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Design biocompatible TeSex nano-alloys being a flexible theranostic nanoplatform.
Thus, we developed a pseudovirus-based assay for SARS-CoV-2, which will be valuable for evaluating viral entry inhibitors and neutralizing antibodies against this highly pathogenic virus.The pandemic COVID-19, caused by a new coronavirus SARS-CoV-2 infection, has infected over 12 million individuals and caused more than 55,200 death worldwide. Currently, there is no specific drug to treating this disease. Here we summarized the mechanisms of antiviral therapies and the clinic findings from different countries. Antiviral chemotherapies have been conducted by in multiple cohorts in different counties. Although FDA has fast approved remdesivir for treating COVID-19, it only speeds up recovery from COVID-19 with mildly reduced mortality. The chloroquine was suggested a potential drug against SARS-CoV-2 infection due to its in vitro antiviral effects, it is imperative high-quality data from worldwide clinical trials are necessitated for an approved therapy. In terms of hydroxychloroquine (HCQ) therapy, although WHO has stopped all the clinic trials due to its strong side-effects in COVID patients, large scale clinical trials with a long-term outcome follow-up may warrant HCQ and azithromycin combination in combating the virus. Convalescent plasma (CP) therapy suggested its safety use in SARS-CoV-2 infection; but both CP immunotherapy and NK cellular therapy must be manufactured and utilized according to scrupulous ethical and controlled conditions to guarantee a possible role of these products of human origin. Further research should be conducted to define the exact mechanism of SARS-CoV-2 pathogenesis, suitable animal models or ex vivo human lung tissues aid in studying replication, transmission and spread of the novel viruses, thereby facilitating highly effective therapies.Coronavirus Disease 2019 (COVID-19) was first identified in China at the end of 2019. Acute respiratory distress syndrome (ARDS) represents the most common and serious complication of COVID-19. Eganelisib Cytokine storms are a pathophysiological feature of COVID-19 and play an important role in distinguishing hyper-inflammatory subphenotypes of ARDS. Accordingly, in this review, we focus on hyper-inflammatory host responses in ARDS that play a critical role in the differentiated development of COVID-19. Furthermore, we discuss inflammation-related indicators that have the potential to identify hyper-inflammatory subphenotypes of COVID-19, especially for those with a high risk of ARDS. Finally, we explore the possibility of improving the quality of monitoring and treatment of COVID-19 patients and in reducing the incidence of critical illness and mortality via better distinguishing hyper- and hypo-inflammatory subphenotypes of COVID-19.COVID-19 has been declared a pandemic by the World Health Organization on March 11th and since then more than 3 million cases and a quarter million deaths have occurred due to it. The urge to find a resultful treatment or cure is now pressing more than any other time since the outbreak of the pandemic. Researchers all over the world from different fields of expertise are trying to find the most suitable drugs, that are already known to treat other diseases, and could tackle the process of SARS-CoV2 through which it invades and replicates in human cells. Here, we discuss five of the most promising drugs that can potentially play a major role in the treatment of COVID-19. While nicotine and ivermectin may be blocking transport abilities of the virus or its components, famotidine, remdesivir and chloroquine in combination with zinc ions can deactivate important enzymes needed for the replication of the virus. While clinical trials for some of these drugs have already started, it is common knowledge that lack of organization between countries, institutes and hospitals might slow down the whole process for an official treatment based in wide, randomized, placebo controlled trials.As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to disperse globally with worrisome speed, identifying amino acid variations in the virus could help to understand the characteristics of it. Here, we studied 489 SARS-CoV-2 genomes obtained from 32 countries from the Nextstrain database and performed phylogenetic tree analysis by clade, country, and genotype of the surface spike glycoprotein (S protein) at site 614. We found that virus strains from mainland China were mostly distributed in Clade B and Clade undefined in the phylogenetic tree, with very few found in Clade A. In contrast, Clades A2 (one case) and A2a (112 cases) predominantly contained strains from European regions. Moreover, Clades A2 and A2a differed significantly from those of mainland China in age of infected population (P = 0.0071, mean age 40.24 to 46.66), although such differences did not exist between the US and mainland China. Further analysis demonstrated that the variation of the S protein at site 614 (QHD43416.1 p.614D>G) was a characteristic of stains in Clades A2 and A2a. Importantly, this variation was predicted to have neutral or benign effects on the function of the S protein. In addition, global quality estimates and 3D protein structures tended to be different between the two S proteins. In summary, we identified different genomic epidemiology among SARS-CoV-2 strains in different clades, especially in an amino acid variation of the S protein at 614, revealing potential viral genome divergence in SARS-CoV-2 strains.Understanding the outbreak dynamics of COVID-19 through the lens of mathematical models is an elusive but significant goal. Within only half a year, the COVID-19 pandemic has resulted in more than 19 million reported cases across 188 countries with more than 700,000 deaths worldwide. Unlike any other disease in history, COVID-19 has generated an unprecedented volume of data, well documented, continuously updated, and broadly available to the general public. Yet, the precise role of mathematical modeling in providing quantitative insight into the COVID-19 pandemic remains a topic of ongoing debate. Here we discuss the lessons learned from six month of modeling COVID-19. We highlight the early success of classical models for infectious diseases and show why these models fail to predict the current outbreak dynamics of COVID-19. We illustrate how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters-in real time-from reported case data to make informed predictions and guide political decision making.
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