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COVID-19 caused by a new type of coronavirus (SARS-CoV-2) originated in China has speedily become a frightening pandemic all over the world. Despite of intense efforts relevant to experimental and clinical studies since the start of the COVID-19 pandemic, no disease-specific drug or vaccine is available, yet. Several treatment strategies including already known antiviral drugs, interpherons, interleukin inhibitors, and other drugs acting through different mechanims are being implemented in COVID-19 patients. On the other hand, natural products especially pythocompounds have been proven to be lead molecules for drug design and development research. Among them, curcumin as the main constituent of Curcuma longa L. (turmeric), is the reputed compound displaying remarkable biological activities for human health. It has been shown to have a inhibiting effect against a wide range of viruses such as HCV, HIV, PEDV, HSV, Ebola, dengue, influenza, Zika, SARS-CoV, etc. Therefore, curcumin could be considered as a structural model for designing new molecules against COVID-19. In the present review, antiviral activity of curcumin is scrutinized thorugh the literature data relevant to its enzyme and receptor interactions, in vitro, in vivo, in silico, and cell-based assays. [ABSTRACT FROM AUTHOR]

Background The outbreak of coronavirus disease 2019 (COVID-19) has spread worldwide and continues to threaten peoples' health as well as put pressure on the accessibility of medical systems. Early prediction of survival of hospitalized patients will help in the clinical management of COVID-19, but a prediction model that is reliable and valid is still lacking. Methods We retrospectively enrolled 628 confirmed cases of COVID-19 using positive RT-PCR tests for SARS-CoV-2 in Tongji Hospital, Wuhan, China. These patients were randomly grouped into a training (60%) and a validation (40%) cohort. In the training cohort, LASSO regression analysis and multivariate Cox regression analysis were utilized to identify prognostic factors for in-hospital survival of patients with COVID-19. A nomogram based on the 3 variables was built for clinical use. AUCs, concordance indexes (C-index), and calibration curves were used to evaluate the efficiency of the nomogram in both training and validation cohorts. Results Hypertension, higher neutrophil-to-lymphocyte ratio, and increased NT-proBNP values were found to be significantly associated with poorer prognosis in hospitalized patients with COVID-19. The 3 predictors were further used to build a prediction nomogram. The C-indexes of the nomogram in the training and validation cohorts were 0.901 and 0.892, respectively. The AUC in the training cohort was 0.922 for 14-day and 0.919 for 21-day probability of in-hospital survival, while in the validation cohort this was 0.922 and 0.881, respectively. Moreover, the calibration curve for 14- and 21-day survival also showed high coherence between the predicted and actual probability of survival. Conclusions We built a predictive model and constructed a nomogram for predicting in-hospital survival of patients with COVID-19. This model has good performance and might be utilized clinically in management of COVID-19. [ABSTRACT FROM AUTHOR]