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 potential mechanism of Cigu Xiaozhi Prescription(CGXP)in the treatment of non-alco-holic steatohepatitis(NASH)liver fibrosis.METHODS A NASH mouse model was established.The degree of liver enlargement was evaluated by calculating the liver index.Hematoxylin-eosin(HE)and Masson staining were used to observe the degree of liver fibro-sis.In addition,immunohistochemistry was employed to detect the expression of liver fibrosis-related proteins,including α-SMA,Collagen 1,MMP2,and MMP9.Western blot and qPCR techniques were used to detect the expression levels of HIF-1α,E-cadher-in,N-cadherin,Shh,Smo,Gli1,and Gli2 in the mouse liver.The alkaline hydrolysis method was used to measure the content of liv-er hydroxyproline.RESULTS CGXP could effectively reduce the liver index(P<0.001),alleviate liver enlargement and inflamma-tion,and significantly improve the pathological damage of liver tissue in mice with liver fibrosis.CGXP significantly decreased the ex-pression levels of liver fibrosis-related proteins α-SMA,Collagen 1,MMP2 and MMP9(P<0.01,P<0.000 1);reduced the levels of HIF-1α,E-cadherin,N-cadherin,Shh,Smo,Gli1,and Gli2,and the therapeutic effect of high-dose CGXP was particularly significant(P<0.05,P<0.01,P<0.001,P<0.000 1).CONCLUSION CGXP can relieve NASH liver fibrosis in mice by reducing the liver index,alleviating inflammation,and improving tissue pathological damage.The mechanism may be related to the inhibition of the Hedgehog signaling pathway,which alters the activation and proliferation of hepatic stellate cells and reduces the synthesis and dep-osition of extracellular matrix.

Objective To evaluate the effectiveness of active screening in improving the detection rate of carbape-nem-resistant Enterobacterales(CRE)in the intensive care units(ICUs).Methods From July 2023 to June 2024,active screening of rectal swab CRE was conducted on ICU patients in 10 hospitals.ICU patients who underwent ac-tive screening from July 2023 to June 2024 were selected as the study group,while those who did not undergo active screening from July 2022 to June 2023 were selected as the control group.Difference in CRE detection rates between the two groups of patients was compared.Results A total of 7 803 ICU patients were included in the study group,744 CRE strains were detected,with a detection rate of 9.53％,out of which 304 CRE strains were detected through routine detection(detection rate 3.90％),3 707 patients underwent active screen,440 CRE strains were detected(detection rate 11.87％).7 561 ICU patients were included in the control group,out of which 250 CRE strains were detected through routine detection,with a detection rate of 3.31％.There was a statistically significant difference in the overall detection rate of CRE between two groups of patients(x2=246.18,P＜0.001).In the study group,CRE detection rate of active screening(11.87％)was higher than that of routine detection(3.90％),with statistically significant difference(x2=264.26,P＜0.001).A total of 17 CRE strains were detected from the study group.The proportions of Klebsiella pneumoniae(80.92％vs 73.41％)and Serratia marcescens(2.30％vs0.23％)in the routine detection group were both higher than in the active screening group,while the proportion of Escherichia coli in the routine detection group was lower(8.22％vs 19.55％),all with statistically significant differences(all P＜0.05).Conclusion The prevalence of CRE in ICUs is relatively high,with a wide range of bac-terial species.Active screening can improve the detection rate of CRE.

OBJECTIVES: To analyze the potential causal relationship between gut microbiota and food allergy (FA) using two-sample Mendelian randomization (MR) methods. METHODS: Data from genome-wide association studies on gut microbiota and FA were utilized. MR analysis was conducted employing inverse variance weighting, MR-Egger regression, and weighted median methods to assess the causal relationship between gut microbiota and FA. Cochrane's Q test was used to evaluate heterogeneity of instrumental variables, MR-PRESSO analysis was conducted to test for outliers and pleiotropy, and MR-Egger regression was employed to assess horizontal pleiotropy. The "leave-one-out" method was used to evaluate the impact of removing individual single nucleotide polymorphisms on the causal relationship. RESULTS: Inverse variance weighting analysis revealed that the phylum Verrucomicrobia, family Verrucomicrobiaceae, order Verrucomicrobiales, genus Ruminococcaceae UCG013, and genus Akkermansia were negatively associated with FA (P<0.05). Sensitivity analyses confirmed the reliability of the findings, indicating no heterogeneity or pleiotropy present. CONCLUSIONS There is a causal relationship between gut microbiota and FA, with Verrucomicrobia, Verrucomicrobiaceae, Verrucomicrobiales, Ruminococcaceae UCG013, and Akkermansia potentially reducing the risk of developing FA. These findings provide potential targets for the treatment and prevention of FA; however, further research is needed to explore the specific mechanisms by which the microbiota influence FA.
Humans ; Mendelian Randomization Analysis ; Gastrointestinal Microbiome ; Food Hypersensitivity/microbiology* ; Genome-Wide Association Study ; Polymorphism, Single Nucleotide

OBJECTIVE: To observe the clinical efficacy and safety of automatic pressure-controlled pressure cupping in patients with lumbar disc herniation, and compare it with traditional cupping. METHODS: A total of 100 patients diagnosed with lumbar disc herniation from January 2022 to August 2024 were selected and divided into two groups:the automatic pressure-controlled pressure cupping group (controlled pressure cupping group) and the traditional cupping group (control group), 50 cases in each group. In the controlled pressure cupping group, there were 18 males and 32 females, with an age of (51.98±12.69) years;in the control group, there were 16 males and 34 females, with an age of (51.32±12.05) years. The visual analogue scale(VAS), comfort score, and lumbar range of motion were observed before treatment and after the 1st, 3rd, and 7th treatments to evaluate the efficacy and safety. RESULTS: All patients completed the treatment intervention, with complete follow-up data collected. No adverse reactions or complications occurred during treatment and follow-up. After the 3rd treatment, the VAS score of the controlled pressure cupping group was (2.38±0.49), which was lower than that of the control group (2.94±0.68), with a statistically significant difference (P<0.001). In the controlled pressure cupping group, the VAS scores after the 1st, 3rd, and 7th treatments were significantly better than those before treatment (P=0.026);in the control group, the VAS scores after the 3rd and 7th treatments were better than those before treatment, but the difference was not statistically significant(P=0.182). Repeated-measures analysis of variance (ANOVA) on VAS scores at different time points in both groups showed that there were statistically significant differences in inter-group, time, and interaction effects (P<0.05). After the 1st treatment, in the controlled pressure cupping group, 0 patients felt comfortable, 42 patients (84%) felt mild discomfort, and 8 patients (16%) felt moderate discomfort;in the control group, 0 patients felt comfortable, 28 patients (56%) felt mild discomfort, and 22 patients(44%) felt moderate discomfort;the difference between the two groups was statistically significant(P=0.005). After the 3rd treatment, in the controlled pressure cupping group, 30 patients(60%) felt comfortable, 20 patients (40%) felt mild discomfort, and 0 patients felt moderate discomfort; in the control group, 9 patients (18%) felt comfortable, 41 patients (82%) felt mild discomfort, and 0 patients felt moderate discomfort;the difference between the two groups was statistically significant(P<0.001). There was no statistically significant difference in comfort between the two groups after the 7th treatment(P>0.001). There was no statistically significant difference in lumbar range of motion between the two groups before and after treatment(P>0.05);compared with before treatment, the lumbar range of motion of both groups after treatment was significantly improved, with statistically significant differences (P<0.001). CONCLUSION Automatic pressure-controlled pressure cupping can effectively relieve symptoms in patients with lumbar disc herniation, with excellent safety.
Humans ; Female ; Male ; Intervertebral Disc Displacement/physiopathology* ; Middle Aged ; Adult ; Lumbar Vertebrae/physiopathology* ; Cupping Therapy/methods* ; Pressure ; Aged ; Treatment Outcome

PURPOSE: To methodically assess the effectiveness of augmentative plating (AP) and exchange nailing (EN) in managing nonunion following intramedullary nailing for long bone fractures of the lower extremity. METHODS: PubMed, EMBASE, Web of Science, and the Cochrane Library were searched to gather clinical studies regarding the use of AP and EN techniques in the treatment of nonunion following intramedullary nailing of lower extremity long bones. The search was conducted up until May 2023. The original studies underwent an independent assessment of their quality, a process conducted utilizing the Newcastle-Ottawa scale. Data were retrieved from these studies, and meta-analysis was executed utilizing Review Manager 5.3. RESULTS: This meta-analysis included 8 studies involving 661 participants, with 305 in the AP group and 356 in the EN group. The results of the meta-analysis demonstrated that the AP group exhibited a higher rate of union (odds ratio: 8.61, 95% confidence intervals (CI): 4.12 - 17.99, p < 0.001), shorter union time (standardized mean difference (SMD): -1.08, 95% CI: -1.79 - -0.37, p = 0.003), reduced duration of the surgical procedure (SMD: -0.56, 95% CI: -0.93 - -0.19, p = 0.003), less bleeding (SMD: -1.5, 95% CI: -2.81 - -0.18, p = 0.03), and a lower incidence of complications (relative risk: -0.17, 95% CI: -0.27 - -0.06, p = 0.001). In the subgroup analysis, the time for union in the AP group in nonisthmal and isthmal nonunion of lower extremity long bones was shorter compared to the EN group (nonisthmal SMD: -1.94, 95% CI: -3.28 - -0.61, p < 0.001; isthmal SMD: -1.08, 95% CI: -1.64 - -0.52, p = 0.002). CONCLUSION In the treatment of nonunion in diaphyseal fractures of the long bones in the lower extremity, the AP approach is superior to EN, both intraoperatively (with reduced duration of the surgical procedure and diminished blood loss) and postoperatively (with an elevated union rate, shorter union time, and lower incidence of complications). Specifically, in the management of nonunion of lower extremity long bones with non-isthmal and isthmal intramedullary nails, AP demonstrated shorter union time in comparison to EN.
Humans ; Bone Nails/adverse effects* ; Bone Plates/adverse effects* ; Femoral Fractures/surgery* ; Fracture Fixation, Intramedullary/methods* ; Fractures, Ununited/surgery* ; Lower Extremity/injuries*

OBJECTIVE To explore the potential mechanism of Cigu Xiaozhi Prescription(CGXP)in the treatment of non-alco-holic steatohepatitis(NASH)liver fibrosis.METHODS A NASH mouse model was established.The degree of liver enlargement was evaluated by calculating the liver index.Hematoxylin-eosin(HE)and Masson staining were used to observe the degree of liver fibro-sis.In addition,immunohistochemistry was employed to detect the expression of liver fibrosis-related proteins,including α-SMA,Collagen 1,MMP2,and MMP9.Western blot and qPCR techniques were used to detect the expression levels of HIF-1α,E-cadher-in,N-cadherin,Shh,Smo,Gli1,and Gli2 in the mouse liver.The alkaline hydrolysis method was used to measure the content of liv-er hydroxyproline.RESULTS CGXP could effectively reduce the liver index(P<0.001),alleviate liver enlargement and inflamma-tion,and significantly improve the pathological damage of liver tissue in mice with liver fibrosis.CGXP significantly decreased the ex-pression levels of liver fibrosis-related proteins α-SMA,Collagen 1,MMP2 and MMP9(P<0.01,P<0.000 1);reduced the levels of HIF-1α,E-cadherin,N-cadherin,Shh,Smo,Gli1,and Gli2,and the therapeutic effect of high-dose CGXP was particularly significant(P<0.05,P<0.01,P<0.001,P<0.000 1).CONCLUSION CGXP can relieve NASH liver fibrosis in mice by reducing the liver index,alleviating inflammation,and improving tissue pathological damage.The mechanism may be related to the inhibition of the Hedgehog signaling pathway,which alters the activation and proliferation of hepatic stellate cells and reduces the synthesis and dep-osition of extracellular matrix.

Objective To evaluate the effectiveness of active screening in improving the detection rate of carbape-nem-resistant Enterobacterales(CRE)in the intensive care units(ICUs).Methods From July 2023 to June 2024,active screening of rectal swab CRE was conducted on ICU patients in 10 hospitals.ICU patients who underwent ac-tive screening from July 2023 to June 2024 were selected as the study group,while those who did not undergo active screening from July 2022 to June 2023 were selected as the control group.Difference in CRE detection rates between the two groups of patients was compared.Results A total of 7 803 ICU patients were included in the study group,744 CRE strains were detected,with a detection rate of 9.53％,out of which 304 CRE strains were detected through routine detection(detection rate 3.90％),3 707 patients underwent active screen,440 CRE strains were detected(detection rate 11.87％).7 561 ICU patients were included in the control group,out of which 250 CRE strains were detected through routine detection,with a detection rate of 3.31％.There was a statistically significant difference in the overall detection rate of CRE between two groups of patients(x2=246.18,P＜0.001).In the study group,CRE detection rate of active screening(11.87％)was higher than that of routine detection(3.90％),with statistically significant difference(x2=264.26,P＜0.001).A total of 17 CRE strains were detected from the study group.The proportions of Klebsiella pneumoniae(80.92％vs 73.41％)and Serratia marcescens(2.30％vs0.23％)in the routine detection group were both higher than in the active screening group,while the proportion of Escherichia coli in the routine detection group was lower(8.22％vs 19.55％),all with statistically significant differences(all P＜0.05).Conclusion The prevalence of CRE in ICUs is relatively high,with a wide range of bac-terial species.Active screening can improve the detection rate of CRE.