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.

ObjectiveTo investigate the predictive factors for HBsAg clearance in chronic hepatitis B （CHB） patients with a low level of hepatitis B surface antigen （HBsAg） treated with pegylated interferon α-2b （PEG-IFN-α-2b）， to establish a combined predictive model and a nomogram based on multiple factors， and to provide a reference for formulating individualized treatment regimens and predicting treatment outcome in clinical practice. MethodsA retrospective analysis was performed for 167 CHB patients with HBsAg <1 500 IU/mL who attended The Third People’s Hospital of Kunming from January 2022 to January 2024 and were treated with PEG-IFN-α-2b. According to whether clinical cure was achieved， the patients were divided into HBsAg clearance group and HBsAg non-clearance group. Related data were collected， including general information and serological/biochemical/virological indicators at different time points during treatment. The independent samples t-test was used for comparison of normally distributed continuous data， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data； the chi-square test was used for comparison of categorical data. The multivariate logistic regression analysis was used to identify independent influencing factors. The receiver operating characteristic （ROC） curve was used to assess the value of indicators used alone or in combination in predicting clinical cure， and calibration curves were plotted to assess the risk prediction model. ResultsThe univariate analysis showed that there were significant differences between the two groups in age （t=-6.839， P<0.05）， history of nucleos（t）ide analogue treatment for over 1 year （χ²=59.339， P<0.05）， genotype （χ²=4.610， P<0.05）， nonalcoholic fatty liver disease （χ²=5.319， P<0.05）， hepatitis B virus DNA status before treatment （χ²=60.861， P <0.05）， compensated liver cirrhosis （χ²=10.960， P<0.05）， HBeAg status before treatment （χ²=19.060， P<0.05）， a history of interferon treatment （χ²=8.162， P<0.05）， presence of interferon antibodies after treatment （χ²=12.858， P<0.05）， HBsAg level before treatment （Z=-7.412， P<0.05）， alanine aminotransaminase （ALT） level at baseline （Z=-6.117， P<0.05）， ALT level at 12 weeks of treatment （Z=-7.171， P<0.05）， platelet count （PLT） at 24 weeks of treatment （Z=-3.622， P<0.05）， and thyroid stimulating hormone （TSH） level at 24 weeks of treatment （Z=-2.830， P<0.05）. The multivariate logistic regression analysis showed that age （odds ratio ［OR］=1.230， P=0.007）， history of nucleos（t）ide analogue treatment for over 1 year （OR=0.008， P=0.011）， HBeAg status before treatment （OR=0.003， P=0.012）， HBsAg level before treatment （OR=1.005， P=0.014）， ALT level at baseline （OR=0.949， P=0.014）， ALT level at 12 weeks of treatment （OR=0.969， P=0.016）， PLT at 24 weeks of treatment （OR=0.969， P=0.022）， and TSH level at 24 weeks of treatment （OR=3.608， P=0.045） were independent influencing factors for HBsAg clearance at 48 weeks of treatment in CHB patients with HBsAg <1 500 IU/mL. The Hosmer-Lemeshow goodness-of-fit test yielded χ²=1.398， P=0.994， indicating that the model had good fitting. The Bootstrap method was used to perform internal validation of the nomogram model， and there was a good degree of fitting between the calibration curve and the ideal curve， with a mean absolute error of 0.029. The ROC curve analysis showed that the combination of predictive factors had an area under the ROC curve of 0.982 （95% confidence interval： 0.961 — 0.999）， with a sensitivity of 94.10% and a specificity of 93.10%， suggesting that the nomogram model had a good discriminatory ability. For the CHB patients with HBsAg <1 500 IU/mL and different features， further analysis of HBsAg clearance rate at 48 weeks of treatment showed an HBsAg clearance rate of 69.60% for those with HBsAg ≤67.65 IU/mL before treatment， 58.30% for those with a baseline ALT level of ≥62.50 U/L， 68.30% for those with an ALT level of ≥92.50 U/L at 12 weeks of treatment， 42.40% for those with PLT ≥104×10⁹/L at 24 weeks of treatment， and 48.30% for those with a TSH level of ≤1.38 μIU/mL at 24 weeks of treatment， with significant differences between the two groups （all P<0.001）. ConclusionAge， history of nucleos（t）ide analogue treatment for over 1 year， HBeAg status before treatment， HBsAg level before treatment， baseline ALT level， ALT level at 12 weeks of treatment， PLT level at 24 weeks， and TSH level at 24 weeks of treatment are independent predictive factors. The combined prediction nomogram model constructed in this study has a relatively high value in predicting clinical cure at 48 weeks of PEG-IFN-α-2b treatment in CHB patients with HBsAg<1 500 IU/mL， thereby providing a reference for selecting suitable treatment population and predicting clinical cure.

ObjectiveTo investigate the influencing factors for hepatic hydrothorax （HH） before intervention for primary hepatic carcinoma （PHC）， and to construct and assess the nomogram risk prediction model. MethodsA retrospective analysis was performed for the clinical data of 353 hospitalized patients who attended the Third People’s Hospital of Kunming for the first time from October 2012 to October 2021 and there diagnosed with PHC， and according to the presence or absence of HH， they were divided into HH group with 153 patients and non-HH group with 200 patients. General data and the data of initial clinical testing after admission were collected from all PHC patients. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. After the multicollinearity test was performed for the variables with statistical significance determined by the univariate analysis， the multivariate Logistic regression analysis was used to identify independent influencing factors. The “rms” software package was used to construct a nomogram risk prediction model， and the Hosmer-Lemeshow test and the receiver operating characteristic （ROC） curve were used to assess the risk prediction model； the “Calibration Curves” software package was used to plot the calibration curve， and the “rmda” software package was used to plot the clinical decision curve and the clinical impact curve. ResultsAmong the 353 patients with PHC， there were 153 patients with HH， with a prevalence rate of 43.34%. Child-Pugh class B （odds ratio ［OR］=2.652， 95% confidence interval ［CI］： 1.050 — 6.698， P=0.039）， Child-Pugh class C （OR=7.963， 95%CI： 1.046‍ ‍—‍ ‍60.632， P=0.045）， total protein （OR=0.947， 95%CI： 0.914‍ ‍—‍ ‍0.981， P=0.003）， high-sensitivity C-reactive protein （OR=1.007， 95%CI： 1.001‍ ‍—‍ ‍1.014， P=0.025）， and interleukin-2 （OR=0.801， 95%CI： 0.653‍ ‍—‍ ‍0.981， P=0.032） were independent influencing factors for HH before PHC intervention， and a nomogram risk prediction model was established based on these factors. The Hosmer-Lemeshow test showed that the model had a good degree of fitting （χ²=5.006， P=0.757）， with an area under the ROC curve of 0.752 （95%CI： 0.701‍ ‍—‍ ‍0.803）， a sensitivity of 78.40%， and a specificity of 63.50%. The calibration curve showed that the model had good consistency in predicting HH before PHC intervention， and the clinical decision curve and the clinical impact curve showed that the model had good clinical practicability within a certain threshold range. ConclusionChild-Pugh class， total protein， interleukin-2， and high-sensitivity C-reactive protein are independent influencing factors for developing HH before PHC intervention， and the nomogram model established based on these factors can effectively predict the risk of developing HH.

ObjectiveTo investigate the influencing factors for recompensation in patients with decompensated hepatitis C cirrhosis， and to establish a predictive model. MethodsA total of 217 patients who were diagnosed with decompensated hepatitis C cirrhosis and were admitted to The Third People’s Hospital of Kunming l from January， 2019 to December， 2022 were enrolled， among whom 63 patients who were readmitted within at least 1 year and had no portal hypertension-related complications were enrolled as recompensation group， and 154 patients without recompensation were enrolled as control group. Related clinical data were collected， and univariate and multivariate analyses were performed for the factors that may affect the occurrence of recompensation. The independent-samples t test was used for comparison of normally distributed measurement data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed measurement data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. A binary Logistic regression analysis was used to investigate the influencing factors for recompensation in patients with decompensated hepatitis C cirrhosis， and the receiver operating characteristic （ROC） curve was used to assess the predictive performance of the model. ResultsAmong the 217 patients with decompensated hepatitis C cirrhosis， 63 （29.03%） had recompensation. There were significant differences between the recompensation group and the control group in HIV history （χ²=4.566， P=0.034）， history of partial splenic embolism （χ²=6.687， P=0.014）， Child-Pugh classification （χ²=11.978， P=0.003）， grade of ascites （χ²=14.229， P<0.001）， albumin （t=4.063， P<0.001）， prealbumin （Z=-3.077， P=0.002）， high-density lipoprotein （t=2.854， P=0.011）， high-sensitivity C-reactive protein （Z=-2.447， P=0.014）， prothrombin time （Z=-2.441， P=0.015）， carcinoembryonic antigen （Z=-2.113， P=0.035）， alpha-fetoprotein （AFP） （Z=-2.063， P=0.039）， CA125 （Z=-2.270， P=0.023）， TT3 （Z=-3.304， P<0.001）， TT4 （Z=-2.221， P=0.026）， CD45⁺ （Z=-2.278， P=0.023）， interleukin-5 （Z=-2.845， P=0.004）， tumor necrosis factor-α （Z=-2.176， P=0.030）， and portal vein width （Z=-5.283， P=0.005）. The multivariate analysis showed that history of partial splenic embolism （odds ratio ［OR］=3.064， P=0.049）， HIV history （OR=0.195， P=0.027）， a small amount of ascites （OR=3.390， P=0.017）， AFP （OR=1.003， P=0.004）， and portal vein width （OR=0.600， P<0.001） were independent influencing factors for the occurrence of recompensation in patients with decompensated hepatitis C cirrhosis. The ROC curve analysis showed that HIV history， grade of ascites， history of partial splenic embolism， AFP， portal vein width， and the combined predictive model of these indices had an area under the ROC curve of 0.556， 0.641， 0.560， 0.589， 0.745， and 0.817， respectively. ConclusionFor patients with decompensated hepatitis C cirrhosis， those with a history of partial splenic embolism， a small amount of ascites， and an increase in AFP level are more likely to experience recompensation， while those with a history of HIV and an increase in portal vein width are less likely to experience recompensation.

Objective To identify the most significantly compressed areas and the areas with the best recovery effects by analyzing the changes in vertebral height after percutaneous vertebroplasty(PVP)in patients with osteoporotic vertebral compression fractures(OVCF)through lateral radiographs.Methods A retrospective analysis was conducted on the lateral X-rays of 186 injured vertebrae from 142 patients hospitalized in our hospital's intervertebral disc center.The sagittal height of the vertebrae was measured at five different points before and after surgery,and the collected data were statistically analyzed using SPSS software.Results There were statistically significant differences in the heights of the five measured points before and after surgery within OVCF injured vertebrae(P＜0.05),in the ascending order:central＜mid-anterior＜mid-posterior＜anterior edge＜posterior edge.Comparison of the height parameters of the five measured points before and after surgery showed statistically significant differences(P＜0.01).In comparing the height restoration differences of the five measured points after PVP,the differences between central and mid-anterior,central and anterior edge,and mid-posterior and anterior edge were found not to be statistically significant(P＞0.05).The differences in height restoration for the remaining groups were statistically significant(P＜0.05),with the height restoration differences from highest to lowest being:mid-anterior,central,anterior edge,mid-posterior,posterior edge.Conclusion In patients with OVCF,the compression of the injured vertebra is most pronounced in the central part,followed by the mid-anterior part.PVP surgery can effectively restore the height of various parts of the injured vertebra,especially in the mid-anterior and central parts of the vertebral body,where the recovery effect is particularly significant.

ObjectiveTo investigate the value of noninvasive imaging detection （FibroScan）， two serological models of gamma-glutamyl transpeptidase-to-platelet ratio （GPR） score and S index， and two inflammatory factors of interleukin-6 （IL-6） and tumor necrosis factor-α （TNF-α） in predicting liver fibrosis in patients with HBeAg-positive chronic hepatitis B （CHB）， as well as the consistency of liver biopsy in pathological staging， and to provide early warning for early intervention of CHB. MethodsA retrospective analysis was performed for 131 HBeAg-positive CHB patients who underwent liver biopsy in The Third People’s Hospital of Kunming from January 2019 to December 2023. The results of liver biopsy were collected from all patients， and related examinations were performed before liver biopsy， including total bilirubin， alanine aminotransferase， platelet count， gamma-glutamyl transpeptidase， albumin， IL-6， TNF-α， liver stiffness measurement （LSM）， and abdominal ultrasound. An analysis of variance was used for comparison of normally distributed continuous data between groups， and the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between groups； the chi-square test was used for comparison of categorical data between groups. A Kappa analysis was used to investigate the consistency between LSM noninvasive histological staging and pathological staging based on liver biopsy， and the Spearman analysis was used to investigate the correlation between each variable and FibroScan in the diagnosis of liver fibrosis stage. The Logistic regression analysis was used to construct joint predictive factors. The receiver operating characteristic （ROC） curve was used to evaluate the value of each indicator alone and the joint predictive model in the diagnosis of liver fibrosis， and the Delong test was used for comparison of the area under the ROC curve （AUC）. ResultsIn the consistency check， inflammation degree based on liver biopsy had a Kappa value of 0.807 （P<0.001）， and liver fibrosis degree based on liver biopsy had a Kappa value of 0.827 （P<0.001）， suggesting that FibroScan noninvasive histological staging and liver biopsy showed good consistency in assessing inflammation degree and liver fibrosis stage. Age was positively correlated with LSM， GPR score， S index， IL-6， and TNF-α （all P<0.05）， and GPR score， S index， IL-6， and TNF-α were positively correlated with LSM （all P<0.05）. GPR score， S index， IL-6， and TNF-α were all independent risk factors for diagnosing significant liver fibrosis （≥S2） and progressive liver fibrosis （≥S3） （all P<0.05）. As for each indicator alone， GPR score had the highest value in the diagnosis of significant liver fibrosis （≥S2）， followed by S index， IL-6， and TNF-α， while S index had the highest value in the diagnosis of progressive liver fibrosis （≥S3）， followed by GPR score， TNF-α， and IL-6. The joint model had a higher predictive value than each indicator alone （all P<0.05）. ConclusionThere is a good consistency between FibroScan noninvasive histological staging and pathological staging based on liver biopsy. GPR score， S index， IL-6， and TNF-α are independent risk factors for evaluating different degree of liver fibrosis in CHB， and the combined prediction model established by them can better diagnose liver fibrosis.

Objective:To investigate the effects of the small molecule inhibitor of human antigen R (HuR)protein, MS-444 on intestinal fat absorption and triglyceride synthesis.Methods:MS-444 was administered by enema to normal chow diet(NCD)and high-fat diet(HFD)mouse models.Key indicators including body weight, serum triglycerides(TAG), serum free fatty acids(FFA), hepatic lipid deposition, and adipose tissue weights were measured.Additionally, the impact of MS-444 on intestinal lipid absorption and the triglyceride synthesis enzymes diacylglycerol acyltransferase 2 (DGAT2)and monoacylglycerol acyltransferase 2(MGAT2)were also assessed.Results:The study demonstrated that MS-444 enema markedly reduced serum TAG and FFA levels in both NCD-and HFD-fed mice [NCD mice TAG: (0.854±0.145)mmol vs.(0.608±0.120)mmol, P=0.032, NCD mice FFA: (0.650±0.119)mmol vs.(0.432±0.106)mmol, P=0.032; HFD mice TAG: (1.410±0.254)mmol vs.(0.704±0.132)mmol, P=0.008, HFD mice FFA: (1.202±0.195)mmol vs.(0.772±0.102)mmol, P=0.008], as well as hepatic lipid deposition [(14.300±0.704)μmol/g vs.(7.640±1.584)μmol/g, P=0.008] in HFD-fed mice.MS-444 modulated intestinal fat absorption by lowering TAG levels[(10.350±2.046)μmol/g vs.(6.802±2.037)μmol/g, P=0.031], while elevating intestinal FFA levels[(5.746±1.433)μmol/g vs.(8.050±1.121)μmol/g, P=0.032]. Mechanistically, MS-444 significantly inhibited the interaction between HuR and the enzymes DGAT2 and MGAT2 in the proximal small intestine, leading to downregulation of their mRNA and protein expression, thereby inhibiting intestinal triglyceride synthesis. Conclusions:MS-444 can alleviate HFD-induced non-alcoholic fatty liver disease(NAFLD)and obesity by reducing intestinal fat absorption.The functional interaction between HuR and the enzymes DGAT2 and MGAT2 may play a key regulatory role, indicating the therapeutic potential application value of MS-444 in metabolic disorders.

Objective To investigate the influencing factors for recompensation in patients with decompensated hepatitis B cirrhosis,and to establish a predictive model.Methods A total of 517 patients who attended The Third People's Hospital of Kunming and were diagnosed with decompensated hepatitis B cirrhosis from January 1,2016 to December 31,2022 were enrolled.The clinical data of the patients were reviewed,and the 207 patients with no portal hypertension-related complications within at least 1 year were enrolled as recompensation group,while the 310 patients without recompensation were enrolled as persistent decompensation group.Related clinical data were collected,and the univariate and multivariate Cox regression analyses were performed for the factors that might affect the occurrence of recompensation.The independent-samples t test was used for comparison of normally distributed continuous data between two groups,and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups;the chi-square test or the Fisher's exact test was used for comparison of categorical data between two groups.The"rms"package was used to establish a nomogram;the receiver operating characteristic(ROC)curve was plotted,and the area under the ROC curve(AUC)was calculated;the Hosmer-Lemeshow test was used to evaluate the degree of fitting of the model;the"Calibration Curves"package was used to plot the calibration curve for model assessment.Results Among the patients with decompensated hepatitis B cirrhosis,207(40.03%)had recompensation.The univariate Cox regression analysis showed that there were significant differences between the recompensation group and the persistent decompensation group in TIPS history,genotyping,portal vein thrombosis,complicated infection,Child-Pugh class,age,hemoglobin(Hb),platelet count,total protein,albumin(Alb),alanine aminotransferase,triglyceride,cholesterol,creatinine,Na,interleukin-6,high-sensitivity C-reactive protein,portal vein width,and portal vein velocity(all P<0.05).The multivariate regression analysis showed that TIPS history(hazard ratio[HR]=2.491,95%confidence interval[CI]:1.325-4.681,P=0.005),portal vein thrombosis(HR=0.345,95%CI:0.152-0.783,P=0.001),Hb(HR=1.007,95%CI:1.000-1.013,P=0.028),Alb(HR=1.048,95%CI:1.017-1.080,P=0.002),and portal vein width(HR=0.899,95%CI:0.835-0.967,P=0.004)were independent influencing factors for recompensation in patients with decompensated hepatitis B cirrhosis.A nomogram model was established based on the above five influencing factors,and the Hosmers-Lemeshow test showed that this model had a good degree of fitting(χ2=3.202,P=0.921).The nomogram model had an AUC of 0.728,a sensitivity of 50.3%,and a specificity of 85.0%,and the calibration curve showed good consistency between the actual value of this model in predicting the occurrence of recompensation and the predicted value in patients with decompensated hepatitis B cirrhosis.Conclusion Patients with decompensated hepatitis B cirrhosis who have a history of TIPS and high levels of Alb and Hb are more likely to have recompensation,and it is relatively difficult for patients with portal vein thrombosis and an increase in portal vein width to achieve recompensation.

Objective:To observe the effects of Maxing Loushi decoction on the inflammatory response and inflammatory indicators in mice with chronic obstructive pulmonary disease (COPD) models.Methods:Thirty-six BALB/C mice were randomly divided into 4 groups by random number table method: 10 mice in the COPD model group (referred to as the model group), 10 mice in the Maxing Loushi decoction group (referred to as the traditional Chinese medicine group), 10 mice in the programmed death receptor-1 (PD-1) inhibitor group (referred to as the control group), and 6 mice in the normal group. The COPD models of mice in the model group, the traditional Chinese medicine group and the control group were prepared by cigarette smoking combined with lipopolysaccharide (LPS) induction method. During the modeling process, the model group and the traditional Chinese medicine group were respectively given normal saline and Maxing Loushi decoction by gavage. The control group was given intraperitoneal injection of PD-1 inhibitor, while the normal group was given intragastric administration of normal saline. Pathological changes of lung tissues in each group of mice were detected by HE staining. The levels of inflammatory factors [monocyte chemotactic protein-1 (MCP-1), macrophage inflammatory protein-1α (MIP-1α), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α)] in the plasma and alveolar lavage fluid (BALF) of mice in each group were determined by enzyme-linked immunosorbent assay (ELISA). The effects of Maxing Loushi decoction intervention on inflammatory responses and inflammatory factors were evaluated.Results:The lung tissue structure in the normal group was basically normal. There was no thickening of the alveolar walls, no infiltration of neutrophils in the tissues, and no obvious inflammatory infiltration. In the model group, the lung tissue structure was slightly abnormal. A small amount of alveolar atrophy could be observed, the alveolar walls were slightly thickened, and inflammatory infiltration could be seen. In the traditional Chinese medicine group, the lung tissue structure was slightly abnormal. A small amount of alveolar atrophy and collapse could be observed, the alveolar walls were not thickened, and individual neutrophil infiltration could be seen in the tissue. In the control group, the lung tissue structure was slightly abnormal, some alveoli atrophied, and a small amount of neutrophil infiltration could be seen in the tissue. The levels of MCP-1 and MIP-1α in plasma and lavage fluid of the model group were significantly higher than those of the normal group (all P<0.05), while the levels of MCP-1 and MIP-1α in plasma and lavage fluid of the traditional Chinese medicine group and the control group were significantly lower than those of the model group (all P<0.05). Moreover, the levels of plasma MCP-1 and MIP-1α in the traditional Chinese medicine group were significantly lower than those in the control group (all P<0.05), while there was no statistically significant difference in the levels of MCP-1 and MIP-1α in alveolar lavage fluid between the traditional Chinese medicine group and the control group (all P>0.05). The levels of IL-6 and TNF-α in plasma and alveolar lavage fluid of the model group were significantly higher than those of the normal group (all P<0.05), while the levels of IL-6 and TNF-α in plasma and alveolar lavage fluid of the traditional Chinese medicine group and the control group were significantly lower than those of the model group (all P<0.05). Moreover, the levels of IL-6 and TNF-α in plasma and alveolar lavage fluid of the traditional Chinese medicine group were significantly lower than those of the control group (all P<0.05). Conclusions:The intervention of Maxing Loushi decoction has a significant improvement effect on the inflammatory response of COPD model mice. Inflammatory factors such as MCP-1, MIP-1α, IL-6, and TNF-α can be used as indicators to determine the degree of COPD inflammation.