with liver fibrosis-related diseases, and the clear mechanism supports wide application in the clinic.

Xiaoaiping (XAP) Injection demonstrates the anti-prostate cancer (PCa) effects, yet the underlying mechanism remains unclear. This study aims to investigate the impact of XAP on PCa and elucidate its mechanism of action. PCa cell proliferation was evaluated using a cell counting kit-8 (CCK-8) assay. Cell apoptosis was assessed through Hoechst staining and Western blotting assays. Proteomics technology was employed to identify key molecules and significant signaling pathways modulated by XAP in PCa cells. To further validate potential key genes and important pathways, a series of assays were conducted, including acridine orange (AO) staining, transmission electron microscopy, and immunofluorescence assays. The molecular mechanism of XAP against PCa in vivo was examined using a PC3 xenograft mouse model. Results demonstrated that XAP significantly inhibited cell proliferation in multiple PCa cell lines. In C4-2 and prostate cancer cell line-3 (PC3) cells, XAP induced cellular apoptosis, evidenced by reduced B-cell lymphoma 2 (Bcl-2) levels and elevated Bcl-2-associated X (Bax) levels. Proteomic, immunofluorescence, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) investigations revealed a strong correlation between forkhead box O3a (FoxO3a) autophagic degradation and the anti-PCa action of XAP. XAP hindered autophagy by reducing the expression levels of autophagy-related protein 5 (Atg5)/autophagy-related protein 12 (Atg12) and enhancing FoxO3a expression and nuclear translocation. Furthermore, XAP exhibited potent anti-PCa action in PC3 xenograft mice and triggered FoxO3a nuclear translocation in tumor tissue. These findings suggest that XAP induces PCa apoptosis via inhibition of FoxO3a autophagic degradation, potentially offering a novel perspective on XAP injection as an effective anticancer therapy for PCa.
Male ; Humans ; Prostatic Neoplasms/physiopathology* ; Autophagy/drug effects* ; Animals ; Drugs, Chinese Herbal/pharmacology* ; Proteomics ; Mice ; Apoptosis/drug effects* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Forkhead Box Protein O3/genetics* ; Xenograft Model Antitumor Assays ; Mice, Nude ; Mice, Inbred BALB C