No abstract

Hepatitis B virus (HBV) infection has persisted as a major public health problem due to the lack of an effective treatment for those chronically infected. Therapeutic vaccination holds promise, and targeting HBV polymerase is pivotal for viral eradication. In this research, a computational approach was employed to predict suitable HBV polymerase targeting multi-peptides for vaccine candidate selection. We then performed in-depth computational analysis to evaluate the predicted epitopes’ immunogenicity, conservation, population coverage, and toxicity. Lastly, molecular docking and MHC-peptide complex stabilization assay were utilized to determine the binding energy and affinity of epitopes to the HLA-A0201 molecule. Criteria-based analysis provided four predicted epitopes, RVTGGVFLV, VSIPWTHKV, YMDDVVLGA and HLYSHPIIL. Assay results indicated the lowest binding energy and high affinity to the HLA-A0201 molecule for epitopes VSIPWTHKV and YMDDVVLGA and epitopes RVTGGVFLV and VSIPWTHKV, respectively. Regions 307 to 320 and 377 to 387 were considered to have the highest probability to be involved in B cell epitopes. The T cell and B cell epitopes identified in this study are promising targets for an epitope-focused, peptide-based HBV vaccine, and provide insight into HBV-induced immune response.

Model for end-stage liver disease (MELD) scoring was initiated using traditional statistical technique by assuming a linear relationship between clinical features, but most phenomena in a clinical situation are not linearly related. The aim of this study was to predict 3-month mortality risk of acute-on-chronic hepatitis B liver failure (ACHBLF) on an individual patient level using an artificial neural network (ANN) system. The ANN model was built using data from 402 consecutive patients with ACHBLF. It was trained to predict 3-month mortality by the data of 280 patients and validated by the remaining 122 patients. The area under the curve of receiver operating characteristic (AUROC) was calculated for ANN and MELD-based scoring systems. The following variables age (P < 0.001), prothrombin activity (P < 0.001), serum sodium (P < 0.001), total bilirubin (P = 0.015), hepatitis B e antigen positivity rate (P < 0.001) and haemoglobin (P < 0.001) were significantly related to the prognosis of ACHBLF and were selected to build the ANN. The ANN performed significantly better than MELD-based scoring systems both in the training cohort (AUROC = 0.869 vs 0.667, 0.591, 0.643, 0.571 and 0.577; P < 0.001, respectively) and in the validation cohort (AUROC = 0.765 vs 0.599, 0.563, 0.601, 0.521 and 0.540; P ≤ 0.006, respectively). Thus, the ANN model was shown to be more accurate in predicting 3-month mortality of ACHBLF than MELD-based scoring systems.

The mechanism underlying the metastasis of gastric cancer (GC) cells remains elusive. REG3A is considered an oncogene in various cancers, but in GC its role is unclear. Here, we report that the expression of REG3A was significantly increased in the tumor tissues of patients with GC compared with the matched normal tissues. Knockdown of REG3A induced by specific small interfering RNA (siRNA) significantly repressed the proliferation of GC cells for 24 h or 48 h. Moreover, knockdown of REG3A significantly suppressed the migration, invasion, and adhesion of GC cells in vitro. Furthermore, knockdown of REG3A reduced the phosphorylation of JAK2 and STAT3, and altered the messenger RNA (mRNA) and protein expression levels of E-cadherin, Snail, RhoC, MTA1, MMP-2, and MMP-9. Taken together, REG3A is overexpressed in GC and promotes the proliferation, migration, invasion, and adhesion of GC cells by regulating the JAK2/STAT3 signal pathway. REG3A may be a potential therapeutic target for GC.