Insights into the Correlation between Toll-Like Receptor 2 Polymorphism and HBV-Related Disease Progression and Occurrence of Hepatocellular Carcinoma: A Case-Control Study in Egyptian Patients.

Methods: In total, 170 chronic HBV patients and 50 healthy controls of comparable age and gender were included in this case-control study. Clinical, laboratory, and imaging evaluations were conducted. ELISA was used to determine serum IL-6 levels, and TLR2 (rs3804099) genotyping allelic discrimination assay was performed using real-time PCR. Results: IL-6 values were significantly higher in the HCC group, followed by the cirrhotic group, than those in chronic hepatitis and control groups (p < 0.001), with a significant correlation with disease activity and progression parameters. TRL2 homozygous TT was the most frequent in the control group, but the CC genotype was significantly more prevalent in the HCC group than that in the other groups. Furthermore, the CC genetic variant was associated with higher levels of IL-6 and viral load in all HBV patients, whereas the TT genotype was associated with larger tumor size. Multivariate regression analysis demonstrated that in chronic HBV patients, viral load and TRL2 polymorphism are independent risk factors associated with the progression from chronic hepatitis to liver cirrhosis and to HCC. Similarly, the HBV viral load (p=0.03, OR = 2.45, and 95% CI: 1.69-3.65), IL-6 levels (p=0.04, OR = 3.45, and 95% CI: 2.01-6.9), and TRL2 variants (p=0.01, OR = 4.25, and 95% CI: 2.14-13.5) are independent risk factors associated with disease progression from cirrhosis to HCC. Conclusion: In chronic HBV patients, TRL2 polymorphism and higher IL-6 levels were positively correlated with a higher likelihood of HCC and chronic hepatitis B disease activity and progression.

Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning.

BACKGROUND: After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity. METHODS: Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models. RESULTS: The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness. CONCLUSIONS: Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.