Sequential coupling of dry and wet COVID-19 screening to reduce the number of quarantined individuals.

INTRODUCTION: Currently, several countries are facing severe public health and policy challenges when designing their COVID-19 screening strategy. A quantitative analysis of the potential impact that combing the Rapid Antigen Test (RAT; Wet screening) and digital checker (Dry screening) can have on the healthcare system is lacking. METHOD: We created a hypothetical COVID-19 cohort for the analysis. The population size was set as 10 million with three levels of disease prevalence (10%, 1%, or 0.1%) under the assumption that a positive test result will lead to quarantine. A digital checker and two RATs are used for analysis. We further hypothesized two scenarios: RAT only and RAT plus digital checker. We then calculated the number of quarantined in both scenarios and compared the two to understand the benefits of sequential coupling of a digital checker with a RAT. RESULT: Sequential coupling of the digital checker and RAT can significantly reduce the number of individuals quarantined to 0.95-1.33M, 0.86-1.29M, and 0.86-1.29M, respectively, under the three different prevalence levels. CONCLUSION: Sequential coupling of digital checker and RAT at a population level for COVID-19 positive test to reduce the number of people who require quarantine and alleviating stress on the overburdened healthcare systems during the COVID-19 pandemic.

Natural Bioactive Molecules: An Alternative Approach to the Treatment and Control of COVID-19.

Several coronaviruses (CoVs) have been associated with serious health hazards in recent decades, resulting in the deaths of thousands around the globe. The recent coronavirus pandemic has emphasized the importance of discovering novel and effective antiviral medicines as quickly as possible to prevent more loss of human lives. Positive-sense RNA viruses with group spikes protruding from their surfaces and an abnormally large RNA genome enclose CoVs. CoVs have already been related to a range of respiratory infectious diseases possibly fatal to humans, such as MERS, SARS, and the current COVID-19 outbreak. As a result, effective prevention, treatment, and medications against human coronavirus (HCoV) is urgently needed. In recent years, many natural substances have been discovered with a variety of biological significance, including antiviral properties. Throughout this work, we reviewed a wide range of natural substances that interrupt the life cycles for MERS and SARS, as well as their potential application in the treatment of COVID-19.

Metformin Use Is Associated with Decreased Mortality in COVID-19 Patients with Diabetes: Evidence from Retrospective Studies and Biological Mechanism.

BACKGROUND AND AIMS: The coronavirus disease 2019 (COVID-19) increases hyperinflammatory state, leading to acute lung damage, hyperglycemia, vascular endothelial damage, and a higher mortality rate. Metformin is a first-line treatment for type 2 diabetes and is known to have anti-inflammatory and immunosuppressive effects. Previous studies have shown that metformin use is associated with decreased risk of mortality among patients with COVID-19; however, the results are still inconclusive. This study investigated the association between metformin and the risk of mortality among diabetes patients with COVID-19. METHODS: Data were collected from online databases such as PubMed, EMBASE, Scopus, and Web of Science, and reference from the most relevant articles. The search and collection of relevant articles was carried out between 1 February 2020, and 20 June 2021. Two independent reviewers extracted information from selected studies. The random-effects model was used to estimate risk ratios (RRs), with a 95% confidence interval. RESULTS: A total of 16 studies met all inclusion criteria. Diabetes patients given metformin had a significantly reduced risk of mortality (RR, 0.65; 95% CI: 0.54-0.80, p < 0.001, heterogeneity I2 = 75.88, Q = 62.20, and τ2 = 0.06, p < 0.001) compared with those who were not given metformin. Subgroup analyses showed that the beneficial effect of metformin was higher in the patients from North America (RR, 0.43; 95% CI: 0.26-0.72, p = 0.001, heterogeneity I2 = 85.57, Q = 34.65, τ2 = 0.31) than in patients from Europe (RR, 0.67; 95% CI: 0.47-0.94, p = 0.02, heterogeneity I2 = 82.69, Q = 23.11, τ2 = 0.10) and Asia (RR, 0.90; 95% CI: 0.43-1.86, p = 0.78, heterogeneity I2 = 64.12, Q = 11.15, τ2 = 0.40). CONCLUSIONS: This meta-analysis shows evidence that supports the theory that the use of metformin is associated with a decreased risk of mortality among diabetes patients with COVID-19. Randomized control trials with a higher number of participants are warranted to assess the effectiveness of metformin for reducing the mortality of COVID-19 patients.

A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19.

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges.

Statin Use Is Associated with a Decreased Risk of Mortality among Patients with COVID-19.

BACKGROUND: Recent epidemiological studies remain controversial regarding the association between statin use and reducing the risk of mortality among individuals with COVID-19. OBJECTIVE: The objective of this study was to clarify the association between statin use and the risk of mortality among patients with COVID-19. METHODS: We conducted a systematic articles search of online databases (PubMed, EMBASE, Scopus, and Web of Science) between 1 February 2020 and 20 February 2021, with no restriction on language. The following search terms were used: "Statins" and "COVID-19 mortality or COVID19 mortality or SARS-CoV-2 related mortality". Two authors individually examined all articles and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for study inclusion and exclusion. The overall risk ratio (RRs) with 95% confidence interval (CI) was calculated to show the strength of the association and the heterogeneity among the studies was presented Q and I2 statistic. RESULTS: Twenty-eight studies were assessed for eligibility and 22 studies met the inclusion criteria. Statin use was associated with a significantly decreased risk of mortality among patients with COVID-19 (RR adjusted = 0.64; 95% CI: 0.57-0.72, p < 0.001). Moreover, statin use both before and after the admission was associated with lowering the risk of mortality among the COVID-19 patients (RR adjusted;before = 0.69; 95% CI: 0.56-0.84, p < 0.001 and RR adjusted;after = 0.57; 95% CI: 0.54-0.60, p < 0.001). CONCLUSION: This comprehensive study showed that statin use is associated with a decreased risk of mortality among individuals with COVID-19. A randomized control trial is needed to confirm and refute the association between them.

Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis.

BACKGROUND: The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia. OBJECTIVE: The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images. METHODS: A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms "COVID-19," or "coronavirus," or "SARS-CoV-2," or "novel corona," or "2019-ncov," and "deep learning," or "artificial intelligence," or "automatic detection." Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus. RESULTS: A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95% CI 12.83-28.19), 0.06 (95% CI 0.04-0.10), and 368.07 (95% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95% CI 0.92-0.94) and 0.95 (95% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools. CONCLUSIONS: Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Obesity and Mortality Among Patients Diagnosed With COVID-19: A Systematic Review and Meta-Analysis.

Coronavirus disease 2019 (COVID-19) has already raised serious concern globally as the number of confirmed or suspected cases have increased rapidly. Epidemiological studies reported that obesity is associated with a higher rate of mortality in patients with COVID-19. Yet, to our knowledge, there is no comprehensive systematic review and meta-analysis to assess the effects of obesity and mortality among patients with COVID-19. We, therefore, aimed to evaluate the effect of obesity, associated comorbidities, and other factors on the risk of death due to COVID-19. We did a systematic search on PubMed, EMBASE, Google Scholar, Web of Science, and Scopus between January 1, 2020, and August 30, 2020. We followed Cochrane Guidelines to find relevant articles, and two reviewers extracted data from retrieved articles. Disagreement during those stages was resolved by discussion with the main investigator. The random-effects model was used to calculate effect sizes. We included 17 articles with a total of 543,399 patients. Obesity was significantly associated with an increased risk of mortality among patients with COVID-19 (RRadjust: 1.42 (95%CI: 1.24-1.63, p < 0.001). The pooled risk ratio for class I, class II, and class III obesity were 1.27 (95%CI: 1.05-1.54, p = 0.01), 1.56 (95%CI: 1.11-2.19, p < 0.01), and 1.92 (95%CI: 1.50-2.47, p < 0.001), respectively). In subgroup analysis, the pooled risk ratio for the patients with stroke, CPOD, CKD, and diabetes were 1.80 (95%CI: 0.89-3.64, p = 0.10), 1.57 (95%CI: 1.57-1.91, p < 0.001), 1.34 (95%CI: 1.18-1.52, p < 0.001), and 1.19 (1.07-1.32, p = 0.001), respectively. However, patients with obesity who were more than 65 years had a higher risk of mortality (RR: 2.54; 95%CI: 1.62-3.67, p < 0.001). Our study showed that obesity was associated with an increased risk of death from COVID-19, particularly in patients aged more than 65 years. Physicians should aware of these risk factors when dealing with patients with COVID-19 and take early treatment intervention to reduce the mortality of COVID-19 patients.

Clinical Characteristics and Neonatal Outcomes of Pregnant Patients With COVID-19: A Systematic Review.

Background and Objective: Coronavirus disease 2019 (COVID-19) characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created serious concerns about its potential adverse effects. There are limited data on clinical, radiological, and neonatal outcomes of pregnant women with COVID-19 pneumonia. This study aimed to assess clinical manifestations and neonatal outcomes of pregnant women with COVID-19. Methods: We conducted a systematic article search of PubMed, EMBASE, Scopus, Google Scholar, and Web of Science for studies that discussed pregnant patients with confirmed COVID-19 between January 1, 2020, and April 20, 2020, with no restriction on language. Articles were independently evaluated by two expert authors. We included all retrospective studies that reported the clinical features and outcomes of pregnant patients with COVID-19. Results: Forty-seven articles were assessed for eligibility; 13 articles met the inclusion criteria for the systematic review. Data is reported for 235 pregnant women with COVID-19. The age range of patients was 25-40 years, and the gestational age ranged from 8 to 40 weeks plus 6 days. Clinical characteristics were fever [138/235 (58.72%)], cough [111/235 (47.23%)], and sore throat [21/235 (8.93%)]. One hundred fifty six out of 235 (66.38%) pregnant women had cesarean section, and 79 (33.62%) had a vaginal delivery. All the patients showed lung abnormalities in CT scan images, and none of the patients died. Neutrophil cell count, C-reactive protein (CRP) concentration, ALT, and AST were increased but lymphocyte count and albumin levels were decreased. Amniotic fluid, neonatal throat swab, and breastmilk samples were taken to test for SARS-CoV-2 but all found negativ results. Recent published evidence showed the possibility of vertical transmission up to 30%, and neonatal death up to 2.5%. Pre-eclampsia, fetal distress, PROM, pre-mature delivery were the major complications of pregnant women with COVID-19. Conclusions: Our study findings show that the clinical, laboratory and radiological characteristics of pregnant women with COVID-19 were similar to those of the general populations. The possibility of vertical transmission cannot be ignored but C-section should not be routinely recommended anymore according to latest evidences and, in any case, decisions should be taken after proper discussion with the family. Future studies are needed to confirm or refute these findings with a larger number of sample sizes and a long-term follow-up period.