Laboratory medicine plays a crucial role in the diagnosis of hepatitis B virus (HBV) infection, primarily through the detection of HBV markers and diagnostic markers of liver disease progression. HBV markers can be categorized into classical and novel types. Diagnostic markers of liver disease progression related to HBV infection are mainly associated with hepatitis, liver fibrosis, and liver cancer. Currently, there are unresolved issues in laboratory medicine regarding the management of chronic HBV infection. Addressing these issues is essential to achieve the goal of "eliminating hepatitis B related public health threat in our country". This article summarizes the research progress and clinical applications of HBV markers and liver disease progression diagnostic markers (the "strengths" of laboratory medicine), while also contemplates the new requirements posed by the elimination of hepatitis B on laboratory medicine (the "limitations"). A commentary on the "strengths" and "limitations" of laboratory medicine in the context of hepatitis B elimination is provided for reference by clinicians and laboratory professionals.

Objective:To develop and validate a machine learning (ML) noninvasive model based on routine laboratory parameters to preoperatively predict the microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).Methods:A total of 629 HCC patients who underwent hepatectomy at the First Affiliated Hospital of Fujian Medical University between January 2019 and December 2023 were retrospectively enrolled in this study and were divided chronologically into a training set ( n=464) and internal validation set ( n=165). A cohort with 190 HCC patients from Fujian Provincial Hospital were used as an external validation set. Preoperatively demographic features, tumor size and routine laboratory data were collected. All patients were divided into MVI-positive or MVI-negative group. The Boruta algorithm and LASSO regression algorithm were used to screen out related features in the training set. Eight different ML algorithms including multivariate logistic regression, decision tree (DT), random forest (RF), extreme gradient boosting (XGboost), k-nearest neighbor (KNN), support vector machine (SVM), light gradient boosting machine (LGBM) and Naive Bayes were used to construct the prediction models. The predictive performances of these models on training and internal validation sets were evaluated by the receiver operating characteristic (ROC) curve with the area under the curve (AUC). The ML model with the highest AUC values was defined as the optimal model and its performance was further validated in the external validation set. The calibration curve showed that the probability value curve was close to the actual occurrence probability curve, and the DCA showed that it could be applied within the threshold probability range of 0.3-0.8 to obtain net benefits. Results:After screening, eight parameters including α-fetoprotein (AFP), protein induced by vitamin K absence Ⅱ (PIVKA-Ⅱ), tumor size, eosinophil count, neutrophil count, creatinine, ApoA1 and total bilirubin were finally selected for the construction of the preoperative prediction model for MVI in HCC. Among all the tested eight ML algorithms, XGboost obtained the optimal performance with an AUC of 0.820 in training set, an AUC of 0.803 in internal testing set and an AUC of 0.758 in external testing set. Further stratified analysis showed that the AUC for preoperatively predicting MVI by XGboost was 0.817 for HCC patients with positive hepatitis B surface antigen, 0.779 for male patients and 0.790 for elder patients. The calibration curves showed good agreement between observed and predicted values and the decision curve analysis curve showed relatively higher net benefits.Conclusions:We successfully established and verified a novel XGboost model based on eight routine laboratory parameters with relatively high and reliable predictive accuracy to preoperatively predict MVI in HCC.

Objective:To establish a microfluidic chip-based digital PCR (cdPCR) method for detecting hepatitis B virus (HBV) RNA and evaluate its application in patients with chronic HBV infection.Methods:A total of 135 patients with chronic HBV infection were recruited from the First Affiliated Hospital of Fujian Medical University and stratified into two groups based on HBV DNA levels: HBV DNA>100 IU/ml ( n=85) and HBV DNA≤100 IU/ml ( n=50). Additionally, healthy individuals and subjects infected with other viruses ( n=15) served as controls. Primers and probes targeting the HBV pre-C/C region were designed to optimize the microfluidic cdPCR method, and its linear range, limit of detection (LOD), specificity, and precision were assessed. Serum HBV RNA levels were measured using the self-developed method and two commercial kits. Pearson correlation was applied to evaluate the relationships between HBV RNA and other HBV markers. Results:The optimized microfluidic cdPCR method featured a denaturation time of 10 seconds, an annealing/extension temperature of 62 ℃, and primer and probe concentrations of 0.3 μmol/L and 0.2 μmol/L, respectively. The method demonstrated a linear range of 103-10? copies/ml and an LOD of 102 copies/ml. The intra-assay coefficient of variation ( CV) for plasmid standards at 10? and 10? copies/ml were 1.06% and 0.82%, respectively, while the inter-assay CVs were 0.75% and 0.44%. Specificity analysis confirmed the absence of positive signals in sera from healthy controls and subjects infected with other pathogens. In the HBV DNA>100 IU/ml group, the detection rate of the self-developed cdPCR method was 81.18% (69/85), significantly higher than the 64.71% (55/85) achieved by commercial kit B ( P<0.016 7). However, in the HBV DNA≤100 IU/ml group, no significant differences were observed among the three methods ( P>0.05). HBV RNA levels were positively correlated with HBV DNA ( r=0.67), hepatitis B surface antigen ( r=0.53), and hepatitis B e antigen ( r=0.44) (all P<0.001). Conclusion:A microfluidic cdPCR assay for the quantitative detection of HBV RNA has been successfully developed. This assay demonstrates high sensitivity, specificity, and robust detection capability, even for low HBV DNA-concentration samples.

Liver macrophages are crucial components of innate immune system in liver and play a pivotal role in chronic hepatitis B virus (HBV) infection. Influenced by the liver microenvironment, liver macrophages can exhibit diverse immunophenotypes and functions, which contribute to either inhibiting HBV infection or mediating immune tolerance. Additionally, HBV actively regulates the phenotype of liver macrophages, thereby facilitating continuous viral infection. Given that the interaction between liver macrophages and HBV might directly impact the outcome of HBV infection, the huge potential clinical value of targeting macrophage markers, this review highlights the research progress on liver macrophages in HBV infection from three aspects: the origin and heterogeneity of liver macrophages, the interaction between liver macrophages and HBV, and the potential serological immune markers associated with macrophages in HBV infection.

Objective:To investigate the laboratory diagnostic value of 5 new blood routine indexes in chronic hepatitis B (CHB), liver cirrhosis and hepatocellular carcinoma (HCC).Methods:The retrospective study included 65 patients with chronic HBV infection, 72 patients with liver cirrhosis and 163 patients with HCC recruited at Liver Disease Center in First Affiliated Hospital of Fujian Medical University, as well as 52 healthy controls recruited from the Physical Examination Center of the First Affiliated Hospital of Fujian Medical University from October 2022 to April 2023. Five new parameters [early granulated cell percent (EGC%), early granulated cell absolute count (EGC#), microcytic anemia factor (MAF), leukocyte estimate(corrected)from the DIFF optical channel (WDOP) and leukocyte estimate(corrected)from the NRBC optical channel (WNOP)] were detected by UniCel DxH 900 blood cell analyzer. Univariate analysis of the expression levels of the 5 new parameterswere compared among CHB, liver cirrhosis and HCC groups. Pearson correlation analysis was used to explore the correlation between the 5 new parameters and HBV-related markers in CHB and Child-Pugh score in liver cirrhosis. Receiver operating characteristic (ROC) curve and AUC were used to estimate the diagnostic capacity of the 5 new blood routine indexes in CHB, liver cirrhosis and HCC.Results:In patients with CHB, the levels of EGC% ( Z=4.613, P<0.001) and EGC# ( Z=4.220, P<0.001) were higher than those of healthy controls; EGC# was positively correlated with HBsAg and HBeAg (both P<0.05). In patients with cirrhosis, the level of MAF ( Z=-4.928, P<0.001) was lower than that of healthy controls, and Child-Pugh score was found to be negatively correlated with MAF ( r=-0.349, P<0.05). In HCC patients, WDOP ( Z=2.45, P=0.017) and WNOP ( Z=2.90, P=0.017) levels were higher in patients with tumor volume>3 cm 3 than those in patients with volume ≤3 cm 3. The AUCs of combination of 5 new parameters to diagnose CHB, liver cirrhosis and HCC were 0.901 (95% CI 0.830-0.973, P<0.001), 0.946 (95% CI 0.909-0.984, P<0.001), and 0.904 (95% CI 0.858-0.950, P<0.001). Conclusions:The 5 new parameters based on peripheral blood cell analysis have good clinical value in the diagnosis of CHB, liver cirrhosis and HCC diseases.

Objective:To investigate the effect of growth stimulation expressed gene 2(ST2)on intestinal microflora of mice with ConA-induced autoimmune hepatitis(AIH)by knockout.Methods:ST2 gene knockout mice were constructed by gene knockout technique.On this basis,AIH mouse model was established by ConA induction.The relative expression of cytokines was detected by real-time fluorescence quantitative PCR(RT-PCR).Intestinal microorganisms were sequenced using the 16S rRNA high-through put sequencing method,and the sequencing results were analyzed using Microbial Ecology related software and database.Results:Com-pared with the control group,ALT and AST were decreased,the damage of liver was mild,and the relative expression of IL-6 was also decreased in the liver of mice with ConA-induced AIH after ST2 gene knockout.There was no significant difference in the ACE and CHAO indices of the abundance of reactive flora.There was no significant difference between Shannon index and Simpson index re-flecting bacterial diversity.Psychrobacter,unclassified_Clostridia,Halothiobacillus,Clostridium_XlVa and Haemophilus genus content increased;Romboutsia,Rikenella,Parabacteroides and unclassified_Clostridiales bacterial content reduce.The areas under the receiv-er operator characteristic(ROC)curves were 0.88,0.89,0.88,0.80,0.90,0.90,0.84,0.91 and 0.84,respectively.Conclusion:Knockout of ST2 gene expression in mice with AIH can reduce liver injury and inflammation,and does not affect the distribution abun-dance and diversity of intestinal flora.However,there are differences among bacteria genera,and it has good diagnostic value.

Objective:To investigate the effect of growth stimulation expressed gene 2(ST2)on intestinal microflora of mice with ConA-induced autoimmune hepatitis(AIH)by knockout.Methods:ST2 gene knockout mice were constructed by gene knockout technique.On this basis,AIH mouse model was established by ConA induction.The relative expression of cytokines was detected by real-time fluorescence quantitative PCR(RT-PCR).Intestinal microorganisms were sequenced using the 16S rRNA high-through put sequencing method,and the sequencing results were analyzed using Microbial Ecology related software and database.Results:Com-pared with the control group,ALT and AST were decreased,the damage of liver was mild,and the relative expression of IL-6 was also decreased in the liver of mice with ConA-induced AIH after ST2 gene knockout.There was no significant difference in the ACE and CHAO indices of the abundance of reactive flora.There was no significant difference between Shannon index and Simpson index re-flecting bacterial diversity.Psychrobacter,unclassified_Clostridia,Halothiobacillus,Clostridium_XlVa and Haemophilus genus content increased;Romboutsia,Rikenella,Parabacteroides and unclassified_Clostridiales bacterial content reduce.The areas under the receiv-er operator characteristic(ROC)curves were 0.88,0.89,0.88,0.80,0.90,0.90,0.84,0.91 and 0.84,respectively.Conclusion:Knockout of ST2 gene expression in mice with AIH can reduce liver injury and inflammation,and does not affect the distribution abun-dance and diversity of intestinal flora.However,there are differences among bacteria genera,and it has good diagnostic value.

Laboratory medicine plays a crucial role in the diagnosis of hepatitis B virus (HBV) infection, primarily through the detection of HBV markers and diagnostic markers of liver disease progression. HBV markers can be categorized into classical and novel types. Diagnostic markers of liver disease progression related to HBV infection are mainly associated with hepatitis, liver fibrosis, and liver cancer. Currently, there are unresolved issues in laboratory medicine regarding the management of chronic HBV infection. Addressing these issues is essential to achieve the goal of "eliminating hepatitis B related public health threat in our country". This article summarizes the research progress and clinical applications of HBV markers and liver disease progression diagnostic markers (the "strengths" of laboratory medicine), while also contemplates the new requirements posed by the elimination of hepatitis B on laboratory medicine (the "limitations"). A commentary on the "strengths" and "limitations" of laboratory medicine in the context of hepatitis B elimination is provided for reference by clinicians and laboratory professionals.

Objective:To develop and validate a machine learning (ML) noninvasive model based on routine laboratory parameters to preoperatively predict the microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).Methods:A total of 629 HCC patients who underwent hepatectomy at the First Affiliated Hospital of Fujian Medical University between January 2019 and December 2023 were retrospectively enrolled in this study and were divided chronologically into a training set ( n=464) and internal validation set ( n=165). A cohort with 190 HCC patients from Fujian Provincial Hospital were used as an external validation set. Preoperatively demographic features, tumor size and routine laboratory data were collected. All patients were divided into MVI-positive or MVI-negative group. The Boruta algorithm and LASSO regression algorithm were used to screen out related features in the training set. Eight different ML algorithms including multivariate logistic regression, decision tree (DT), random forest (RF), extreme gradient boosting (XGboost), k-nearest neighbor (KNN), support vector machine (SVM), light gradient boosting machine (LGBM) and Naive Bayes were used to construct the prediction models. The predictive performances of these models on training and internal validation sets were evaluated by the receiver operating characteristic (ROC) curve with the area under the curve (AUC). The ML model with the highest AUC values was defined as the optimal model and its performance was further validated in the external validation set. The calibration curve showed that the probability value curve was close to the actual occurrence probability curve, and the DCA showed that it could be applied within the threshold probability range of 0.3-0.8 to obtain net benefits. Results:After screening, eight parameters including α-fetoprotein (AFP), protein induced by vitamin K absence Ⅱ (PIVKA-Ⅱ), tumor size, eosinophil count, neutrophil count, creatinine, ApoA1 and total bilirubin were finally selected for the construction of the preoperative prediction model for MVI in HCC. Among all the tested eight ML algorithms, XGboost obtained the optimal performance with an AUC of 0.820 in training set, an AUC of 0.803 in internal testing set and an AUC of 0.758 in external testing set. Further stratified analysis showed that the AUC for preoperatively predicting MVI by XGboost was 0.817 for HCC patients with positive hepatitis B surface antigen, 0.779 for male patients and 0.790 for elder patients. The calibration curves showed good agreement between observed and predicted values and the decision curve analysis curve showed relatively higher net benefits.Conclusions:We successfully established and verified a novel XGboost model based on eight routine laboratory parameters with relatively high and reliable predictive accuracy to preoperatively predict MVI in HCC.

Objective:To establish a microfluidic chip-based digital PCR (cdPCR) method for detecting hepatitis B virus (HBV) RNA and evaluate its application in patients with chronic HBV infection.Methods:A total of 135 patients with chronic HBV infection were recruited from the First Affiliated Hospital of Fujian Medical University and stratified into two groups based on HBV DNA levels: HBV DNA>100 IU/ml ( n=85) and HBV DNA≤100 IU/ml ( n=50). Additionally, healthy individuals and subjects infected with other viruses ( n=15) served as controls. Primers and probes targeting the HBV pre-C/C region were designed to optimize the microfluidic cdPCR method, and its linear range, limit of detection (LOD), specificity, and precision were assessed. Serum HBV RNA levels were measured using the self-developed method and two commercial kits. Pearson correlation was applied to evaluate the relationships between HBV RNA and other HBV markers. Results:The optimized microfluidic cdPCR method featured a denaturation time of 10 seconds, an annealing/extension temperature of 62 ℃, and primer and probe concentrations of 0.3 μmol/L and 0.2 μmol/L, respectively. The method demonstrated a linear range of 103-10? copies/ml and an LOD of 102 copies/ml. The intra-assay coefficient of variation ( CV) for plasmid standards at 10? and 10? copies/ml were 1.06% and 0.82%, respectively, while the inter-assay CVs were 0.75% and 0.44%. Specificity analysis confirmed the absence of positive signals in sera from healthy controls and subjects infected with other pathogens. In the HBV DNA>100 IU/ml group, the detection rate of the self-developed cdPCR method was 81.18% (69/85), significantly higher than the 64.71% (55/85) achieved by commercial kit B ( P<0.016 7). However, in the HBV DNA≤100 IU/ml group, no significant differences were observed among the three methods ( P>0.05). HBV RNA levels were positively correlated with HBV DNA ( r=0.67), hepatitis B surface antigen ( r=0.53), and hepatitis B e antigen ( r=0.44) (all P<0.001). Conclusion:A microfluidic cdPCR assay for the quantitative detection of HBV RNA has been successfully developed. This assay demonstrates high sensitivity, specificity, and robust detection capability, even for low HBV DNA-concentration samples.