covid19论文文献综述

最初的回答是:COVID-19有有效的治疗方法吗?

图像来源:FDA和EMA COVID-19治疗和疫苗更新

最好是服用大剂量的维生素D3，每天50,000国际单位连续三天，那么你的维生素D3维持剂量。雷·席林(Ray Schilling)对这么多人死于流感和冠状病毒的主要原因之一的回答是肺炎。既然我们有肺炎疫苗，为什么让公众接种肺炎疫苗不是标准程序?

维生素D治疗冠状病毒的研究已经彻底展开。2020年4月2日出版的《补充维生素D可降低流感和COVID-19感染风险及死亡的证据》包含157篇参考文献。

一个美国研究小组证实了我上面所述的一切:补充维生素D可以预防和治疗流感、冠状病毒和肺炎感染。这个参考文献有243个支持性的参考文献。以下是这些作者注意到的情况:

补充维生素D3在Covid-19冠状病毒感染中的作用:更高剂量的维生素D3将减轻流感和Covid-19冠状病毒的病程。研究人员概述了维生素D工作的三种机制:

维持紧密的上皮连接使Covid-19冠状病毒更难渗透。”

通过诱导抗菌素和防御素杀灭包膜病毒。“这些强大的抗病毒多肽可以在1到2天内杀死入侵血液的病毒。”

减少先天免疫系统促炎细胞因子的产生，从而降低细胞因子风暴导致肺炎的风险。“感染病毒性肺炎的人死亡的风险很高。通过将血液水平提高到较高的正常范围(50至80 ng/mL)，遇到Covid-19冠状病毒的患者更有可能生存下来。

此前曾发表:雷·席林关于“是否有任何药物可以在与冠状病毒阳性者接触后立即服用，以防止疾病全面爆发”的回答?

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application. key: 1）基于网络（聚类和传播） 2）基于结构 3）基于AI 提示关键过程是病毒刺突蛋白与人血管紧张素转化酶2（ACE2）和跨膜丝氨酸蛋白酶2（TMPRSS2）的相互作用：刺突蛋白的受体结合结构域与人ACE2的肽酶结构域结合。Mpro介导病毒的复制和转录 计算了药物靶标与HCoV相关蛋白之间的网络邻近度，以筛选人蛋白相互作用组模型下HCoV的候选可重复使用药物 A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption 1 ,[2]( "WHO. Coronavirus disease (COVID-2019) situation reports. (2020)."). There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein–protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19. key: 亲和纯化质谱 26 332 66 69The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus. key: 125 proteins (31 viral proteins and 94 human host proteins) and 200 unique interactions. Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Various studies exist about the molecular mechanisms of viral infection. However, such information is spread across many publications and it is very time-consuming to integrate, and exploit. We develop CoVex, an interactive online platform for SARS-CoV-2 host interactome exploration and drug (target) identification. CoVex integrates virus-human protein interactions, human protein-protein interactions, and drug-target interactions. It allows visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drug candidates. Thus, CoVex is a resource to understand molecular mechanisms of pathogenicity and to prioritize candidate therapeutics. We investigate recent hypotheses on a systems biology level to explore mechanistic virus life cycle drivers, and to extract drug repurposing candidates. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms. It is available at . AI方法： Background The ongoing COVID-19 pandemic has caused more than 193,825 deaths during the past few months. A quick-to-be-identified cure for the disease will be a therapeutic medicine that has prior use experiences in patients in order to resolve the current pandemic situation before it could become worsening. Artificial intelligence (AI) technology is hereby applied to identify the marketed drugs with potential for treating COVID-19. Methods An AI platform was established to identify potential old drugs with anti-coronavirus activities by using two different learning databases; one consisted of the compounds reported or proven active against SARS-CoV, SARS-CoV-2, human immunodeficiency virus, influenza virus, and the other one containing the known 3C-like protease inhibitors. All AI predicted drugs were then tested for activities against a feline coronavirus in in vitro cell-based assay. These assay results were feedbacks to the AI system for relearning and thus to generate a modified AI model to search for old drugs again. Results After a few runs of AI learning and prediction processes, the AI system identified 80 marketed drugs with potential. Among them, 8 drugs (bedaquiline, brequinar, celecoxib, clofazimine, conivaptan, gemcitabine, tolcapone, and vismodegib) showed in vitro activities against the proliferation of a feline infectious peritonitis (FIP) virus in Fcwf-4 cells. In addition, 5 other drugs (boceprevir, chloroquine, homoharringtonine, tilorone, and salinomycin) were also found active during the exercises of AI approaches. Conclusion Having taken advantages of AI, we identified old drugs with activities against FIP coronavirus. Further studies are underway to demonstrate their activities against SARS-CoV-2 in vitro and in vivo at clinically achievable concentrations and doses. With prior use experiences in patients, these old drugs if proven active against SARS-CoV-2 can readily be applied for fighting COVID-19 pandemic. The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with Kd of nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir ( nM), efavirenz ( nM), ritonavir ( nM), and dolutegravir ( nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra (lopinavir/ritonavir), could be used for the treatment of SARS-CoV-2. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for SARS-CoV-2. key: 同时基于药物-靶点相互作用和分子结构 注意力机制学习药物序列和靶点氨基酸序列的亲和力，Docking We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. We mapped 219 molecular features with high significance to COVID-19 status and severity, many of which were involved in complement activation, dysregulated lipid transport, and neutrophil activation. We identified sets of covarying molecules, ., protein gelsolin and metabolite citrate or plasmalogens and apolipoproteins, offering pathophysiological insights and therapeutic suggestions. The observed dysregulation of platelet function, blood coagulation, acute phase response, and endotheliopathy further illuminated the unique COVID-19 phenotype. We present a web-based tool () enabling interactive exploration of our compendium and illustrate its utility through a machine learning approach for prediction of COVID-19 severity. key: RNA-seq and high-resolution mass spectrometry 跨组学 鉴定与疾病严重程度相关的分子特征 219 molecular features 表明在COVID-19下确实可以调节关键的生物学过程，包括补体系统激活，脂质转运，血管损伤，血小板激活和脱粒，凝血，和急性期反应 我们还提供了一个应用示例，该示例利用此资源基于所有组学数据开发疾病严重性预测模型

我认为对于COVID-19，我们应该持有一个平常心态度，其实说到底，它和流感病毒是很相似的，只是它的传染性和攻击性更强，是我们人类还未曾遇到过的。加强身体的抵抗力，和健康程度，应该从一个角度上可以有效的抵抗病毒。我认为从加强自身免疫力的角度，应该是可以并有希望治疗的。

肯定有有希望治疗的方法，可以去接种疫苗，在生活中需要注意卫生，多多运动，锻炼身体，增强自身免疫力，还是要坚持戴口罩的。

通过血清治疗法，或者打疫苗都可以有效的根治病毒，在日常生活中需要注意卫生。