ABSTRACT
Kombucha, originated in China 2000 years ago, is a sour and sweet-tasted drink, prepared traditionally through fermentation of black tea. During the fermentation of kombucha, consisting of mainly acidic compounds, microorganisms, and a tiny amount of alcohol, a biofilm called SCOBY forms. The bacteria in kombucha has been generally identified as Acetobacteraceae. Kombucha is a noteworthy source of B complex vitamins, polyphenols, and organic acids (mainly acetic acid). Nowadays, kombucha is tended to be prepared with some other plant species, which, therefore, lead to variations in its composition. Pre-clinical studies conducted on kombucha revealed that it has desired bioactivities such as antimicrobial, antioxidant, hepatoprotective, anti-hypercholestorelomic, anticancer, anti-inflammatory, etc. Only a few clinical studies have been also reported. In the current review, we aimed to overhaul pre-clinical bioactivities reported on kombucha as well as its brief compositional chemistry. The literature data indicate that kombucha has valuable biological effects on human health.
ABSTRACT
New coronavirus referred to SARS-CoV-2 has caused a worldwide pandemic (COVID-19) declared by WHO. Coronavirus disease 2019 (COVID-19) is an infectious disease with severe acute respiratory syndrome caused by coronavirus-2 (SARS-CoV-2). SARS-CoV-2 is akin to SARS-CoV, which was the causative agent of severe acute respiratory syndrome (SARS) in 2002 as well as to that of Middle East respiratory syndrome (MERS) in 2012. SARS-CoV-2 has been revealed to belong to Coronaviridiae family as a member of ß-coronaviruses. It has a positive-sense single-stranded RNA with the largest RNA genome. Since its genomic sequence has a notable similarity to that of SARS-CoV, antiviral drugs used to treat SARS and MERS are now being also applied for COVID-19 treatment. In order to combat SARS-CoV-2, many drug and vaccine development studies at experimental and clinical levels are currently conducted worldwide. In this sense, medicinal plants and the pure natural molecules isolated from plants have been reported to exhibit significant inhibitory antiviral activity against SARS-CoV and other types of coronaviruses. In the present review, plant extracts and natural molecules with the mentioned activity are discussed in order to give inspiration to researchers to take these molecules into consideration against SARS-CoV-2.
ABSTRACT
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.