Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative

COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.

Endotheliitis, Shunts, and Ventilation-Perfusion Mismatch in Coronavirus Disease 2019: A Literature Review of Disease Mechanisms

The severe acute respiratory syndrome coronavirus 2 pandemic has continued to impact global health. However, while immunity acquired by vaccines has been developed, 40% of the world's population has still not been vaccinated. Economic problems associated with acquiring novel therapies, misinformation, and differences in treatment protocols have generated catastrophic results, especially in low-resource countries. Understanding the pathophysiological aspects of coronavirus disease and the therapeutic strategies that have been validated to date is essential for successful medical care. In this review, I summarize the historical aspects of the virus, molecules involved in infecting the host, and consequences of viral interactions with and in tissues.

Screening of Natural Products Inhibitors of SARS-CoV-2 Entry

The COVID-19 pandemic has led to the search for new molecules with antiviral activity against SARS-CoV-2. The entry of the virus into the cell is one of the main targets for inhibiting SARS-CoV-2 infection. Natural products are an important source of new therapeutic alternatives against diseases. Pseudotyped viruses allow the study of SARS-CoV-2 viral entry inhibitors, and due to their simplicity, they allow the screening of a large number of antiviral candidates in Biosafety Level 2 facilities. We used pseudotyped HIV-1 with the D614G SARS-CoV-2 spike glycoprotein to test its ability to infect ACE2-expressing HEK 293T cells in the presence of diverse natural products, including 21 plant extracts, 7 essential oils, and 13 compounds from plants and fungi. The 50% cytotoxic concentration (CC50) was evaluated using the resazurin method. From these analyses, we determined the inhibitory activity of the extract of Stachytarpheta cayennensis, which had a half-maximal inhibitory concentration (IC50) of 91.65 µg/mL, a CC50 of 693.5 µg/mL, and a selectivity index (SI) of 7.57, indicating its potential use as an inhibitor of SARS-CoV-2 entry. Moreover, our work indicates the usefulness of the pseudotyped-virus system in the screening of SARS-CoV-2 entry inhibitors.

Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook

A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.

Serum cytokine/chemokine profile and clinical/paraclinical data in COVID-19 deceased and recovered patients

Objectives: The induction of an intense immune response and cytokine storm is proposed to be central in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The study evaluated serum cytokine/chemokine profiles, and clinical and paraclinical data of COVID-19 deceased and recovered patients in Iran. Methods: The severity of disease, clinical data, and routine laboratory and inflammatory cytokine/chemokine responses were retrospectively explored in 60 in-hospital patients in northern Iran. Characteristics of those who deceased (n = 30) were compared to recovered (n = 30), and associations with serum levels of potential disease regulating pro- and anti-inflammatory mediators were studied. Results: The serum levels of IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-17, IP-10, MIP1-α, MCP1, RANTES, and TNF-α were upregulated in all COVID-19 patients when compared to healthy and gender-matched individuals (n = 30). Although with no significant difference between deceased and recovered cases, the serum levels of all cytokines/chemokines tended to be higher in the severely diseased non-surviving patients. Association analyses revealed that all cytokine/chemokine levels (except IL-10) significantly affect the disease outcome. Conclusion: This study provides more evidence for the association of cytokine/chemokine levels with the clinical course and outcome of COVID-19. More studies are needed to consider this measurement as an indicator of disease stage and strategy for treatment.