Experimental Models of SARS-CoV-2 Infection: Possible Platforms to Study COVID-19 Pathogenesis and Potential Treatments.

In December 2019, a novel coronavirus crossed species barriers to infect humans and was effectively transmitted from person to person, leading to a worldwide pandemic. Development of effective clinical interventions, including vaccines and antiviral drugs that could prevent or limit theburden or transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global health priority. It is thus of utmost importance to assess possible therapeutic strategies against SARS-CoV-2 using experimental models that recapitulate aspects of the human disease. Here, we review available models currently being developed and used to study SARS-CoV-2 infection and highlight their application to screen potential therapeutic approaches, including repurposed antiviral drugs and vaccines. Each identified model provides a valuable insight into SARS-CoV-2 cellular tropism, replication kinetics, and cell damage that could ultimately enhance understanding of SARS-CoV-2 pathogenesis and protective immunity.

The Association of CT-measured Cardiac Indices with Lung Involvement and Clinical Outcome in Patients with COVID-19.

RATIONALE AND OBJECTIVES: Cardiac indices can predict disease severity and survival in a multitude of respiratory and cardiovascular diseases. Herein, we hypothesized that CT-measured cardiac indices are correlated with severity of lung involvement and can predict survival in patients with COVID-19. MATERIALS AND METHODS: Eighty-seven patients with confirmed COVID-19 who underwent chest CT were enrolled. Cardiac indices including pulmonary artery-to-aorta ratio (PA/A), cardiothoracic ratio (CTR), epicardial adipose tissue (EAT) thickness and EAT density, inferior vena cava diameter, and transverse-to-anteroposterior trachea ratio were measured by non-enhanced CT. Logistic regression and Cox-regression analyses evaluated the association of cardiac indices with patients' outcome (death vs discharge). Linear regression analysis was used to assess the relationship between the extent of lung involvement (based on CT score) and cardiac indices. RESULTS: Mean (±SD) age of patients was 54.55 (±15.3) years old; 65.5% were male. Increased CTR (>0.49) was seen in 52.9% of patients and was significantly associated with increased odds and hazard of death (odds ratio [OR] = 12.5, p = 0.005; hazard ratio = 11.4, p = 0.006). PA/A >1 was present in 20.7% of patients and displayed a nonsignificant increase in odds of death (OR = 1.9, p = 0.36). Furthermore, extensive lung involvement was positively associated with elevated CTR and increased PA/A (p = 0.001). CONCLUSION: CT-measured cardiac indices might have predictive value regarding survival and extent of lung involvement in hospitalized patients with COVID-19 and could possibly be used for the risk stratification of these patients and for guiding therapy decision-making. In particular, increased CTR is prevalent in patients with COVID-19 and is a powerful predictor of mortality.

Epicardial adipose tissue, inflammatory biomarkers and COVID-19: Is there a possible relationship?

BACKGROUND & AIMS: Adipose tissue is a biologically active organ with pro-immunogenic properties. We aimed to evaluate the prognostic value of epicardial adipose tissue (EAT) in COVID-19 and its correlation with other inflammatory biomarkers. MATERIAL AND METHODS: One-hundred patients with COVID-19 were enrolled. C-reactive protein (CRP), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-CRP ratio (LCR), and platelet-to-lymphocyte ratio (PLR) were evaluated on admission. EAT volume and density were measured by computed tomography. Patients were followed until death or discharge. Univariate and multivariate analysis was performed and ROC curve analysis was used to assess the ability of inflammatory markers in predicting survival. The relationship between EAT and other inflammatory markers was also investigated. RESULTS: The mean ± SD age of patients was 55.5 ± 15.2 years old; 68% were male. Univariate analysis revealed that increased lung involvement, blood urea nitrogen, LDH and NLR, and decreased platelet count were significantly associated with death. After adjustment, LDH was independently predictive of death (OR = 1.013, p-value = 0.03). Among inflammatory markers, LCR had the best ability for predicting survival with 79.7% sensitivity and 64.3% specificity at an optimal cut-off value of 20.8 (AUC = 0.744, 95% CI = 0.612-0.876, p-value = 0.004). EAT volume demonstrated positive correlation with NLR and PLR (p = 0.001 and 0.01), and a negative correlation with LCR (p = 0.02). EAT density was significantly different between decedents and survivors (p = 0.008). CONCLUSION: Routine laboratory tests that represent status of inflammation can be used as cost-effective prognostic markers of COVID-19. Also, the significant association between EAT volume and other inflammatory biomarkers might explain the more severe disease in obese patients.