Nomogram for evaluating obvious liver inflammation in treatment-naïve HBeAg positive chronic hepatitis B virus infection patients with normal ALT

Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study

Background

With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS.

Methods

We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449).

Findings

From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model.

Interpretation

Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes.

Funding

This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).

Strategy for immunological analysis of pro-inflammatory cytokine marker studies with chronic hepatitis B virus in Southwestern region of Saudi Arabia

BACKGROUND

Hepatitis B Virus (HBV) is one of the leading causes of infectious disease in the global population, and its prevalence has been increasing globally. Human HBV infection is complex, involving both innate and adaptive immune systems. Cytokines play a role in both physiologic and pathologic processes in the liver. This study was designed to screen serum levels using an enzyme linked immunosorbent assay (ELISA) and genetic variants in the TNF-α and IL6 genes using polymerase chain reactions (PCR). The aim of this study was to screen the serum levels and genotype levels with TNF-α (C-308 T/G-308A) and IL-6 (G-174 C) genes in HBV patients and control subjects.

METHODS

In this study, we have selected 50 HBV patients and 40 control subjects from Saudi Population. Patient serum samples was used for measuring the serum levels and PCR analysis using RFLP analysis. Prior to this, HBV patients were confirmed with PCR analysis followed by Sanger sequencing analysis.

RESULTS

The current study results confirmed positive association in serum levels (p < 0.05) and negative association with both genotype and allele frequencies in TNF-α (C-308 T) and IL-6 (G-174 C) genes among HBV patients and controls (p > 0.05). Positive associations between blood levels of TNF-α and IL-6 were confirmed, while negative associations were found between PCR investigations involving the TNF-α (G-308A) and IL-6 (G-174 C) genes with the HBV prevalence in the Saudi population.

CONCLUSION

This study confirmed serum levels are strongly associated with HBV patients in the Saudi population. However, PCR studies showed the negative association with the couple of variants selected for this study.

Development and Validation of a Nomogram to Predict Significant Liver Inflammation in Patients with Chronic Hepatitis B

Background

Noninvasive diagnosis of liver inflammation is important for patients with chronic hepatitis B (CHB). This study aimed to develop a nomogram to predict significant liver inflammation for CHB patients.

Methods

CHB patients who underwent liver biopsy were retrospectively collected and randomly divided into a development set and a validation set. The least absolute shrinkage and selection operator regression and logistic regression analysis were used to select independent predictors of significant liver inflammation, and a nomogram was developed. The performance of nomogram was assessed by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA).

Results

A total of 1019 CHB patients with a median age of 39.0 years were included. Alanine aminotransaminase (ALT, P = 0.018), gamma-glutamyl transpeptidase (P = 0.013), prothrombin time (P < 0.001), and HBV DNA level (P = 0.030) were identified as independent predictors of significant liver inflammation in the development set. A model namely AGPD-nomogram was developed based on the above parameters. The area under the ROC curve in predicting significant inflammation was 0.765 (95% CI: 0.727-0.803) and 0.766 (95% CI: 0.711-0.821) in the development and validation sets, which were significantly higher than other indexes. The AGPD-nomogram had a high predictive value in patients with normal ALT. Moreover, the nomogram was proven to be clinically useful by DCA.

Conclusion

A visualized AGPD-nomogram which incorporated routine clinical parameters was proposed to facilitate the prediction of significant liver inflammation in CHB patients. This nomogram had high accuracy in the identification of significant liver inflammation and would be a useful tool for the better management of CHB patients, especially for those with normal ALT.

Advances in the Therapeutic Applications of Plant-Derived Exosomes in the Treatment of Inflammatory Diseases

Plant-derived exosomes (PLDEs) are small extracellular vesicles that encapsulate proteins, nucleic acids and lipids, and they are usually involved in intercellular communication and molecular transport in plants. PLDEs are widely used in the therapy of diseases due to their abundance and easy availability. The diverse roles of PLDEs, which include transportation of drugs, acting as biomarkers for diagnosis of diseases and their roles in different therapies, suggest that there is a need to fully understand all the mechanisms involved in order to provide the optimum conditions for their therapeutic use. This review summarizes the biogenesis, components and functions of PLDEs and focuses on their use as therapeutic agents in the treatment of inflammatory diseases. It also explores new ideas for novel approaches in which PLDEs could potentially help patients with inflammatory diseases in the future.