Pharmacological Activity of Quercetin: An Updated Review.

Quercetin, a natural flavonoid compound with a widespread occurrence throughout the plant kingdom, exhibits a variety of pharmacological activities. Because of the wide spectrum of health-promoting effects, quercetin has attracted much attention of dietitians and medicinal chemists. An updated review of the literature on quercetin was performed using PubMed, Embase, and Science Direct databases. This article presents an overview of recent developments in pharmacological activities of quercetin including anti-SARS-CoV-2, antioxidant, anticancer, antiaging, antiviral, and anti-inflammatory activities as well as the mechanism of actions involved. The biological activities of quercetin were evaluated both in vitro and in vivo, involving a number of cell lines and animal models, but metabolic mechanisms of quercetin in the human body are not clear. Therefore, further large sample clinical studies are needed to determine the appropriate dosage and form of quercetin for the treatment of the disease.

Pharmacological Activity of Quercetin: An Updated Review

Quercetin, a natural flavonoid compound with a widespread occurrence throughout the plant kingdom, exhibits a variety of pharmacological activities. Because of the wide spectrum of health-promoting effects, quercetin has attracted much attention of dietitians and medicinal chemists. An updated review of the literature on quercetin was performed using PubMed, Embase, and Science Direct databases. This article presents an overview of recent developments in pharmacological activities of quercetin including anti-SARS-CoV-2, antioxidant, anticancer, antiaging, antiviral, and anti-inflammatory activities as well as the mechanism of actions involved. The biological activities of quercetin were evaluated both in vitro and in vivo, involving a number of cell lines and animal models, but metabolic mechanisms of quercetin in the human body are not clear. Therefore, further large sample clinical studies are needed to determine the appropriate dosage and form of quercetin for the treatment of the disease.

Research progress of CRISPR-based biosensors and bioassays for molecular diagnosis.

CRISPR/Cas technology originated from the immune mechanism of archaea and bacteria and was awarded the Nobel Prize in Chemistry in 2020 for its success in gene editing. Molecular diagnostics is highly valued globally for its development as a new generation of diagnostic technology. An increasing number of studies have shown that CRISPR/Cas technology can be integrated with biosensors and bioassays for molecular diagnostics. CRISPR-based detection has attracted much attention as highly specific and sensitive sensors with easily programmable and device-independent capabilities. The nucleic acid-based detection approach is one of the most sensitive and specific diagnostic methods. With further research, it holds promise for detecting other biomarkers such as small molecules and proteins. Therefore, it is worthwhile to explore the prospects of CRISPR technology in biosensing and summarize its application strategies in molecular diagnostics. This review provides a synopsis of CRISPR biosensing strategies and recent advances from nucleic acids to other non-nucleic small molecules or analytes such as proteins and presents the challenges and perspectives of CRISPR biosensors and bioassays.

Dose-response relationship between risk factors and incidence of COVID-19 in 325 hospitalized patients: A multicenter retrospective cohort study.

BACKGROUND: The epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19) patients have been widely reported, but the assessment of dose-response relationships and risk factors for mortality and severe cases and clinical outcomes remain unclear. AIM: To determine the dose-response relationship between risk factors and incidence of COVID-19. METHODS: In this retrospective, multicenter cohort study, we included patients with confirmed COVID-19 infection who had been discharged or had died by February 6, 2020. We used multivariable logistic regression and Cox proportional hazard models to determine the dose-response relationship between risk factors and incidence of COVID-19. RESULTS: It clarified that increasing risk of in-hospital death were associated with older age (HR: 1.04, 95%CI: 1.01-1.09), higher lactate dehydrogenase [HR: 1.04, 95% confidence interval (CI): 1.01-1.10], C-reactive protein (HR: 1.10, 95%CI: 1.01-1.23), and procalcitonin (natural log-transformed HR: 1.88, 95%CI: 1.22-2.88), and D-dimer greater than 1 µg/mL at admission (natural log transformed HR: 1.63, 95%CI: 1.03-2.58) by multivariable regression. D-dimer and procalcitonin were logarithmically correlated with COVID-19 mortality risk, while there was a linear dose-response correlation between age, lactate dehydrogenase, D-dimer and procalcitonin, independent of established risk factors. CONCLUSION: Higher lactate dehydrogenase, D-dimer, and procalcitonin levels were independently associated with a dose-response increased risk of COVID-19 mortality.

Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: a retrospective study from Wuhan, China.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. METHODS: This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID-19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression. RESULTS: Patients with severe/critical symptoms were older (p < 0.001) and more often male (p = 0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (> 11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 (95% confidence interval: 0.811-0.902). CONCLUSIONS: Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (> 11) was associated with severe illness.

Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. RESULTS: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.