Acute Liver Failure due to Hepatitis A Virus

IL-10 and IL-18: Key players in liver damage associated with hepatitis A virus infection

BACKGROUND

Hepatitis A virus infection is a health threat with multiple transmission patterns across areas, It is evaluated using immune response markers IL-10 and IL-18, along with molecular and biochemical diagnostic methods for accurate diagnosis.

OBJECTIVE

The association between liver damage and interleukin-10 and interleukin-18 levels in people with hepatitis A virus infection as indications of the risk of acute liver failure.

METHODS

110 samples were collected from Iraqi individuals from both sexes and different age groups ⩽ 1 to ⩾ 25, including 60 patients and 50 healthy people. All samples were collected from a hospital in Diwaniyah city, and the infection was confirmed by antiHAV IgM titers and One-Step RT-PCR. ELISA was used to determine the levels of IL-10 and IL-18, while Biochemical tests measured for alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase (AST) and total serum Bilirubin (TSB) in serum.

RESULTS

In this study, IL-10 levels were higher in HAV patients (0.12 ± 0.06 ng/L) compared to controls (0.11 ± 0.04 ng/L), but the difference was not significant (p= 0.17). Conversely, IL-18 levels were significantly elevated in patients (1.41 ± 0.71) versus controls (0.58 ± 0.35) (p= 0.00). Biochemical tests showed significantly elevated levels in HAV patients: ALT (170.18 ± 117.67 vs. 21.25 ± 7.41), AST (183.05 ± 128.13 vs. 26.00 ± 7.69), ALP (607.68 ± 214.93 vs. 202.02 ± 121.35), and TSB (2.77 ± 2.5 vs. 0.55 ± 0.14) (all p< 0.001). These findings underscore the potential of IL-10 and IL-18 as biomarkers for HAV severity and highlight their role in liver injury.

CONCLUSION

Our study highlights the important roles of IL-10 and IL-18 in acute hepatitis A and reveals their impact on the immune response and liver damage. Elevated levels of IL-10, IL-18 and Biochemical tests are associated with disease severity, suggesting their potential as biomarkers and therapeutic targets to improve the management of HAV infection.

Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats

Although drug-induced liver injury (DILI) is a major target of the pharmaceutical industry, we currently lack an efficient model for evaluating liver toxicity in the early stage of its development. Recent progress in artificial intelligence-based deep learning technology promises to improve the accuracy and robustness of current toxicity prediction models. Mask region-based CNN (Mask R-CNN) is a detection-based segmentation model that has been used for developing algorithms. In the present study, we applied a Mask R-CNN algorithm to detect and predict acute hepatic injury lesions induced by acetaminophen (APAP) in Sprague-Dawley rats. To accomplish this, we trained, validated, and tested the model for various hepatic lesions, including necrosis, inflammation, infiltration, and portal triad. We confirmed the model performance at the whole-slide image (WSI) level. The training, validating, and testing processes, which were performed using tile images, yielded an overall model accuracy of 96.44%. For confirmation, we compared the model’s predictions for 25 WSIs at 20× magnification with annotated lesion areas determined by an accredited toxicologic pathologist. In individual WSIs, the expert-annotated lesion areas of necrosis, inflammation, and infiltration tended to be comparable with the values predicted by the algorithm. The overall predictions showed a high correlation with the annotated area. The R square values were 0.9953, 0.9610, and 0.9445 for necrosis, inflammation plus infiltration, and portal triad, respectively. The present study shows that the Mask R-CNN algorithm is a useful tool for detecting and predicting hepatic lesions in non-clinical studies. This new algorithm might be widely useful for predicting liver lesions in non-clinical and clinical settings.