Effects of HBeAg Status on cellular immune function of patients with Hepatitis B virus/Treponema Pallidum Co-infectio

A comparison of the therapeutic efficacy of Tenofovir Disoproxil Fumarate and Entecavir in patients with chronic Hepatitis-B

Objective

To compare the therapeutic efficacy of tenofovir disoproxil fumarate (TDF) and entecavir (ETV) in patients with chronic Hepatitis-B (CHB).

Methods

This retrospective study included 110 patients with CHB who received treatment at The First People's Hospital of Linping District, Hangzhou from January 2021 to January 2023. Clinical data of the patients were reviewed and the patients were classified according to the treatment received: TDF group (n=53, patients received TDF treatment) and ETV group (n=57, patients received ETV treatment). Hepatitis-B virus deoxyribonucleic acid (HBV DNA) levels, liver function indicators, hepatitis-B e antigen (HBeAg) seroconversion rate, alanine transaminase (ALT) normalization rate, HBV DNA negative conversion rate, overall efficacy, and incidence of adverse reactions were compared.

Results

The total efficacy of the treatment in the TDF group was 94.33%, significantly higher than that in the ETV group (78.95%; P<0.05). After the treatment, the HBV DNA levels in both groups decreased compared to pretreatment levels, and were significantly lower in the TDF group compared to the ETV group (P<0.05). Both groups showed significant post-treatment improvement in liver function that was markedly better in the TDF group compared to the ETV group (P<0.05). The HBeAg seroconversion rate, ALT normalization rate, and HBV DNA conversion rate in the TDF group were significantly higher compared to the ETV group (P<0.05). There was no difference in the incidence of adverse reactions between the two groups.

Conclusions

Compared with ETV, TDF has comparable adverse reaction profile but has more significant clinical effects in patients with CHB, improving HBeAg seroconversion rate, ALT normalization rate, and HBV DNA negative conversion rate. TDF is associated with lower HBV DNA levels after treatment and better improvements in liver function of patients.

Construction and validation of a predictive model for hepatocellular carcinoma based on serum markers

Background

Early hepatocellular carcinoma (HCC) detection with non-invasive biomarkers remains an unmet clinical need. We aimed to construct a predictive model based on the pre-diagnostic levels of serum markers to predict the early-stage onset of HCC.

Methods

A total of 339 HCC patients (including 157 patients from Changzhou cohort and 182 patients from Wuxi cohort) were enrolled in our retrospective study. Levels of 25 baseline serum markers were collected. Propensity score matching (PSM) analysis was conducted to balance the distributions of patients’ gender, age, and the surveillance time between HCC group and control group. Then, Receiver operating characteristic (ROC) and Logistic regression analysis were performed to screen the independent predictive variables and construct a non-invasive predictive model. Subsequently, ROC curve and Kaplan–Meier (K–M) curve were used to evaluate the predictive values of the model. Clinical net benefit of the model was demonstrated by decision curve analysis (DCA) and clinical impact curve.

Results

Five independent predictive variables for HCC onset and two general characteristics of patients (age and gender) were incorporated into the score model. ROC and DCA curves showed that the score model had better predictive performance in discrimination and clinical net benefit compared with single variable or other score systems, with the area under the curve (AUC) of 0.890 (95% CI 0.856–0.925) in Changzhou cohort and 0.799 (95% CI 0.751–0.849) in Wuxi cohort. Meanwhile, stratification analysis indicated that the score model had good predictive values for patients with early tumor stage (AJCC stage I) or small tumors (< 2 cm). Moreover, the score of HCC patient began to increase at 30 months before clinical diagnosis and reach a peak at 6 months.

Conclusion

Based on this model, we could optimize the current risk stratification at an early stage and consider further intensive surveillance programs for high-risk patients. It could also help clinicians to evaluate the progression and predict the prognosis of HCC patients.