Objective: To establish a real-time quality control scheme based on the median (MD-IC) of internal control cycle threshold value in negative samples (NEG-IC-CT), so as to monitor anomalies such as progressive drift in nucleic acid testing system not covered by conventional internal quality control (IQC) in blood center nucleic acid laboratories, and to verify its feasibility. Methods: The internal control CT values of 54 426 negative samples were retrospectively collected. These samples were from four reagent batches of the two new and old equipment sets during the operation of the Wantai nucleic acid testing system in our blood center. The daily median of NEG-IC-CT values was used as the research indicator. Control limits were calculated using median absolute deviation (MAD) to construct the Median-MAD quality control chart. The monitoring performance of this scheme for the operation status of the testing system was simultaneously evaluated. Results: Statistical analysis showed significant differences in NEG-IC-CT value distribution between the new and old equipment sets, as well as between the two different reagent batches of the old equipment (P<0.000 1). The NEG-IC-CT value performance of the two different reagent batches of the new equipment was no significant difference in distribution (P>0.05). This scheme identified three typies of distinct anomalies. The out-of-control events observed with the old equipment in both the O1 and O2 reagent batches suggested potential performance decay due to equipment aging. The unreported change of reagent batch in time of Phase B with new equipment caused a stepwise drift on the quality control chart. In the later stage of Phase A with the new equipment, an alert was triggered, indicating potential quality risks associated with practices such as the mixed use of the remaining reagents and extremely long operator working hours. Conclusion: The realtime quality control scheme based on NEG-IC-CT value established in this study has been preliminarily validated for its monitoring effectiveness in nucleic acid testing in our blood center. This scheme performed well in detecting differences among testing systems and reagent batches, serving as an effective supplement to routine internal quality control. It can provide an intuitive and effective evaluation method for monitoring the performance of the nucleic acid testing process at blood center.

ObjectiveTo investigate the influencing factors for recompensation in patients with decompensated liver cirrhosis， as well as the impact of recompensation on the prognosis of such patients， and to provide a basis for early identification of high-risk patients in clinical practice. MethodsA retrospective analysis was performed for the clinical data of patients who attended The Third People’s Hospital of Kunming from January 2016 to December 2022 and were diagnosed with decompensated liver cirrhosis due to hepatitis B， hepatitis C， alcoholic hepatitis， and autoimmune hepatitis， and they were divided into recompensation group and persistent decompensation group. To control for confounding factors， whether recompensation occurred was used as the rouping variable，and BMI， alcohol consumption history， HIV infection history， TG， CHOL， LDL， and HDL were used as covariates. The propensity score was calculated， and 1：1 nearest neighbor matching was performed with a caliper value of 0.1. After propensity score matching， the recompensation group and the persistent decompensation group with relatively balanced covariates were obtained. Univariate and multivariate Cox proportional-hazards regression model analyses were used to investigate the influencing factors for recompensation； the “rms” package was used to establish a nomogram； the receiver operating characteristic （ROC） curve was plotted to calculate the area under the ROC curve （AUC）； the Hosmer-Lemeshow test was used to assess the goodness of fit of the model； the “Calibration Curves” package was used to plot calibration curves for model assessment. The Kaplan-Meier method was used to plot survival curves， and the Log-rank test was used for comparison of survival curves. ResultsAmong the 863 patients with decompensated liver cirrhosis， 305 experienced recompensation， resulting in an incidence rate of 35.3%. After PSM， 610 cases were successfully matched， with 305 cases in each group. The univariate and multivariate Cox regression analyses showed that etiology （hepatitis C： hazard ratio［HR］=0.288， P=0.002）； male（HR=0.701， P=0.016）， age（HR=0.988， P=0.047）， hemoglobin （HGB）（HR=1.006， P=0.017）， and CD4 T cell（HR=1.001，P=0.047）， TIPS procedure （HR=1.808，P=0.042） were independent influencing factors for recompensation in patients with decompensated liver cirrhosis. During follow-up， 116 patients died of liver disease-related causes， with 27 patients （8.85%） in the recompensation group and 89 （15.95%） in the persistent decompensation group； 109 patients developed HCC， with 23 patients （7.54%） in the recompensation group and 86 （15.41%） in the persistent decompensation group. The Kaplan-Meier survival curves showed significant separation between the patients with different states of compensation in terms of liver disease-related mortality rate and the incidence rate of HCC， and the Log-rank test showed that there were significant differences between the two groups in liver disease-related mortality rate （χ²=9.023， P=0.003） and the incidence rate of HCC （χ²=10.526， P=0.001）. ConclusionEtiology，sex，age，TIPS，HGB，and CD4 T cell are independent influencing factors for recompensation in patients with decompensated liver cirrhosis. There is a significant difference in the incidence rate of recompensation between decompensated liver cirrhosis patients with different etiologies， and female patients and patients with a younger age，a history of TIPS， a higher HGB level， and a higher CD4 lymphocyte count are more likely to experience recompensation. Recompensation is the key to improving the long-term prognosis of patients and can significantly reduce long-term liver disease-related mortality rate and the incidence rate of HCC.

BACKGROUND:Promoting skin wound healing is a huge challenge facing global public health.To promote faster and higher-quality wound healing,it is necessary to explore more advantageous dressings to address this problem.OBJECTIVE:To investigate the hemostatic properties of acellular dermal matrix hydrogel and its effect on skin wound healing.METHODS:(1)Acellular dermal matrix hydrogel was prepared,and the differences in microscopic morphology and main components between it and acellular dermal matrix were analyzed.(2)Acellular dermal matrix hydrogel and chitosan hydrogel were used to cover the femoral artery puncture site of rats,and the bleeding quality and coagulation time were recorded.Acellular dermal matrix hydrogel and chitosan hydrogel were mixed with rat anticoagulated blood,and the coagulation index within 30 minutes was detected.(3)A full-thickness skin defect model with a diameter of 12 mm was made on the back of 18 SD rats,and they were randomly divided into 3 groups,with 6 rats in each group:the model group used PBS to clean the wound,and the control group and the experimental group used chitosan hydrogel and acellular dermal matrix hydrogel to cover the wound,respectively.The hydrogel dressing was changed every day,and the treatment was continued for 14 days,and the wound healing was observed.On day 3 after modeling,immunofluorescence staining of inducible nitric oxide synthase(M1 macrophages)and CD206(M2 macrophages)was performed on the wound surface.On day 14 after modeling,hematoxylin-eosin staining,Masson staining,and CD31 immunohistochemical staining were performed on the wound surface.RESULTS AND CONCLUSION:(1)Scanning electron microscopy revealed that the acellular dermal matrix hydrogel had a porous structure,and the Fourier transform infrared spectrum showed that it had the same main components as the acellular dermal matrix.(2)Both acellular dermal matrix hydrogel and chitosan hydrogel had obvious hemostatic ability in vivo.In the in vitro coagulation experiments,the coagulation index of acellular dermal matrix hydrogel was significantly higher than that of chitosan hydrogel.(3)In the rat skin full-thickness defect model,both acellular dermal matrix hydrogel and chitosan hydrogel could improve the wound healing rate.Hematoxylin-eosin and Masson staining results showed that acellular dermal matrix hydrogel could reduce the infiltration of inflammatory cells in the center of the wound.Both acellular dermal matrix hydrogel and chitosan hydrogel could decrease scar width and increase collagen deposition rate.CD31 immunohistochemical staining results showed that both hydrogels could promote angiogenesis in the wound site.Immunofluorescence staining results showed that both hydrogels could reduce the proportion of M1 macrophages and increase the proportion of M2 macrophages,and the effect of acellular dermal matrix hydrogel was stronger than that of chitosan hydrogel.(4)The results show that the acellular dermal matrix hydrogel has good hemostatic properties and the ability to promote wound healing.

BACKGROUND:Promoting skin wound healing is a huge challenge facing global public health.To promote faster and higher-quality wound healing,it is necessary to explore more advantageous dressings to address this problem.OBJECTIVE:To investigate the hemostatic properties of acellular dermal matrix hydrogel and its effect on skin wound healing.METHODS:(1)Acellular dermal matrix hydrogel was prepared,and the differences in microscopic morphology and main components between it and acellular dermal matrix were analyzed.(2)Acellular dermal matrix hydrogel and chitosan hydrogel were used to cover the femoral artery puncture site of rats,and the bleeding quality and coagulation time were recorded.Acellular dermal matrix hydrogel and chitosan hydrogel were mixed with rat anticoagulated blood,and the coagulation index within 30 minutes was detected.(3)A full-thickness skin defect model with a diameter of 12 mm was made on the back of 18 SD rats,and they were randomly divided into 3 groups,with 6 rats in each group:the model group used PBS to clean the wound,and the control group and the experimental group used chitosan hydrogel and acellular dermal matrix hydrogel to cover the wound,respectively.The hydrogel dressing was changed every day,and the treatment was continued for 14 days,and the wound healing was observed.On day 3 after modeling,immunofluorescence staining of inducible nitric oxide synthase(M1 macrophages)and CD206(M2 macrophages)was performed on the wound surface.On day 14 after modeling,hematoxylin-eosin staining,Masson staining,and CD31 immunohistochemical staining were performed on the wound surface.RESULTS AND CONCLUSION:(1)Scanning electron microscopy revealed that the acellular dermal matrix hydrogel had a porous structure,and the Fourier transform infrared spectrum showed that it had the same main components as the acellular dermal matrix.(2)Both acellular dermal matrix hydrogel and chitosan hydrogel had obvious hemostatic ability in vivo.In the in vitro coagulation experiments,the coagulation index of acellular dermal matrix hydrogel was significantly higher than that of chitosan hydrogel.(3)In the rat skin full-thickness defect model,both acellular dermal matrix hydrogel and chitosan hydrogel could improve the wound healing rate.Hematoxylin-eosin and Masson staining results showed that acellular dermal matrix hydrogel could reduce the infiltration of inflammatory cells in the center of the wound.Both acellular dermal matrix hydrogel and chitosan hydrogel could decrease scar width and increase collagen deposition rate.CD31 immunohistochemical staining results showed that both hydrogels could promote angiogenesis in the wound site.Immunofluorescence staining results showed that both hydrogels could reduce the proportion of M1 macrophages and increase the proportion of M2 macrophages,and the effect of acellular dermal matrix hydrogel was stronger than that of chitosan hydrogel.(4)The results show that the acellular dermal matrix hydrogel has good hemostatic properties and the ability to promote wound healing.

Lung cancer， with persistently high incidence and mortality rates， remains a significant global health challenge. By taking the study on the experience in diagnosis and treatment of lung cancer with phlegm-dampness and blood stasis syndrome by distinguished traditional Chinese medicine physicians of the Sichuan School as an example， the diagnosis and treatment system for lung cancer with phlegm-dampness and blood stasis syndrome， which was formed in response to the humid and foggy environment of the Sichuan Basin， possesses unique value. However， traditional inheritance modes face challenges such as fragmentation， lack of standardization， and insufficient quantification， which hinder the promotion and application of this experience. This research focused on how to leverage multimodal data and artificial intelligence-generated content （AIGC） to achieve precise analysis， intelligent inheritance， and clinical innovation of the experience in diagnosis and treatment of lung cancer with phlegm-dampness and blood stasis syndrome by distinguished traditional Chinese medicine physicians of the Sichuan School. By integrating multimodal data （encompassing four diagnostic methods of traditional Chinese medicine， modern medical imaging， clinical laboratory tests， molecular biology， and regional environmental information）， a precise diagnosis and treatment system integrating macro and micro perspectives for the "disease， syndrome， and pathogenesis" was constructed. The research yielded the following results： （1） In precise syndrome differentiation， the objective quantification of the phlegm-dampness and blood stasis syndrome was achieved. By constructing a "four diagnostic methods， imaging， and molecule" correlation model， the study revealed intrinsic links between tongue and pulse parameters and the tumor microenvironment， as well as between regional climatic factors and syndrome characteristics， enabling real-time dynamic monitoring of efficacy. （2） In elucidating patterns， the study systematically explored the syndrome differentiation thoughts of Sichuan School physicians， such as the timing of purgation and tonification. A "pathogenesis， syndrome complex， and prescriptions and herb" network model was constructed， which accurately elucidated the synergistic action mechanisms of core herb pairs and quantified the dynamic compatibility patterns of reinforcing healthy Qi and eliminating pathogenic factors. （3） In intelligent empowerment， an auxiliary system integrating intelligent syndrome differentiation， treatment plan generation， and efficacy evaluation was built. This system can fuse regional characteristics with individual data， dynamically generate and optimize personalized prescriptions aligned with the experience of Sichuan School， and predict efficacy trends and potential adverse reactions. The integration of multimodal data and AIGC can effectively facilitate the structured inheritance and clinical translation of distinguished physicians' experience. The established intelligent diagnosis and treatment model integrating traditional Chinese medicine and Western medicine demonstrates clear potential in prolonging patients' progression-free survival， alleviating symptoms， and reducing adverse reactions to treatment. This study provides a referential methodological framework for the traditional Chinese medicine experience in diagnosis and treatment of lung cancer， especially the empirical inheritance and modernized development of regional academic schools. It contributes to advancing clinical diagnosis and treatment toward greater precision and personalization.

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.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

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.