Evidence-based characterization of the diagnostic and prognostic value of the hematological and immunological markers related to the epidemic of Coronavirus Disease 2019 (COVID-19) is critical to understand the clinical course of the infection and to assess in development and validation of biomarkers. Based on systematic search in Web of Science, PubMed, Scopus, and Science Direct up to April 22, 2020, a total of 52 eligible articles with 6,320 laboratory-confirmed COVID-19 cohorts were included. Pairwise comparison between severe versus mild disease, Intensive Care Unit (ICU) versus general ward admission and expired versus survivors were performed for 36 laboratory parameters. The pooled standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated using the DerSimonian Laird method/random effects model and converted to the Odds ratio (OR). The decision tree algorithm was employed to identify the key risk factor(s) attributed to severe COVID-19 disease. Cohorts with elevated levels of white blood cells (WBCs) (OR = 1.75), neutrophil count (OR = 2.62), D-dimer (OR = 3.97), prolonged prothrombin time (PT) (OR = 1.82), fibrinogen (OR = 3.14), erythrocyte sedimentation rate (OR = 1.60), procalcitonin (OR = 4.76), IL-6 (OR = 2.10), and IL-10 (OR = 4.93) had higher odds of progression to severe phenotype. Decision tree model (sensitivity = 100%, specificity = 81%) showed the high performance of neutrophil count at a cut-off value of more than 3.74x10⁹/L for identifying patients at high risk of severe COVID‐19. Likewise, ICU admission was associated with higher levels of WBCs (OR = 5.21), neutrophils (OR = 6.25), D-dimer (OR = 4.19), and prolonged PT (OR = 2.18). Patients with high IL-6 (OR = 13.87), CRP (OR = 7.09), D-dimer (OR = 6.36), and neutrophils (OR = 6.25) had the highest likelihood of mortality. Several hematological and immunological markers, in particular neutrophilic count, could be helpful to be included within the routine panel for COVID-19 infection evaluation to ensure risk stratification and effective management.

Background: Coronavirus disease‐2019 (COVID‐19) has a deleterious effect on several systems, including the cardiovascular system. We aim to systematically explore the association of COVID‐19 severity and mortality rate with the history of cardiovascular diseases and/or other comorbidities and cardiac injury laboratory markers.Methods: The standardized mean difference (SMD) or odds ratio (OR) and 95% confidence intervals (CIs) were applied to estimate pooled results from the 56 studies. The prognostic performance of cardiac markers for predicting adverse outcomes and to select the best cutoff threshold was estimated by receiver operating characteristic curve analysis. Decision tree analysis by combining cardiac markers with demographic and clinical features was applied to predict mortality and severity in patients with COVID‐19.Results: A meta‐analysis of 17 794 patients showed patients with high cardiac troponin I (OR = 5.22, 95% CI = 3.73‐7.31, P < .001) and aspartate aminotransferase (AST) levels (OR = 3.64, 95% CI = 2.84‐4.66, P < .001) were more likely to develop adverse outcomes. High troponin I more than 13.75 ng/L combined with either advanced age more than 60 years or elevated AST level more than 27.72 U/L was the best model to predict poor outcomes.Conclusions: COVID‐19 severity and mortality are complicated by myocardial injury. Assessment of cardiac injury biomarkers may improve the identification of those patients at the highest risk and potentially lead to improved therapeutic approaches.

The gut–liver-axis is a bidirectional coordination between the gut, including microbial residents, the gut microbiota, from one side and the liver on the other side. Any disturbance in this crosstalk may lead to a disease status that impacts the functionality of both the gut and the liver. A major cause of liver disorders is hepatitis C virus (HCV) infection that has been illustrated to be associated with gut microbiota dysbiosis at different stages of the disease progression. This dysbiosis may start a cycle of inflammation and metabolic disturbance that impacts the gut and liver health and contributes to the disease progression. This review discusses the latest literature addressing this interplay between the gut microbiota and the liver in HCV infection from both directions. Additionally, we highlight the contribution of gut microbiota to the metabolism of antivirals used in HCV treatment regimens and the impact of these medications on the microbiota composition. This review sheds light on the potential of the gut microbiota manipulation as an alternative therapeutic approach to control the liver complications post HCV infection.

Background: Microorganisms use two-component signal transduction (TCST) systems to regulate the response of the organism to changes of environmental conditions. Such systems are absent from mammalian cells and are thus of interest as drug targets. Fungal TCST systems are usually composed of a hybrid histidine kinase, comprising the histidine kinase (HisKA) domain and a receiver domain, a histidine phosphotransfer protein and a response regulator. Among the 11 groups of fungal histidine kinases, group III histidine kinases are of particular relevance as they are essential for the activity of different groups of fungicides. A characteristic feature is the N-terminal amino acid repeat domain comprising multiple HAMP domains, of which the function is still largely unknown. In Candida albicans, a fungal human pathogen, three histidine kinases were identified, of which CaNik1p is a group III histidine kinase. Heterologous expression of this protein in Sacchromyces cerevisiae conferred susceptibility to different fungicides. Fungicide activity was associated with phosphorylation of the mitogen activated protein kinase Hog1p. Results: We have constructed mutated versions of CaNik1p, from which either all HAMP domains were deleted (CaNik1pΔHAMP) or in which the histidine kinase or the receiver domains were not-functional. Expression of CaNIK1ΔHAMP in S. cerevisiae led to severe growth inhibition. Normal growth could be restored by either replacing the phosphate-accepting histidine residue in CaNik1pΔHAMP or by expressing CaNIK1ΔHAMP in S. cerevisiae mutants, in which single genes encoding several components of the HOG pathway were deleted. Expression of proteins with non-functional histidine kinase or receiver domains resulted in complete loss of susceptibility to antifungals, such as fludioxonil. Conditions leading to growth inhibition of transformants also led to phosphorylation of the MAP kinase Hog1p. Conclusion: Our results show that functional histidine kinase and receiver domains of CaNik1p were essential for antifungal susceptibility and for activation of the Hog1p. Moreover, for the first time we show that deletion of all HAMP domains from CaNik1p led to activation of Hog1p without an external stimulus. This phenotype was similar to the effects obtained upon treatment with fungicides, as in both cases growth inhibition correlated with Hog1p activation and was dependent on the functionality of the conserved phosphate-accepting histidine residue.

Mixed-species biofilms could create a protected environment that allows for survival to external antimicrobials and allows different bacterial-fungal interactions.Pseudomonas aeruginosa-Candida albicanscoexistence is an example for such mixed-species community. Numerous reports demonstrated howP. aeruginosaor its metabolites could influence the growth, morphogenesis, and virulence ofC. albicans. In this study, we investigated how theC. albicansquorum sensing compounds, tyrosol and farnesol, might affect Egyptian clinical isolates ofP. aeruginosaregarding growth, antibiotic sensitivity, and virulence. We could demonstrate that tyrosol possesses an antibacterial activity againstP. aeruginosa(10 µM inhibited more than 50% of growth after 16 h cultivation). Moreover, we could show for the first time that tyrosol strongly inhibits the production of the virulence factors hemolysin and protease inP. aeruginosa, whereas farnesol inhibits, to lower extent, hemolysin production in this bacterial pathogen. Cumulatively, tyrosol is expected to strongly affectP. aeruginosain mixed microbial biofilm.