New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning.

Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.

A methanol extract and N,N-dimethyltryptamine from Psychotria viridis Ruiz & Pav. inhibit Zika virus infection in vitro.

Zika virus (ZIKV) is a public health problem due to its association with serious fetal and neurological complications and the lack of antiviral agents and licensed vaccines against this virus. Surveillance studies have alerted about the potential occurrence of a new South American epidemic episode due to the recent circulation of an African ZIKV strain detected in Brazil. Therefore, it is essential to discover antiviral agents, including natural substances, that are capable of neutralizing the action of ZIKV. Several Psychotria species have antimicrobial and anti-inflammatory properties. Thus, a methanol extract and dimethyltryptamine from Psychotria viridis were evaluated for their ability to inhibit ZIKV infection in vitro by measuring the effective concentration that protects 50% of cells and investigating their possible mechanisms of action. The tested samples showed antiviral activity against ZIKV. The extract showed virucidal activity, affecting viral and non-cellular elements, inactivating the virus before infection or when it becomes extracellular after the second cycle of infection. It was also observed that both extract and dimethyltryptamine could inhibit the virus at intracellular stages of the viral cycle. In addition to dimethyltryptamine, it is believed that other compounds also contribute to the promising virucidal effect observed for the methanol extract. To our knowledge, this is the first report of the activity of a methanolic extract and dimethyltryptamine from Psychotria viridis against cellular ZIKV infection. These two samples, extracted from natural sources, are potential candidates for use as antiviral drugs to inhibit ZIKV infections.

Will a little change do you good? A putative role of polymorphisms in COVID-19.

An alarming disease caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) named COVID-19 has emerged as an unprecedented public health problem and ignited a world health crisis. As opposed to what was believed at the beginning of the pandemic, the virus has not only spread but persevere causing secondary waves and challenging the concept of herd immunity against viral infections. While the majority of SARS-CoV-2-infected individuals may remain asymptomatic, a fraction of individuals may develop low to high-grade severity signs and symptoms of COVID-19. The disease is multifactorial and can progress quickly, leading to severe complications and even death in a few days. Therefore, understanding the pre-existing factors for disease development has never been so pressing. In this scenario, the insights on the mechanisms underlying disease allied to the immune response developed during the viral invasion could shed light on novel predictive factors and prognostic tools for COVID-19 management and interventions. A recent genome-wide association study (GWAS) revealed several molecules that significantly impacted critically ill COVID-19 patients, leading to the core mechanisms of COVID-19 pathogenesis. Considering these findings and the fact that ACE-2 polymorphisms alone cannot explain disease progress and severity, this review aims at summarizing the most important and recent findings of the research and expert consensus of possible cytokine-related polymorphisms existing in the differential expression of paramount immune molecules that could be crucial for providing guidelines for decision-making and appropriate clinical management of COVID-19.