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 construct and validate a predictive model for hepatic encephalopathy （HE） in patients with end-stage liver disease （ESLD） by combining key indicators such as cytokines and Model for End-Stage Liver Disease （MELD） score， and to provide evidence-based support for the early identification of high-risk populations and the optimization of intervention strategies in clinical practice. MethodsA retrospective analysis was performed for 2 167 patients with ESLD who were admitted to The Third People’s Hospital of Kunming from January 2022 to December 2024， the patients were divided into a training set with 1 517 patients and a validation set with 650 patients at a ratio of 7∶3 using the stratified random sampling method. A univariate analysis and a least absolute shrinkage and selection operator （LASSO） regression analysis were performed for the training set to identify potential influencing variables， and then a binary Logistic regression model was constructed to investigate the independent influencing factors for HE in ESLD patients. A nomogram prediction model was established based on this regression model， and the Hosmer-Lemeshow goodness-of-fit test was performed. The receiver operating characteristic （ROC） curve， calibration curves， decision curve analysis， and clinical impact curve were used to comprehensively evaluate the degree of fit， accuracy， consistency， and clinical practicability of the predictive model derived from the training set. The Mann-Whitney U test was used for comparison of non-normally distributed quantitative data between two groups. The chi-square test or Fisher exact test was used for comparison of categorical data between two groups. ResultsThe univariate analysis showed that there were significant differences between the study group and the control group in age， sex， blood ammonia， lymphocytes， hemoglobin， platelet count， prothrombin time， fibrinogen， international normalized ratio， total bilirubin， aspartate aminotransferase， total protein， albumin， prealbumin， alkaline phosphatase， cholinesterase， total bile acid， triglyceride， total cholesterol， high-density lipoprotein， low-density lipoprotein， blood glucose， CD3⁺ T cells， CD4⁺ T cells， CD8⁺ T cells， high-sensitivity C-reactive protein， carcinoembryonic antigen， triiodothyronine， thyroxine， free triiodothyronine， free thyroxine， interleukin-1β， interleukin-5， interleukin-6， interleukin-8， interleukin-12p70， interleukin-17， interferon-γ， and MELD score （all P<0.05）. The LASSO regression analysis identified nine variables of age， blood ammonia， albumin， prealbumin， cholinesterase， total cholesterol， thyroxine， interleukin-17， and MELD score， and the binary Logistic regression analysis showed that blood ammonia， interleukin-17， and MELD score were independent risk factors for HE in ESLD patients， while age and thyroxine were independent protective factors （all P<0.05）. A nomogram model was constructed based on these five variables. The Hosmer-Lemeshow goodness-of-fit test in the training set showed a good degree of fit （P=0.207）， with a McFadden’s pseudo-R² of 0.184， suggesting that the model had acceptable explanatory power； the Hosmer-Lemeshow goodness-of-fit test in the internal validation set further confirmed the calibration stability of the model （P=0.067）， suggesting that the model had a good degree of fit in independent data. The model had an area under the ROC curve of 0.784 in the validation set， with a sensitivity of 0.710 and a specificity of 0.752. The mean absolute error of the calibration curve was 0.067， and decision curve analysis and clinical impact curve showed that the nomogram model had positive net benefit and good clinical practicability. ConclusionThe nomogram model constructed based on age， blood ammonia， thyroxine， interleukin-17， and MELD score for predicting HE in patients with ESLD has a good degree of fit， high accuracy and consistency， and excellent clinical practicability.

Objective: To investigate the characteristics and risk factors of an outbreak of acute hemorrhagic conjunctivitis (AHC) in a boarding middle school in Guangdong Province, in order to provide a scientific evidence for effective prevention and control of campus AHC outbreaks. Methods: From September 1st to 28th 2023, case identification was conducted among 559 students and 60 faculty members using standardized definition. Descriptive analysis was conducted on the three distrubution patterns of the outbreak. Questionnaires were designed, and a case-control study was adopted to analyze the possible risk factors of the disease transmission. The propensity score matching (PSM) method was used to control the difference of baseline data. Results: A total of 269 cases of AHC were identified, with an attack rate of 43.46%. The pathogen was confirmed as Coxsackie virus A24 variant (CA24v). Among these, 264 cases were students (attack rate of 47.23%) and 5 were staff (attack rate of 8.33%). A total of 153 pairs of PSM were successfully matched. After PSM matching, there were no statistically significant differences in gender, grade and class between the case group and the control group ( χ 2=0.12, 5.41, 11.24, P >0.05). The results of multivariate Logistic regression analysis showed that middle school students whose towels contacted with others ( OR =1.81), and direct contact with other AHC cases recently ( OR =4.89) were more likely to have AHC; while wearing glasses ( OR =0.43) and frequent use of hand sanitizer ( OR = 0.37 ) were less likely to have AHC ( P <0.05). Conclusion The outbreak of AHC is caused by CA24v, demonstrating rapid spread and extensive impact within the school setting.

ObjectiveTo investigate the therapeutic efficacy， influencing factors， and safety of a treatment regimen based on coblopasvir hydrochloride capsules/sofosbuvir tablets in patients with chronic hepatitis C virus （HCV） infection in a real-world setting. MethodsA total of 253 patients who attended The Third People’s Hospital of Kunming from September 1， 2021 to May 31， 2024 were enrolled， among whom there were 86 patients with compensated liver cirrhosis （CLC group） and 167 patients with chronic hepatitis C （CHC group）. The patients were treated with coblopasvir hydrochloride capsules （60 mg）/sofosbuvir tablets （400 mg） with or without ribavirin tablets for 12 weeks， and they were followed up for 12 weeks after drug withdrawal. The primary outcome measures were the rate of sustained virologic response at week 12 after treatment （SVR12） and safety， and the secondary outcome measures were the changes in liver function， renal function， blood routine， and liver stiffness measurements （LSM） after 4 weeks of treatment， after 12 weeks of treatment， and at 12 weeks after drug withdrawal. The independent-samples t test and the Mann-Whitney U test were used for comparison of continuous data between two groups， and the Friedman test was used for comparison between multiple groups， while the Bonferroni method was used for paired comparison within each group； the chi-square test was used for comparison of categorical data between two groups. The Logistic analysis was used to investigate related influencing factors. ResultsThe 253 patients with chronic HCV infection had a mean age of 49.38±8.65 years， and there were 151 male patients （59.7%）. Of all patients， 33.99% （86/253） had liver cirrhosis， 25.69% （65/253） had hypertension， 10.67% （27/253） had HIV infection， 8.70% （22/253） had diabetes， 3.95% （10/253） had liver cancer， 1.98% （5/253） had chronic hepatitis B， and 7.91% （20/253） were treatment-experienced patients. As for genotype distribution， 2.77% （7/253） had genotype 1， 12.65% （32/253） had genotype 2， 66.01% （167/253） had genotype 3， 16.60% （42/253） had genotype 6， and 1.98% （5/253） had unknown genotype. The patients had an overall SVR12 rate of 92.09%， with an SVR12 rate of 93.02% in the CLC group and 91.02% in the CHC group. The multivariate logistic regression analysis showed that age （odds ratio ［OR］=1.086， 95% confidence interval ［CI］： 1.007 — 1.170， P=0.032） and HCC （OR=9.178， 95%CI： 1.722 — 48.912， P=0.009） were independent influencing factors for sustained virologic response. Compared with baseline data， the CLC group had significant reductions in alanine aminotransferase （ALT） （χ²=107.103， P0.05）， aspartate aminotransferase （AST） （χ²=90.602， P0.05）， and LSM （χ²=42.235， P0.05） after 12 weeks of treatment， while the CHC group had significant reductions in total bilirubin （χ²=15.113， P0.05）， ALT （χ²=202.237， P0.05）， AST （χ²=161.193， P0.05）， and LSM （χ²=37.606， P0.05）. The incidence rate of serious adverse events was 1.58%， and none of the patients withdrew from drug therapy； the patients with such events were relieved after active symptomatic treatment. The incidence rate of all adverse events was 23.72%， among which fatigue （17.39%） and nausea （2.37%） were the most common adverse events， and these events often disappeared within 2 weeks or were gradually relieved after symptomatic treatment. ConclusionCoblopasvir hydrochloride capsules/sofosbuvir tablets with or without ribavirin tablets has good efficacy and safety in the treatment of chronic HCV infection.

Objective:To explore the mediating role of anxiety/depression emotions between sleep quality and cognitive function in adolescents with attention deficit hyperactivity disorder(ADHD).Methods:A cross-sectional study design was adopted, involving 204 ADHD adolescents aged 12-18 who were treated between January 2021 and November 2023.All participants were assessed using the Pittsburgh sleep quality index(PSQI)for sleep quality, the self-rating anxiety scale(SAS) and self-rating depression scale(SDS) for emotional states.Four cognitive information processing processes(planning, simultaneous processing, attention, and successive processing) were evaluated by the Das-Naglieri cognitive assessment system(DN: CAS). Data were statistically analyzed using SPSS 23.0 and Zstats software, including descriptive statistics, Spearman correlation analysis, and mediation effect analysis.Results:Among ADHD adolescents, girls exhibited significantly higher rates of sleep disturbance(71.59%(63/88) vs 43.97%(51/116), χ2=15.490, P<0.001)and depressive emotion(47.73%(42/88) vs 33.62%(39/116), χ2=4.159, P=0.041)compared to boys.High school students had a significantly higher rate of sleep disturbance than middle school students(64.84%(59/91) vs 48.67%(55/113), χ2=5.341, P=0.021). Spearman correlation analysis revealed that sleep quality(8.00(6.00, 11.00)) in ADHD adolescents was significantly and positively correlated with anxiety(38.00(32.00, 46.00); r=0.504, P<0.01) and depression(46.00(39.00, 54.00); r=0.427, P<0.01). And sleep quality, anxiety and depression were significantly and negatively correlated with the DN: CAS total score(109.5(91.25, 123.75); r=-0.158--0.237, P<0.05). Mediation analysis indicated that anxiety mediated the relationship between sleep quality and attention function(indirect effect β=-0.159, Bootstrap 95% CI=-0.287--0.046). Conclusion:ADHD adolescents exhibit complex interactions among sleep quality, emotion, and cognitive function, sleep quality indirectly affects attention function through anxiety.

Objective:To explore the mediating role of anxiety/depression emotions between sleep quality and cognitive function in adolescents with attention deficit hyperactivity disorder(ADHD).Methods:A cross-sectional study design was adopted, involving 204 ADHD adolescents aged 12-18 who were treated between January 2021 and November 2023.All participants were assessed using the Pittsburgh sleep quality index(PSQI)for sleep quality, the self-rating anxiety scale(SAS) and self-rating depression scale(SDS) for emotional states.Four cognitive information processing processes(planning, simultaneous processing, attention, and successive processing) were evaluated by the Das-Naglieri cognitive assessment system(DN: CAS). Data were statistically analyzed using SPSS 23.0 and Zstats software, including descriptive statistics, Spearman correlation analysis, and mediation effect analysis.Results:Among ADHD adolescents, girls exhibited significantly higher rates of sleep disturbance(71.59%(63/88) vs 43.97%(51/116), χ2=15.490, P<0.001)and depressive emotion(47.73%(42/88) vs 33.62%(39/116), χ2=4.159, P=0.041)compared to boys.High school students had a significantly higher rate of sleep disturbance than middle school students(64.84%(59/91) vs 48.67%(55/113), χ2=5.341, P=0.021). Spearman correlation analysis revealed that sleep quality(8.00(6.00, 11.00)) in ADHD adolescents was significantly and positively correlated with anxiety(38.00(32.00, 46.00); r=0.504, P<0.01) and depression(46.00(39.00, 54.00); r=0.427, P<0.01). And sleep quality, anxiety and depression were significantly and negatively correlated with the DN: CAS total score(109.5(91.25, 123.75); r=-0.158--0.237, P<0.05). Mediation analysis indicated that anxiety mediated the relationship between sleep quality and attention function(indirect effect β=-0.159, Bootstrap 95% CI=-0.287--0.046). Conclusion:ADHD adolescents exhibit complex interactions among sleep quality, emotion, and cognitive function, sleep quality indirectly affects attention function through anxiety.

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.

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 review the application and progress of unilateral biportal endoscopy (UBE) technology in the treatment of cervical degenerative diseases, and to provide reference for clinical treatment decisions. METHODS: The literature related to UBE technology in the treatment of cervical spondylotic radiculopathy (CSR) and cervical spondylotic myelopathy (CSM) at home and abroad was extensively reviewed, and the surgical methods, indications, effectiveness, and safety were analyzed and summarized. RESULTS: UBE technology is effective in the treatment of CSR and CSM, and has the advantages of good surgical field, reducing the injury of the posterior structure of the cervical spine, and protecting the facet joint process, but in general, the indications are relatively narrow, limited to single-segment or adjacent double-segment lesions, and the requirements for the operator are relatively high, and the learning curve is long. CONCLUSION UBE technology can be applied to the treatment of CSR and CSM, but it needs to be carried out by experienced UBE surgeons for specific cases.
Humans ; Cervical Vertebrae/surgery* ; Endoscopy/methods* ; Radiculopathy/surgery* ; Spondylosis/surgery* ; Decompression, Surgical/methods* ; Spinal Cord Diseases/surgery* ; Treatment Outcome