Flavonoid as possible therapeutic targets against COVID-19: a scoping review of in silico studies.

OBJECTIVES: This scoping review aims to present flavonoid compounds' promising effects and possible mechanisms of action on potential therapeutic targets in the SARS-CoV-2 infection process. METHODS: A search of electronic databases such as PubMed and Scopus was carried out to evaluate the performance of substances from the flavonoid class at different stages of SARS-CoV-2 infection. RESULTS: The search strategy yielded 382 articles after the exclusion of duplicates. During the screening process, 265 records were deemed as irrelevant. At the end of the full-text appraisal, 37 studies were considered eligible for data extraction and qualitative synthesis. All the studies used virtual molecular docking models to verify the affinity of compounds from the flavonoid class with crucial proteins in the replication cycle of the SARS-CoV-2 virus (Spike protein, PLpro, 3CLpro/ MPro, RdRP, and inhibition of the host's ACE II receptor). The flavonoids with more targets and lowest binding energies were: orientin, quercetin, epigallocatechin, narcissoside, silymarin, neohesperidin, delphinidin-3,5-diglucoside, and delphinidin-3-sambubioside-5-glucoside. CONCLUSION: These studies allow us to provide a basis for in vitro and in vivo assays to assist in developing drugs for the treatment and prevention of COVID-19.

Bioactive compounds as potential angiotensin-converting enzyme II inhibitors against COVID-19: a scoping review.

OBJECTIVE AND DESIGN: The current study aimed to summarize the evidence of compounds contained in plant species with the ability to block the angiotensin-converting enzyme 2 (ACE-II), through a scoping review. METHODS: PubMed and Scopus electronic databases were used for the systematic search and a manual search was performed RESULTS: Studies included were characterized as in silico. Among the 200 studies retrieved, 139 studies listed after the exclusion of duplicates and 74 were included for the full read. Among them, 32 studies were considered eligible for the qualitative synthesis. The most evaluated class of secondary metabolites was flavonoids with quercetin and curcumin as most actives substances and terpenes (isothymol, limonin, curcumenol, anabsinthin, and artemisinin). Other classes that were also evaluated were alkaloid, saponin, quinone, substances found in essential oils, and primary metabolites as the aminoacid L-tyrosine and the lipidic compound 2-monolinolenin. CONCLUSION: This review suggests the most active substance from each class of metabolites, which presented the strongest affinity to the ACE-II receptor, what contributes as a basis for choosing compounds and directing the further experimental and clinical investigation on the applications these compounds in biotechnological and health processes as in COVID-19 pandemic.

The naturally-derived alkaloids as a potential treatment for COVID-19: A scoping review.

Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which has a high mortality rate and transmissibility. In this context, medicinal plants have attracted attention due to the wide availability and variety of therapeutic compounds, such as alkaloids, a vast class with several proven pharmacological effects, like the antiviral and anti-inflammatory activities. Therefore, this scoping review aimed to summarize the current knowledge of the potential applicability of alkaloids for treating COVID-19. A systematic search was performed on PubMed and Scopus, from database inception to August 2021. Among the 63 eligible studies, 65.07% were in silico model, 20.63% in vitro and 14.28% clinical trials and observational studies. According to the in silico assessments, the alkaloids 10-hydroxyusambarensine, cryptospirolepine, crambescidin 826, deoxynortryptoquivaline, ergotamine, michellamine B, nigellidine, norboldine and quinadoline B showed higher binding energy with more than two target proteins. The remaining studies showed potential use of berberine, cephaeline, emetine, homoharringtonine, lycorine, narciclasine, quinine, papaverine and colchicine. The possible ability of alkaloids to inhibit protein targets and to reduce inflammatory markers show the potential for development of new treatment strategies against COVID-19. However, more high quality analyses/reviews in this field are necessary to firmly establish the effectiveness/safety of the alkaloids here described.

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning.

OBJECTIVE: To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes. MATERIAL AND METHODS: Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN. RESULTS: The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19. CONCLUSION: The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?

OBJECTIVE: This study aimed to implement and evaluate machine learning based-models to predict COVID-19' diagnosis and disease severity. METHODS: COVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients' laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN) models were used. The four models were validated based on the accuracy (area under the ROC curve). RESULTS: The first subset of data had 5,643 patient samples (5,086 negatives and 557 positives for COVID-19). The second subset included 557 COVID-19 positive patients. The ANN, DT, PLS-DA, and KNN models allowed the classification of negative and positive samples with >84% accuracy. It was also possible to classify patients with severe and non-severe disease with an accuracy >86%. The following were associated with the prediction of COVID-19 diagnosis and severity: hyperferritinaemia, hypocalcaemia, pulmonary hypoxia, hypoxemia, metabolic and respiratory acidosis, low urinary pH, and high levels of lactate dehydrogenase. CONCLUSION: Our analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.

Risk factors associated with delay in diagnosis and mortality in patients with COVID-19 in the city of Rio de Janeiro, Brazil

We investigated the predictors of delay in the diagnosis and mortality of patients with COVID-19 in Rio de Janeiro, Brazil. A cohort of 3,656 patients were evaluated (Feb-Apr 2020) and patients'sociodemographic characteristics, and social development index (SDI) were used as determinant factors of diagnosis delays and mortality. Kaplan-Meier survival analyses, time-dependent Cox regression models, and multivariate logistic regression analyses were conducted. The median time from symptoms onset to diagnosis was eight days (interquartile range [IQR] 7.23-8.99 days). Half of the patients recovered during the evaluated period, and 8.3% died. Mortality rates were higher in men. Delays in diagnosis were associated with male gender (p = 0.015) and patients living in low SDI areas (p <0.001). The age groups statistically associated with death were: 70-79 years, 80-89 years, and 90-99 years. Delays to diagnosis greater than eight days were also risk factors for death. Delays in diagnosis and risk factors for death from COVID-19 were associated with male gender, age under 60 years, and patients living in regions with lower SDI. Delays superior to eight days to diagnosis increased mortality rates. Resumo Investigamos os preditores de atraso no diagnóstico e mortalidade de pacientes com COVID-19 no Rio de Janeiro, Brasil. Uma coorte de 3.656 pacientes foi avaliada (fevereiro-abril de 2020) e as características sociodemográficas dos pacientes, o bairro e o índice de desenvolvimento social (IDS) foram usados como fatores determinantes dos atrasos no diagnóstico e da mortalidade. Foram realizadas análises de sobrevivência de Kaplan-Meier, modelos de regressão Cox dependentes do tempo e análises de regressão logística multivariada. O tempo mediano desde o início dos sintomas até o diagnóstico foi de oito dias (intervalo interquartil [IQR] 7,23-8,99 dias). Metade dos pacientes se recuperou no período avaliado e 8,3% faleceram. As taxas de mortalidade foram maiores nos homens. Atrasos no diagnóstico foram associados ao sexo masculino (p = 0,015) e pacientes que moravam em áreas com baixo IDS (p <0,001). As faixas etárias estatisticamente associadas à morte foram: 70-79 anos, 80-89 anos e 90-99 anos. Atrasos no diagnóstico superiores a oito dias também foram fatores de risco para óbito. Atrasos no diagnóstico e fatores de risco para morte por COVID-19 foram associados ao sexo masculino, idade abaixo de 60 anos e pacientes que vivem em regiões com menor IDS. Atrasos superiores a oito dias no diagnóstico aumentam as taxas de mortalidade.

Risk factors associated with delay in diagnosis and mortality in patients with COVID-19 in the city of Rio de Janeiro, Brazil

We investigated the predictors of delay in the diagnosis and mortality of patients with COVID-19 in Rio de Janeiro, Brazil. A cohort of 3,656 patients were evaluated (Feb-Apr 2020) and patients'sociodemographic characteristics, and social development index (SDI) were used as determinant factors of diagnosis delays and mortality. Kaplan-Meier survival analyses, time-dependent Cox regression models, and multivariate logistic regression analyses were conducted. The median time from symptoms onset to diagnosis was eight days (interquartile range [IQR] 7.23-8.99 days). Half of the patients recovered during the evaluated period, and 8.3% died. Mortality rates were higher in men. Delays in diagnosis were associated with male gender (p = 0.015) and patients living in low SDI areas (p <0.001). The age groups statistically associated with death were: 70-79 years, 80-89 years, and 90-99 years. Delays to diagnosis greater than eight days were also risk factors for death. Delays in diagnosis and risk factors for death from COVID-19 were associated with male gender, age under 60 years, and patients living in regions with lower SDI. Delays superior to eight days to diagnosis increased mortality rates. Resumo Investigamos os preditores de atraso no diagnóstico e mortalidade de pacientes com COVID-19 no Rio de Janeiro, Brasil. Uma coorte de 3.656 pacientes foi avaliada (fevereiro-abril de 2020) e as características sociodemográficas dos pacientes, o bairro e o índice de desenvolvimento social (IDS) foram usados como fatores determinantes dos atrasos no diagnóstico e da mortalidade. Foram realizadas análises de sobrevivência de Kaplan-Meier, modelos de regressão Cox dependentes do tempo e análises de regressão logística multivariada. O tempo mediano desde o início dos sintomas até o diagnóstico foi de oito dias (intervalo interquartil [IQR] 7,23-8,99 dias). Metade dos pacientes se recuperou no período avaliado e 8,3% faleceram. As taxas de mortalidade foram maiores nos homens. Atrasos no diagnóstico foram associados ao sexo masculino (p = 0,015) e pacientes que moravam em áreas com baixo IDS (p <0,001). As faixas etárias estatisticamente associadas à morte foram: 70-79 anos, 80-89 anos e 90-99 anos. Atrasos no diagnóstico superiores a oito dias também foram fatores de risco para óbito. Atrasos no diagnóstico e fatores de risco para morte por COVID-19 foram associados ao sexo masculino, idade abaixo de 60 anos e pacientes que vivem em regiões com menor IDS. Atrasos superiores a oito dias no diagnóstico aumentam as taxas de mortalidade.

A multivariate analysis of risk factors associated with death by Covid-19 in the USA, Italy, Spain, and Germany.

Aim: Our aim was to investigate the risk factors associated with death from COVID-19 in four countries: The USA, Italy, Spain, and Germany. Subject and methods: We used data from the Institute for Health Metrics and Evaluation with projection information from January-August 2020. A multivariate analysis of logistic regression was performed. The following factors were analyzed (per day): number of beds needed for the hospital services, number of intensive care units (ICU) beds required, number of ventilation devices, number of both hospital and ICU admissions due to COVID-19. Nagelkerke's R2 coefficient of determination was used to evaluate the model's predictive ability. The quality of the model's fit was assessed by the Hosmer-Lemeshow and the chi-square tests. Results: Among the evaluated countries, Italy presented greater need for ICU beds/day (≤ 98; OR = 2315.122; CI 95% [334.767-16,503.502]; p < 0.001) and daily ventilation devices (≤ 118; OR = 1784.168; CI 95% [250.217-12,721.995]; p < 0.001). It is expected that both Italy and Spain have a higher ICU admission rate due to COVID-19 (n = 14/day). Spain will need more beds/day (≤ 357; OR = 146.838; CI 95% [113.242-190.402]; p  < 0.001) and probably will have a higher number of daily hospital admissions (n = 48/day). All the above-mentioned factors have an important impact on patients' mortality due to COVID-19 in all four countries. Conclusions: Further investments in hospitals' infrastructure, as well as the development of innovative devices for patient's ventilation, are paramount to fight the pandemic in the USA, Italy, Spain, and Germany.

Risk factors associated with delay in diagnosis and mortality in patients with COVID-19 in the city of Rio de Janeiro, Brazil.

We investigated the predictors of delay in the diagnosis and mortality of patients with COVID-19 in Rio de Janeiro, Brazil. A cohort of 3,656 patients were evaluated (Feb-Apr 2020) and patients' sociodemographic characteristics, and social development index (SDI) were used as determinant factors of diagnosis delays and mortality. Kaplan-Meier survival analyses, time-dependent Cox regression models, and multivariate logistic regression analyses were conducted. The median time from symptoms onset to diagnosis was eight days (interquartile range [IQR] 7.23-8.99 days). Half of the patients recovered during the evaluated period, and 8.3% died. Mortality rates were higher in men. Delays in diagnosis were associated with male gender (p = 0.015) and patients living in low SDI areas (p < 0.001). The age groups statistically associated with death were: 70-79 years, 80-89 years, and 90-99 years. Delays to diagnosis greater than eight days were also risk factors for death. Delays in diagnosis and risk factors for death from COVID-19 were associated with male gender, age under 60 years, and patients living in regions with lower SDI. Delays superior to eight days to diagnosis increased mortality rates.