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.

Indocyanine green has been a popular near-infrared fluorescent reagent in recent years, and indocyanine green fluorescence imaging navigation surgery technology has been widely used in hepatobiliary surgery. Many factors affect imaging in various clinical scenarios, which are roughly divided into liver background fluorescence interference and penetrating tissue thickness, including the dose and timing of the drug, liver lesions of the patient, and individualization of the patient. Under the concept of precise surgery, we should individualise the treatment of patients to minimise the influence of unfavourable factors and further improve the safety and efficacy of surgery. Currently, there is no reliable research evidence to guide the use of indocyanine green in terms of influencing factors, and clinical studies are urgently needed to optimise the use of indocyanine green in hepatobiliary surgery.

Objective To use imaging features of pulmonary nodules to predict the risk of lymph node metastasis in patients with cT1-stage non-small cell lung cancer (NSCLC), providing a reference for clinical decision-making. Methods A retrospective analysis was conducted on the imaging features and postoperative pathological results of cT1 NSCLC patients who underwent surgical treatment at Linyi People’s Hospital from July 2019 to July 2022. Patients were grouped and analyzed according to lymph node metastasis status. Results A total of 1 123 patients were included, comprising 471 males and 652 females, with a median age of 59 (52, 66) years. Comparative analysis revealed that sex, age, nodule location, nodule size on imaging, solid component size, consolidation tumor ratio (CTR), average CT value, and tumor proximity to the pleura all influenced lymph node metastasis. A nomogram was constructed, indicating that the probability of lymph node metastasis in cT1 NSCLC was positively correlated with solid component size, CTR, and average CT value of the pulmonary nodule, and negatively correlated with patient age. The area under the receiver operating characteristic curve was 0.929. Conclusion For cT1 NSCLC patients, the probability of lymph node metastasis can be predicted by measuring the solid component size, CTR, and average CT value of the pulmonary nodule, in conjunction with patient age. However, relying solely on pulmonary nodule imaging characteristics is insufficient to determine a specific lymph node dissection strategy.

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

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 effects of a continuous-dose administration versus different dosage regimens of polyethylene glycol electrolyte solution(PEG)taken in two doses with a 12-hour interval on bowel cleansing efficacy,with the goal of optimizing bowel preparation protocols and improving patient tolerability.Methods 232 patients who underwent painless colonoscopy and used PEG as a bowel cleanser from June 2024 to September 2024 were selected as study subjects.Participants were divided into three groups:the control group(3.00 L PEG continuous dose),experimental group A(0.75 L+2.25 L PEG),and experimental group B(1.50 L+1.50 L PEG).All patients underwent painless colonoscopy within 4～6 h after completing PEG intake.The interval between the two doses of PEG in group A and group B was 12 h.The bowel cleansing efficacy was assessed by using the Boston bowel preparation scale(BBPS),and the rates of colon polyp detection,adverse reactions,sleep duration,and tolerability were recorded.Results There were no significant statistical differences in BBPS scores and colon polyp detection rates among the three groups(P>0.05).Experimental group B experienced the least adverse reactions,followed by experimental group A,while the control group reported the most significant adverse reactions(P<0.05).The timing of PEG administration did not have a significant impact on sleep duration among the three groups(P>0.05).Patients in experimental group B showed good tolerability to PEG and were willing to accept this bowel preparation regimen,followed by group A,while the control group exhibited the poorest tolerability,with significant statistical differences among the three groups(P<0.05).Conclusion The continuous administration and divided administration of PEG have no significant impact on the effectiveness of intestinal cleansing and the detection rate of colonic polyps.However,the divided PEG regimen with a 12 h interval results in fewer adverse reactions and better tolerance,especially the optimal regimen of taking 1.50 L PEG in two doses with a 12 h interval.

Objective To determine the chemical composition of TangNiaoLing Tablets by UHPLC-Q-Exactive-Orbitrap-MS/MS.Methods A Waters ACQUITY HSS T3 column(100 mm×2.1 mm,1.8 μm)was used for separation at a total flow rate of 0.2 mL/min.The mobile phase included an aqueous solution of 0.1%formic acid and acetonitrile mixed with 0.1%formic acid was supplied.The injection volume was set at 2 μL and the column oven temperature was 40℃.High-resolution mass spectrometric data were obtained by concurrently scanning the positive and negative ion modes.The identification was accomplished by inferring the empirical fragmentation patterns and comparing it with databases and references.Results 100 different chemical elements,including triterpenes,flavonoids,phenylpropanoids,phenylethanoid glycosides,iridoid glycosides,and phenols,among others were identified from the 50%methanol extract of TangNiaoLing pills.Conclusion The chemical contents of TangNiaoLing tablets were identified and analyzed using the UHPLC-Q-Exactive-Orbitrap-MS/MS method for the first time.This served as a foundation for future research into the tablets' effective components and quality control.

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.