Cancer and viruses have a long history that has evolved over many decades. Much information about the interplay between viruses and cell proliferation and metabolism has come from the history of clinical cases of patients infected with virus-induced cancer. In addition, information from viruses used to treat some types of cancer is valuable. Now, since the global coronavirus pandemic erupted almost a year ago, the scientific community has invested countless time and resources to slow down the infection rate and diminish the number of casualties produced by this highly infectious pathogen. A large percentage of cancer cases diagnosed are strongly related to dysregulations of the tyrosine kinase receptor (TKR) family and its downstream signaling pathways. As such, many therapeutic agents have been developed to strategically target these structures in order to hinder certain mechanisms pertaining to the phenotypic characteristics of cancer cells such as division, invasion or metastatic potential. Interestingly, several authors have pointed out that a correlation between coronaviruses such as the SARS-CoV-1 and -2 or MERS viruses and dysregulations of signaling pathways activated by TKRs can be established. This information may help to accelerate the repurposing of clinically developed anti-TKR cancer drugs in COVID-19 management. Because the need for treatment is critical, drug repurposing may be an advantageous choice in the search for new and efficient therapeutic compounds. This approach would be advantageous from a financial point of view as well, given that the resources used for research and development would no longer be required and can be potentially redirected towards other key projects. This review aims to provide an overview of how SARS-CoV-2 interacts with different TKRs and their respective downstream signaling pathway and how several therapeutic agents targeted against these receptors can interfere with the viral infection. Additionally, this review aims to identify if SARS-CoV-2 can be repurposed to be a potential viral vector against different cancer types. Coronaviruses are RNA viruses that affect mammals, having an affinity for the respiratory apparatus in humans. Strains of coronavirus, namely severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), have previously caused a large number of cases before completely disappearing. SARS-CoV-2’s origin is currently still unknown, but bats are a very likely source, as SARS-CoV and MERS-CoV, similar coronaviruses, have been associated with bats ,2. SARS-SARS-CoV-2 is the newest strain of beta coronaviruses, known to have an incubation period of 5.2 days . This loThe pathogenesis of the virus is mainly represented by the attachment of the spike (S)-glycoprotein located on the surface of the coronavirus to the angiotensin conversion enzyme 2 (ACE2) receptor from the human cells . The S-gFurthermore, research has been conducted regarding the ability of the SARS-CoV-2 S1 RBD to bind heparin. Heparins are drugs used for their anticoagulant/thrombotic properties and are known for being safe, stable and highly effective. They also present antiviral activity, which was never fully explored in a clinical setting. Interestingly, coronaviruses are also targeted by heparin because of SARS-CoV’s envelope proteins containing positively charged amino acids that are prone to interact with the negatively charged sulfate groups of heparin sulfate proteoglycans .Lmin) [The innate immune system is activated, and pattern recognition receptors (PRRs) are used to recognize the pathogen-associated molecular patterns (PAMP). PRRs consist predominantly of toll-like receptor (TLR), RIG-I-like receptor (RLR) , NOD-liLmin) , cytosolLmin) .The aforementioned complex factors catalyze the activation of the transcription factor nuclear factor-κB (NF-κB) and interferon regulatory factor 3 (IRF3), leading to the production of type I interferons (IFN-α/β) and a series of proinflammatory cytokines ,16.Oncolytic virotherapy is a novel therapy consisting of the use of replicating viruses, through the genetic modification that they produce in cells, as a means of treating cancer. The viruses’ tropism is restricted in order to infect only certain cell types. Furthermore, exogenous genes can be added in order to make the virus more aggressive, hence inducing the host’s immune response against the specifically targeted cancer cells ,18.Growth factor receptors (GFRs) possess the important role of binding extracellular polypeptide growth factors, which determines a cascade of signaling events with the final purpose of regulating cell growth . GFRs arThe epidermal growth factor receptor (EGFR) is a member of the ErbB family of TKRs with important functions in epithelial cell physiology . It is wThe EGFR can play a role in the internalization of coronaviruses through binding to the S protein. Transmissible gastroenteritis virus (TGEV) is an alpha-coronavirus that infects the epithelial cells of the intestine, causing severe, potentially lethal, diarrhea in piglets. In a study, the mechanism of infection of TGEV was analyzed, concentrating on the binding with the EGFR. The internalization of the virus was achieved through clathrin- and caveolin-mediated endocytosis. Afterwards, the virus was bound to the EGFR, promoting successive clathrin-mediated endocytosis .After the TGEV spike protein binds with EGFR, the phosphoinositide 3 kinase (PI3K) pathway is activated, inducing the phosphorylation of cofilin and the polymerization of F-actin via Rac1/Cdc42 GTPases. EGFR activates the MAPK pathway, correlated with F-actin reorganization, thus proving again the important involvement of the EGFR in the coronavirus endocytosis . TGEV inResearchers explored the possibility of how SARS-CoV infection can influence EGFR signaling and consequently amplify the effect of the receptor’s activation. The authors tested the potential of EGFR to provoke fibrosis and how much it varies depending on the presence of viral infection. To their surprise, the overregulation of EGFR signaling followed by SARS-CoV infection determined higher levels of inflammation in the lungs than it would be normally expected alongside interstitial edema .EGFR TKIs are known to have the side effect of promoting interstitial lung disease in the patients receiving these drugs . An impoGefitinib, a TKI used for the first-line treatment of EGFR-mutated NSCLC for almost two decades, is known to aggravate pulmonary fibrosis inflicted by bleomycin ,33,34. AThe available data are contradictory given that anti-EGFR TKIs can cause pulmonary fibrosis in humans while preventing pulmonary fibrosis in mice, so there can be several ways of explaining the difference. One explanation is that EGFR signaling can determine different downstream results, depending on the species it encounters. Another way of explaining this discrepancy is that the EGFR downstream signaling kinetics could be dysregulated and not necessarily dependent on the strength of the signal itself .Fibroblast growth factor receptors (FGFRs) are TKRs that possess an important role in cell proliferation, migration and differentiation. The dysregulation of their expression can lead to the emergence of different tumors . The FGFFGFR can also be relevant in viral infections. FGF, bound to heparan sulfate molecules, interacts with FGFR, creating a trimolecular FGF–HS–FGFR complex, setting off subsequent FGFR activation . FGFR1 wIn a study by Hardie et al., several human kinases were screened in order to identify those that could be linked to dengue fever replication. Of those explored, the study focused on the role of FGFR4, a member of the FGFR family. The study showed that dengue fever infection determines an impairment of FGFR phosphorylation. More interestingly, the inhibition of FGFRs via siRNA provided a decrease in the RNA replication of dengue virus, while simultaneously increasing its viral particle production, suggesting that the FGFR might play a regulatory role in the lifecycle of the virus, switching between the early and late stages . In anotAnother study that analyzed MERS-CoV-induced apoptosis in kidney and lung tissues discovered a correlation between FGFR2 inhibition and the degree of cell death induced by viral infection. By using a specific anti-FGFR TKI, tyrphostin AG1296, the authors observed a reduction in apoptosis by over 40%. However, an anti-EGFR tyrphostin, AG490, had no influence over MERS-CoV-induced apoptosis .Platelet-derived growth factor receptors (PDGFRs) are TKRs with important functions in the development of connective tissue. The two types of receptors are PDGFRα and PDGFRβ. PDGF-PDGFR signaling is important in development, but in the adult age, its function remains relevant only in tissue repair and lesion healing . The mosIt was shown that influenza virus entered the cell through the PDGFRβ/GM3 signaling pathway, and endocytosis was successfully inhibited with the TKIKi8751, which specifically targets PDGFRβ phosphorylation. . FurtherAnticancer drugs have consistently shown potential in the treatment of antiviral infections. During the SARS-CoV-2 pandemic, an important task for researchers has been to find a correlation between the antiviral and antineoplastic function of drugs in order to implement them most effectively in the treatment protocols of COVID-19 patients . Even moIbrutinib, a powerful inhibitor of the Bruton tyrosine kinase (BTK), is a drug that has a possible anti-inflammatory effect best observed in the respiratory apparatus. Its ability to reduce lung damage, cytokine levels in the lung tissue and mortality have been documented in animal experiments using the H1N1 influenza virus strain. The animals that received ibrutinib survived and made a complete recovery .The effect of ibrutinib was also tested in SARS-CoV-2 subjects. A total of 300 patients suffering from Waldenström’s macroglobulinemia (WM) were included in a study in which they received BTK inhibitors. Six of these patients were diagnosed with a SARS-CoV-2 infection and received different doses of ibrutinib . Patients receiving the higher dose presented better evolution with easier symptoms and with hospitalization not being necessary. On the contrary, patients receiving the lower dose showed symptoms with increasing severity, which caused the necessity of hospitalization .Acalabrutinib, another BTK inhibitor, was also successful in the treatment of several patients suffering from severe cases of COVID-19. The patients, 11 of whom received supplemental oxygen and 8 of whom were on mechanical ventilation, were administered acalabrutinib, with improved oxygenation being observed for the majority of them. This proved that BTK inhibitors are relevant for targeting excessive host inflammation in COVID-19 patients .Selinexor, a selective inhibitor of nuclear export (SINE), is a drug approved for treating relapsed/refractory multiple myeloma . SINEs aTKIs are considered a potential treatment for COVID-19, as they are known to target specific host functions that are required by multiple viruses, including SARS-CoV-2 .MAPK/ERK and PI3K/AKT/mTOR signaling responses have been shown to be relevant in MERS-CoV infection through bioinformatics analysis in vivo. Therefore, by suppressing these pathways, the replication was substantially inhibited in vitro .For SARS-CoV, the potential for use of imatinib, an ABL 2 inhibitor approved for clinical practice 20 years ago, was attested due to the inhibition of the replication of SARS-CoV and MERS-CoV prior to RNA production. Thus, a correlation was found between Abl2 and the productive replication of SARS-CoV and MERS-CoV . ImatiniFor the more recent SARS-CoV-2, imatinib’s viral inhibiting properties have been tested in vitro and results showed potential for inhibition, acting especially on the spike protein and blocking the viral entry at the endosomal level . ProstagThe possible link between JAK inhibitors (JAKi) and SARS-CoV-2 has also been taken into account. JAKi are drugs that usually have a tendency to interfere with the immune system, increasing the infectious risk in patients. There have been three anecdotal cases of patients that tested positive for SARS-CoV-2 who are taking JAKi for alopecia areata. None of them had significant events but were nonetheless taken off JAKi. Thus, an important aspect during the pandemic is that doctors are careful what drugs they are prescribing to their patients, especially if those drugs have a potential influence on the evolution of COVID-19 .The carcinogenic potential of viruses is a well-known and documented fact. Of the 219 viral species known to humans, almost 150 types of viruses have carcinogenic potential. Some, such as HPV, are exceptionally carcinogenic, being responsible for almost 95% of cervical cancer cases, while others, such as human herpesvirus 8, are linked to rarer types of cancer such as Kaposi’s sarcoma . While tAnother relation between SARS-CoV-2 and TKR activity in cancer might be established between the large number of proinflammatory cytokines and chemokines, which are largely responsible for acute respiratory distress syndrome and the tumor microenvironment, which has a strong impact on carcinogenesis; more explicitly, a link between the IL-6/JAK/STAT3 pathway and the plethora of proinflammatory molecules found in patients suffering from COVID-19. For example, Zhang et al. observed that the mortality of bladder cancer patients suffering from COVID-19 was 10 times higher than that of other patients suffering from the virus . This waOncolytic virotherapy is becoming an attractive option for the treatment of patients with different forms of cancer. Several clinical studies have researched the use of viral therapy, providing promising results ,82,83. CThe infectious properties of mouse hepatitis coronavirus (MHCV) are obtained through the binding of its S protein to the murine carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1a). Virus–cell membrane fusion is achieved through the induction of conformational changes after the binding of the N-terminal part of the cellular receptor (soR) and the S protein. In order to direct it against the EGFR, a single-chain monoclonal antibody 425 was bound to the soR, creating a bispecific adapter protein (soR-425). The soR-425 proved successful in targeting the EGFR in vitro, but the S protein fusion process was necessary for the virus entry. This research first demonstrated the potential of coronaviruses for tumor-targeting purposes .A few years later, a similar experiment was performed and was again successful in vitro and subsequently continued with an in vivo study. A mouse previously exposed to a lethal intracranial tumor was treated with an MHCV soR-EGF (adaptor protein soR-EGF injected into the MHCV’s genome) injection. This significantly prolonged its survival, stopping the recurrence of the tumor load .The EGFR and other TKRs seem to have a strong correlation with SARS-CoV-2, providing diverse insights into the treatment of COVID-19. The direct binding to the EGFR of TGEV shows that coronaviruses have an affinity for the EGFR, so a potential application for the future is blocking the endocytosis at this level by downregulating the signaling pathway that promotes it. TKIs, more specifically A9 (a tyrphostin-class TKI), produced a satisfactory response in vitro, partially inhibiting the endocytosis of the virus through the EGFR.A common aspect between SARS-CoV-2 and the EGFR TKIs is that they promote interstitial lung disease, having a high similarity of symptoms and radiological showings. Furthermore, EGFR overexpression facilitates pulmonary fibrosis for a SARS-CoV-infected patient. Although many studies have offered the perspective that EGFR has antifibrotic properties, there has also been research demonstrating the opposite. The difference may come from the different species involved in the testing or from the fact that the signal’s intensity/time is not relevant for the EGFR’s activity in fibrosis.Imatinib, a representative of the TKI class, has proven efficient in the inhibition of replication in SARS- and MERS-CoV, proving the implication of Abl2 in the replication. Imatinib was recently tested for SARS-CoV-2 and it successfully inhibited the endocytosis of the virus and also suppressed the NF-κB signaling pathway, which enhanced viral activity levels. The importance of JAKi for COVID-19 has been questioned on a theoretical level, JAKi being known to negatively affect the immune response. The purpose of this correlation is that during the pandemic, the prescription of drugs that may have an effect on the immune response of patients should be closely regulated in order to minimize the rampant advancement of the pandemic.The EGFR and SARS-CoV-2 correlation is also relevant in the oncology field. The potential for oncolytic virotherapy is an important one. Coronaviruses can exert an antitumoral effect when attached to the cancer cell. The targeting of the cell is achieved through the modification of the virus. An additional protein or antibody is bound to the virus, making it prone to connect to the EGFR of the cancer cell.Experiments have been conducted for coronaviruses  and proved successful in vitro and in vivo. The use of bispecific adapter proteins attached to the virus redirected its course towards the EGFR, and endocytosis occurred through S protein fusion.SARS-CoV-2 can certainly be relevant in the oncolytic virotherapy approach, as per its similarity with the MHCV coronavirus, with tests and research required in order for SARS-CoV-2 to prove itself as an important candidate for effective tumor targeting and cancer treatment.No treatment has proven successful in treating SARS-CoV-2 as of yet. With the race to implement an international immunization scheme through vaccination being strongly underway, it might prove wise to try to replicate different treatment strategies that proved effective for other types of viral agents. Additionally, with oncolytic viral therapy being a popular option in the last decade, SARS-CoV-2 might prove useful as a therapeutic agent for the treatment of different cancer forms.


Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East respiratory syndrome has proven valuable for identifying approaches to treating and preventing novel coronavirus disease 2019 (COVID-19). Building on this research, the medical and scientific communities have responded rapidly to the COVID-19 crisis and identified many candidate therapeutics. The approaches used to identify candidates fall into four main categories: adaptation of clinical approaches to diseases with related pathologies, adaptation based on virological properties, adaptation based on host response, and data-driven identification (ID) of candidates based on physical properties or on pharmacological compendia. To date, a small number of therapeutics have already been authorized by regulatory agencies such as the Food and Drug Administration (FDA), while most remain under investigation. The scale of the COVID-19 crisis offers a rare opportunity to collect data on the effects of candidate therapeutics. This information provides insight not only into the management of coronavirus diseases but also into the relative success of different approaches to identifying candidate therapeutics against an emerging disease.After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid-2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: IMPORTANCE The COVID-19 pandemic is a rapidly evolving crisis. With the worldwide scientific community shifting focus onto the SARS-CoV-2 virus and COVID-19, a large number of possible pharmaceutical approaches for treatment and prevention have been proposed. What was known about each of these potential interventions evolved rapidly throughout 2020 and 2021. This fast-paced area of research provides important insight into how the ongoing pandemic can be managed and also demonstrates the power of interdisciplinary collaboration to rapidly understand a virus and match its characteristics with existing or novel pharmaceuticals. As illustrated by the continued threat of viral epidemics during the current millennium, a rapid and strategic response to emerging viral threats can save lives. In this review, we explore how different modes of identifying candidate therapeutics have borne out during COVID-19. Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) emerged in late 2019 and quickly precipitated the worldwide spread of novel coronavirus disease 2019 (COVID-19). COVID-19 is associated with symptoms ranging from mild or even asymptomatic to severe, and up to 2% of patients diagnosed with COVID-19 die from COVID-19-related complications such as acute respiratory disease syndrome (ARDS) (The novel coronavirus e (ARDS) , with thDue to the continued threat of the virus and the severity of the disease, the identification and development of therapeutic interventions have emerged as significant international priorities. Prior developments during other recent outbreaks of emerging diseases, especially those caused by human coronaviruses (HCoVs), have guided biomedical research into the behavior and treatment of this novel coronavirus infection. However, previous emerging HCoV-related disease threats were controlled much more quickly than SARS-CoV-2 through public health efforts . The scaSevere acute respiratory syndrome-related coronavirus 1 (SARS-CoV-1), which was responsible for the 2002−2003 SARS epidemic. The first documented case of COVID-19 was reported in Wuhan, China, in November 2019, and the disease quickly spread worldwide in the early months of 2020. In comparison, the first case of SARS was reported in November 2002 in the Guangdong Province of China, and it spread within China and then into several countries across continents during the first half of 2003  of 4   for SARS of 9.5% , while e people) . There i people) –14. MostMiddle East respiratory syndrome-related coronavirus (MERS-CoV)  , 16. SigR0 has been estimated to be 1  are the gold standard method for assessing the effects of an intervention. Here, patients are prospectively and randomly assigned to treatment or control conditions, allowing for much stronger interpretations to be drawn; however, data from these trials take much longer to collect. Both approaches have proven to be important sources of information in the development of a rapid response to the COVID-19 crisis, but as the pandemic draws on and more results become available from RCTs, more definitive answers are becoming available about proposed therapeutics. Interventional clinical trials are currently investigating or have investigated a large number of possible therapeutics and combinations of therapeutics for the treatment of COVID-19 .The purpose of this review is to provide an evolving resource tracking the status of efforts to repurpose and develop drugs for the treatment of COVID-19. We highlight four strategies that provide different paradigms for the identification of potential pharmaceutical treatments. The WHO guidelines  and a syA variety of symptom profiles with a range of severity are associated with COVID-19 . SimilarIn the early days of the COVID-19 pandemic, physicians sought to identify potential treatments that could benefit patients, and in some cases shared their experiences and advice with the medical community on social media sites such as Twitter . These oMany treatments considered for COVID-19 have relied on a broad-spectrum approach. These treatments do not specifically target a virus or particular host receptor but rather induce broad shifts in host biology that are hypothesized to be potential inhibitors of the virus. This approach relies on the fact that when a virus enters a host, the host becomes the virus’s environment. Therefore, the state of the host can also influence the virus’s ability to replicate and spread. The administration and assessment of broad-spectrum small-molecule drugs on a rapid time course were feasible because they are often available in hospitals, or in some cases, they may also be prescribed to a large number of outpatients. One of the other advantages is that these well-established compounds, if found to be beneficial, are often widely available, in contrast to boutique experimental drugs.In some cases, prior data were available from experiments examining the response of other HCoVs or HCoV infections to a candidate drug. In addition to nonpharmaceutical interventions such as encouraging nonintubated patients to adopt a prone position , knowledOne approach to identifying candidate small-molecule drugs was to look at the approaches used to treat SARS and MERS. Treatment of SARS and MERS patients prioritized supportive care and symptom management . Among t34–40–46–Dexamethasone (9α-fluoro-16α-methylprednisolone) is a synthetic corticosteroid that binds to glucocorticoid receptors , 51. It 54–Despite this initial concern, dexamethasone was evaluated as a potential treatment for COVID-19 . DexametTherapeutics that directly target the virus itself hold the potential to prevent people infected with SARS-CoV-2 from developing potentially damaging symptoms . Such drin vitro analyses of SARS and the SARS-CoV-1 virus, respectively, have been inconclusive , for replication , 72. RdRnclusive . While I unknown . The cur unknown .Flaviviridae and Picornaviridae, through a reduction in plaque formation  is an intravenous antiviral that was proposed by Gilead Sciences as a possible treatment for Ebola virus disease. It is metabolized to GS-441524, an adenosine analog that inhibits a broad range of polymerases and then evades exonuclease repair, causing chain termination –88. Gile–89–a priori, and there are several examples where early enthusiasm was not borne out in subsequent trials. One of the most famous examples of an analysis of whether a well-known medication could provide benefits to COVID-19 patients came from the assessment of chloroquine (CQ) and hydroxychloroquine (HCQ), which are used for the treatment and prophylaxis of malaria as well as the treatment of lupus erythematosus and rheumatoid arthritis in adults   . Howevero-hSLAM) , 111, who-hSLAM) , 113.Retrospective studies and small RCTs began investigating the effects of standard doses of this low-cost, widely available drug. One retrospective study reported that ivermectin reduced all-cause mortality , while a116–131–in vitro and molecular docking analysis  and angiotensin II receptor blockers (ARBs) are among today’s most commonly prescribed medications, often being used to control blood pressure , 147. Inanalysis  but ultianalysis . Data franalysis , but clianalysis . In thisanalysis  was retrlability . As RCTslability , 157 . TCZ is a MAb that was developed to manage chronic inflammation caused by the continuous synthesis of the cytokine IL-6 . IL-6 isCurrently, TCZ is being administered either as a monotherapy or in combination with other treatments in 73 interventional COVID-19 clinical trials . A numbe171–178–TCZ is just one example of a candidate drug targeting the host immune response and specifically excessive inflammation. For example, interferons (IFNs) have also been investigated; these are a family of cytokines critical to activating the innate immune response against viral infections. Synairgen has been investigating a candidate drug, SNG001, which is an IFN-β-1a formulation to be delivered to the lungs via inhalation  that theWhile TCZ is an example of an MAb focused on managing the innate immune response, other treatments are more specific, targeting the adaptive immune response after an infection. In some cases, treatments can utilize biologics obtained directly from recovered individuals. From the very early days of the COVID-19 pandemic, polyclonal antibodies from convalescent plasma were investigated as a potential treatment for COVID-19 , 189. CoA different narrative is shaping up around the use of MAbs specifically targeting SARS-CoV-2. During the first SARS epidemic in 2002, neutralizing antibodies (nAbs) were found in SARS-CoV-1-infected patients , 195. SeTechnological advances in antibody drug design as well as in structural biology massively accelerated the discovery of novel antibody candidates and the mechanisms by which they interact with the target structure. Within just a year of the structure of the SARS-CoV-2 spike protein being published, an impressive pipeline of monoclonal antibodies targeting SARS-CoV-2 entered clinical trials, with hundreds more candidates in preclinical stages. The first human monoclonal neutralizing antibody specifically against the SARS-CoV-2 S glycoprotein was developed using hybridoma technology , where ain vivo using a nonhuman primate model (One of the studied antibody cocktails consists of bamlanivimab and estesevimab. Bamlanivimab (Ly-CoV555) is a human MAb that was derived from convalescent plasma donated by a recovered COVID-19 patient, evaluated in research by the National Institute of Allergy and Infectious Diseases (NIAID), and subsequently developed by AbCellera and Eli Lilly. The neutralizing activity of bamlanivimab was initially demonstrated te model . On the te model . Estesevte model . Etesevite model . To assete model  was initte model . Howeverte model . Based ote model .New England Journal of Medicine shows that the REGN-COV2 antibody cocktail reduced viral load, particularly in patients with high viral load or whose endogenous immune response had not yet been initiated (A second therapy is comprised of casirivimab and imdevimab (REGN-COV2). Casirivimab (REGN10933) and imdevimab (REGN10987) are two monoclonal antibodies against the SARS-CoV-2 spike protein. They were both developed by Regeneron in a parallel high-throughput screening (HTS) to identify neutralizing antibodies from either humanized mice or patient-derived convalescent plasma . In thesnitiated . Howevernitiated . Ongoingnitiated .Sotrovimab is the most recent MAb to receive an EUA. It was identified in the memory B cells of a 2003 survivor of SARS  and was Several potential limitations remain in the application of MAbs to the treatment of COVID-19. One of the biggest challenges is identifying antibodies that not only bind to their target but also prove to be beneficial for disease management. Currently, use of MAbs is limited to people with mild to moderate disease that are not hospitalized, and it has yet to be determined whether they can be used as a successful treatment option for severe COVID-19 patients. While preventing people from developing severe illness provides significant benefits, patients with severe illness are at the greatest risk of death, and therefore therapeutics that provide benefits against severe illness are particularly desirable. It remains to be seen whether MAbs confer any benefits for patients in this category.in vivo, it suggests that some adjustments to therapeutic antibody treatments may be necessary to maintain the efficacy that was reported in previous clinical trials.Another concern about therapeutics designed to amplify the response to a specific viral target is that they may need to be modified as the virus evolves. With the ongoing global spread of new SARS-CoV-2 variants, there is a growing concern that mutations in the SARS-CoV-2 spike protein could escape antibody neutralization, thereby reducing the efficacy of monoclonal antibody therapeutics and vaccines. A comprehensive mutagenesis screen recently identified several amino acid substitutions in the SARS-CoV-2 spike protein that can prevent antibody neutralization . While s223–Several strategies have been employed to try to mitigate the risk of diminished antibody neutralization. Antibody cocktails such as those already holding an EUA may help overcome the risk for attenuation of the neutralizing activity of a single monoclonal antibody. These cocktails consist of antibodies that recognize different epitopes on the spike protein, decreasing the likelihood that a single amino acid change can cause resistance to all antibodies in the cocktail. However, neutralizing resistance can emerge even against an antibody cocktail if the individual antibodies target subdominant epitopes . AnotherThe development of MAbs against SARS-CoV-2 has made it clear that this technology is rapidly adaptable and offers great potential for the response to emerging viral threats. However, additional investigation may be needed to adapt MAb treatments to SARS-CoV-2 as it evolves and potentially to pursue designs that confer benefits for patients at the greatest risk of death. While polyclonal antibodies from convalescent plasma have been evaluated as a treatment for COVID-19, these studies have suggested fewer potential benefits against SARS-CoV-2 than MAbs; convalescent plasma therapy has been thoroughly reviewed elsewhere , 189. Thin vitro or in vivo models, focusing on empirical, function-oriented exploration naive to molecular mechanism  or between a viral pathogen and another illness  are potential candidates, as are drugs that are thought to interact with additional molecular targets beyond those they were developed for . Such reompounds .in vitro. Two polyproteins encoded by the SARS-CoV-2 replicase gene, pp1a and pp1ab, are critical for viral replication and transcription (Pro), a 33.8-kDa SARS-CoV-2 protease that is therefore fundamental to viral replication and transcription. Therefore, it was hypothesized that compounds targeting MPro could be used to prevent or slow the replication of the SARS-CoV-2 virus.One example of the application of this approach to COVID-19 research comes from work on protease inhibitors. Studies have shown that viral proteases play an important role in the life cycle of viruses, including coronaviruses, by modulating the cleavage of viral polyprotein precursors . Severalcription . These pPro. In 2005, computer-aided design facilitated the development of a Michael acceptor inhibitor, now known as N3, to target MPro of SARS-like coronaviruses   –252. TheRS-CoV-2 . N3 was RS-CoV-2 . A librahia coli .In vitro analysis revealed that ebselen had the strongest potency in reducing the viral load in SARS-CoV-2-infected Vero cells , and biological data science can all provide different insights and perspectives for addressing the challenging question of which existing drugs might provide benefits against an emerging viral threat. A symptom management-driven approach allows clinicians to apply experience with related diseases or related symptoms to organize a rapid response aimed at saving the lives of patients already infected with a new disease. Oftentimes, the pharmaceutical agents that are applied are small-molecule, broad-spectrum pharmaceuticals that are widely available and affordable to produce, and they may already be available for other purposes, allowing clinicians to administer them to patients quickly either with an EUA or off-label. In this vein, dexamethasone has emerged as the strongest treatment against severe COVID-19 .Alternatively, many efforts to repurpose drugs for COVID-19 have built on information gained through basic scientific research of HCoV. Understanding how related viruses function has allowed researchers to identify possible pharmacological strategies to disrupt pathogenesis . Some ofAll of the small-molecule drugs evaluated and most of the biologics are repurposed, and thus hinge on a theoretical understanding of how the virus interacts with a human host and how pharmaceuticals can be used to modify those interactions rather than being designed specifically against SARS-CoV-2 or COVID-19. As a result, significant attention has been paid to computational approaches that automate the identification of potentially desirable interactions. However, work in COVID-19 has made it clear that relevant compounds can also be masked by confounding factors, and spurious associations can drive investment in candidate therapeutics that are unlikely to translate to the clinic. Such spurious hits are especially likely to impact hypothesis-free screens. However, hypothesis-free screens may still be able to contribute to the drug discovery or repurposing ecosystem, assuming the computational arm of HTS follows the same trends seen in its experimental arm. In 2011, a landmark study in drug discovery demonstrated that although more new drugs were discovered using target-based rather than phenotypic approaches, the majority of drugs with a novel molecular mechanism of action (MMOA) were identified in phenotypic screens . This pain silico or in vitro that will translate to a successful treatment. While significant progress has been made thus far in the pandemic, the therapeutic landscape is likely to continue to evolve as more results become available from clinical trials and as efforts to develop novel therapeutics for COVID-19 progress.The unpredictable nature of success and failure in drug repurposing for COVID-19 thus highlights one of the tenets of phenotypic screening: there are a lot of “unknown unknowns,” and a promising mechanism at the level of an MMOA will not necessarily propagate up to the pathway, cellular, or organismal level . DespiteOnly very limited testing of candidate therapies was feasible during the SARS and MERS epidemics, and as a result, few treatments were available at the outset of the COVID-19 pandemic. Even corticosteroids, which were used to treat SARS patients, were a controversial therapeutic prior to the release of the results of the large RECOVERY trial. The scale and duration of the COVID-19 pandemic have made it possible to conduct large, rigorous RCTs such as RECOVERY, Solidarity, TOGETHER, and others. As results from these trials have continued to emerge, it has become clear that small clinical trials often produce spurious results. In the case of HCQ/CQ, the therapeutic had already attracted so much attention based on small, preliminary  studies that it took the results of multiple large studies before attention began to be redirected to more promising candidates . In factIn the long term, new drugs specific for treatment of COVID-19 may also enter development. Development of novel drugs is likely to be guided by what is known about the pathogenesis and molecular structure of SARS-CoV-2. For example, understanding the various structural components of SARS-CoV-2 may allow for the development of small-molecule inhibitors of those components. Crystal structures of the SARS-CoV-2 main protease have been resolved , 288. Mu


Conclusions: The AI-based analysis of preoperative OCT-A images of eyes affected by FTMH may be a useful support system in setting up visual acuity recovery prediction. The combination of preoperative SVP and DVP images showed a significant morphological predictive performance for visual acuity recovery.Purpose: To evaluate the 1-year visual acuity predictive performance of an artificial intelligence (AI) based model applied to optical coherence tomography angiography (OCT-A) vascular layers scans from eyes with a full-thickness macular hole (FTMH). Methods: In this observational cross-sectional, single-center study, 35 eyes of 35 patients with FTMH were analyzed by OCT-A before and 1-year after surgery. Superficial vascular plexus (SVP) and deep vascular plexus (DVP) images were collected for the analysis. AI approach based on convolutional neural networks (CNN) was used to generate a continuous predictive variable based on both SVP and DPV. Different pre-trained CNN networks were used for feature extraction and compared for predictive accuracy. Results: Among the different tested models, the inception V3 network, applied on the combination of deep and superficial OCT-A images, showed the most significant differences between the two obtained image clusters defined in C1 and C2  C1 = 66.67 (16.00 SD) and BCVA C2 = 49.10 (18.60 SD,  Artificial intelligence-based analysis of preoperative optical coherence tomography angiography images of eyes affected by a full-thickness macular hole may be useful to support systems in setting up visual acuity recovery prediction. The combination of preoperative superficial plexus and deep plexus images showed a significant morphological predictive performance for visual acuity recovery.The role of advanced imaging analysis is becoming increasingly important in daily clinical practice and biomedical research due to recent advancements of radiomics and other artificial intelligence (AI)-based image analysis ,2. Clinical decision support systems are now routinely incorporated into patient evaluation workflows, often including quantitative, multimodal imaging assessments integrating several variables coming from different omics domains according to the most innovative paradigms of personalized medicine . Ophthalmology, and more specifically retinal surgery, are exemplar subspecialties in which a remarkable use of several data sources  is performed for the definition of comprehensive diagnosis, prognostic stratification, and follow-up strategies of patients affected by ocular diseases ,5. Owing to the application of deep learning (DL) techniques, promising AI-based models have recently been developed in ophthalmology, which incorporates different types of imaging to predict diabetic retinopathy , glaucomThe most common image modality used in ophthalmology is to date represented by fundus photography. Nevertheless, fundus photography has also been characterized by increasing use in recent years, including screening for blindness in diabetic eyes and for glaucoma disease Considerable interest has also been addressed to the introduction of high-resolution optical coherence tomography (OCT), not only in the ophthalmology scientific community but also for image scientists, because OCT is considered a reliable in vivo histological retinal section surrogate with up to 3.3 µm of axial resolution and significant potential for innovative AI-based investigations ,11.B-scan OCT images were recently evaluated in full-thickness macular holes (FTMH) for outcome prediction after surgery, paving the way towards the introduction of a more systematic application of AI in ophthalmological surgery . The intDespite the promising quality of retinal vascular imaging, some images are still not immediately interpretable for the clinician due to their very recent introduction in clinical practice and the scarce available evidence . As an eOther vascular anomalies are constantly discovered with OCT-A .In FTMH disease, the indication for surgery is often only supported by the reduction of visual acuity or the degree of metamorphopsia referred by the patient . AlthougVitreoretinal surgeons are therefore struggling to identify the most reliable morpho-functional biomarkers for surgical outcome prediction in order to identify the best therapeutic approach. The use of microperimetry recently provided useful information to determine the function-structure correlation before and after vitreoretinal surgery , but relThe functional predictive information on vascular variability obtained through the qualitative analysis of OCT-A after FTMH surgery was recently described by our group showing a significant improvement of BCVA when better restoration of the vessel density (VD) was observed, especially of the deep vascular plexus (DVP) towards the superficial vascular plexus (SVP) . Interestingly, Hu et al. observed that DL modeling of preoperative macular OCT images allows predicting postoperative FTMH status after vitrectomy and internal limiting membrane (ILM) peeling.The aim of this study was to assess the 1-year visual acuity predictive performance of an unsupervised DL model using preoperative OCT-A scans of both, DVP and SVP.This prospective, observational, cross-sectional study evaluated 35 eyes of 35 patients affected by FTMH evaluated with preoperative and postoperative structural OCT and OCT-A. The surgical procedure was performed by one surgeon for all the enrolled eyes. The 25-Gauge Pars Plana Vitrectomy (PPV-25G) with inner limiting membrane removal and endotamponade of sulfur hexafluoride gas (SF6) was applied. This study adheres to the Declaration of Helsinki , and written informed consent was obtained from all patients prior to participation in the study. Institutional ethics committee approval was obtained at the Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del “Sacro Cuore”of Rome, Italy (ID number: 3680). Patients were considered for this study if they were cooperative and had an FTMH detected by structural spectral-domain OCT (SD-OCT). The acquired images had to be of adequate quality in order to define the details of vascular layers at attending ophthalmologist judgment. Possible segmentation errors were adjusted in editing modalities and propagated to all B-scans.Exclusion criteria were media opacity and concomitant diseases such as diabetic retinopathy, vein or artery occlusion, and glaucoma. All patients underwent a baseline ophthalmic examination, including medical and ocular history, family medical history, measurement of BCVA, slit-lamp examination of the anterior and posterior segments, measurement of intraocular pressure, and dilated fundus examination. The RS-3000 Advance 2  was used to acquire SD-OCT and OCT-A. All scans were centered on the fovea based on a live scanning laser ophthalmoscopy (SLO) image. All OCT-A scans were stetted at 3 × 3 mm centered on the macula. After the complete acquisition, high-definition images of SVP and DVP were exported and used for the DL analysis..Convolutional neural networks (CNN) are a set of machine learning models that take images as input and typically produce a classification, generally represented by a continuous variable or other images as output, depending on the network architecture and the specific learning task they were trained for. In recent years, a variety of CNN architectures were developed and trained in many different computer vision scenarios, e.g., from image classification to semantic segmentation and image generation ,24,25. ATo date, several CNN are available to advance bio-imaging studies, which can be effectively employed for image characterization tasks. For instance, pre-trained deep CNN can be used for feature extraction from images, by removing the last classification layer of the network keeping the last dense layer, which represents the numerical categorization of the image according to the pre-trained model. As described in Layer after layer, the input image is then transformed into numeric vectors that capture the geometric characteristics of every possible sub-region of the image and patterns of pixel intensities in the same sub-regions at different levels of abstraction. Altogether, these embedded features provide a quantitative description of the image, which is completely automated, as opposed to the hand-crafted feature extraction often used in traditional radiomics analysis . The advantage of this technique is the exploitation of highly complex models that contain billions of parameters without the need to re-train them from scratch, which would require a massive number of training images and significant computational resources. In fact, the model weights and coefficient are already determined and can be eventually waked for the particular task. For the scope of this analysis, several pre-trained CNN networks were used for feature extraction and compared in terms of accuracy for the specific predictive task. The following networks have been used: Inception V  3, VGG-1Sample size calculations were not necessary because our analysis is based on a computer vision perspective using classification models of radiomics images, analytical tools of machine learning, and deep learning. To mitigate the possible effect of reduced sample size, we opted for an unsupervised classification algorithm (clustering).The feature extraction process was applied to two different classes of OCT-A images, namely superficial and deep, acquired in the preoperative settings. t-test both for deep and superficial images. The same clustering process was then repeated on a merged dataset with half of the features obtained from deep images and half from superficial images. Again, the 1-year visual acuity score was compared between the clusters. The rationale of this approach was that a clustering on the complete superficial and deep datasets could take the best of both image types in terms of total OCT characterization. For this reason, the t-test p-values were compared to define which cluster configuration achieved the highest level of separation on the 1-year visual acuity score. The comparison was also extended to the four different CNN networks to assess whether the network architecture had an influence on the OCT-A images characterization. Statistical analysis was performed in Python version 3.7 and Orange version 3.26.For each CNN tested, the process led to the creation of two different datasets―one for the superficial and another for the deep OCT-A images―which have a number of rows equal to the number of images included in the study, and a number of columns equal to the number of features extracted by the particular CNN. A distance matrix was then computed for the two datasets using the cosine distance metric, which was given as input to a hierarchical clustering algorithm to assign each image to one of two clusters of similar images. Once the images had been assigned to a cluster, the distribution of 1-year visual acuity score was compared between the clusters with Student’s Thirty-five eyes of 35 patients with FTMH were analyzed with preoperative OCT-A images. SVP and DVP high-quality images were collected and exported for the analysis. For a single image type, the feature extraction step produced the following number of features, depending on the network architecture: 4096 for VGG-16 and VGG-19, 2048 for Inception V3, and 1000 for the SqueezeNet. The distribution of baseline visual acuity score had a mean of 29.15 and a 13.10 standard deviation, while the 1-year BCVA score distribution had a mean of 56.63 and a standard deviation of 19.6 . Table wThe 1-year change in visual acuity score is therefore 27.49 letters with an 18.5 letters standard deviation. Based on the features extracted by the four CNNs, the hierarchical clustering algorithm with a number of target clusters equal to two assigned a mean of 12.5 images to cluster 1(C1) and 22.5 images to cluster 2 (C2). The distribution of the 1-year visual acuity score is reported in Clustering based on SqueezeNet features from DVP assigned just one image to C1, thus its value is not included in the aforementioned table.The most significant mean separation between the 1-year visual acuity score distributions on the two clusters defined by C1 and C2 was associated with the Inception V3 network applied on the combination of deep and superficial OCTs. The DL clustering assignment for the 35 superficial and deep plexus images, according to the features extracted with the Inception V3 network, are reported in t-test p-value equal to 0.005 . Although the possible significant variables can be multiple and originate in different data and knowledge domains , in most cases, the reason why some eyes improve visual acuity after surgery while others persist stable or even get worse, with apparently the same morphological image features, still remains obscure. Our findings suggest that the DL-based analysis of OCT-A images, acquired in the pre-operative setting may be of support to create promising decisional support systems.To our knowledge, this is the first study including DL prediction in OCT-A images evaluation in the peer-reviewed literature .The combination of superficial and deep preoperative OCT-A images showed how the Inception V3 and SqueezeNet models were significant morphological predictors for visual acuity recovery in eyes affected by FTMH, and the different identified clusters were peculiar for visual acuity restoration after 1-year, disclosing a further potential role for AI applications on larger validation cohorts of patients. Indeed, our results reported that for C1, the probability increases with increased BCVA value while the opposite is true for C2 since it contains all the lower BVCA values at 1 year. These preliminary findings may be useful in ophthalmological clinical practice, as to date retinal specialists are inaccurate and demonstrate a significantly lower sensitivity in detecting retinal vascular details information in the case of FTMH when compared to deep learning models. Furthermore, the combination of SVP and DVP datasets, in terms of total features and network architecture characterization, seems to achieve a higher accuracy.Besides the promising predictive performances, this exploratory experience still presents different pitfalls that may limit its immediate translational applications. First of all, the limited total number of used images scanned. This choice was imposed by the unavailability of patients with images of adequate quality and a sufficient follow-up length. Furthermore, the absence of an external validation cohort reduces the methodological robustness of our model. In order to address these issues, prospective validation studies are ongoing in order to confirm model robustness and replicability on more numerous patient cohorts. Further in this experience, we decided to focus on the informative value of the images only, in order to apply a more straightforward unsupervised learning experience. The integration of other variables  will be taken into account in future studies with a specific focus on composite biomarkers. According to a recent study by Obata et al. , postopeIn the near future, we will evaluate the potential of our algorithm also on B-scan and then compare the predictive responses of both.Despite the aforementioned limitations, these innovative AI-based applications may successfully support ophthalmologists in functional predictive tasks and clinical decision making, representing a promising and still unexplored knowledge domain.The application of the described model in the clinical setting of FTMH surgery represents the future perspective of this investigation. This real-world validation will provide the final proof of the model’s efficacy in predicting post-operative functional outcomes  of macular hole surgery.


Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases  and preprint servers . In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images  in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic. Coronavirus disease 2019 (COVID-19) was firstly reported in December 2019 In severe infectious disease outbreaks, both hospitals and physicians are suffered with the increasing workloads that weaken their ability to classify and hospitalize the suspected patients. The previous reports indicated that some of patients with early coronaviral infection were negative for CT Fig. 1, it is mainly compiled from two aspects: machine learning (ML) and deep learning (DL), including electronic medical records, and medical images , in clinical COVID-19 diagnosis.Artificial intelligence (AI) has been deployed at various levels of the health care system, including diagnosis, public health, clinical decision making, and therapeutics Table 1.The potential applications of ML for COVID-19 have been previously described The first priority of ML was suggested as technical support for early detection and diagnosis of infections. A recent study demonstrated that the more accurate diagnosis could be generated using a computational model trained on large clinical datasets Chest CT has been used to evaluate the patients with suspected SARS-CoV-2 infection. AI system had equal sensitivity as compared to a senior thoracic radiologist. The radiologists with less expertise in chest imaging demand AI-assisted screening. In a test set of 279 patients, an AI algorithm combining chest CT presentation correctly identified 68%, while radiologists classified all of these patients as COVID-19 negative Developed ML models are suitable for surveillance efforts to predict the SARS-CoV-2 infection risk of patients with severe diseases. Monahan et al reported that two unique ML models were generated to predict the risk of the hemodialysis (HD) patients having the undetected SARS-CoV-2 infections Table 2.DL, as a subset of ML, has been explored extensively in the diagnosis of COVID-19, especially in the field of lung detection images, including CT images, X-ray images and ultrasound images. The details are summarized in Wu et al developed a DL-based screening framework for coronavirus through a multi-view chest CT imaging Hybrid systems show the higher accuracy than a single model. Hasan et al generated a hybrid system that combined Q-deformed entropy and DL features (QDE-DF) with LSTM from extracting deep features of CT images Multitask DL based model can be used to detect COVID-19 lesions on CT scans. A multitask DL model, including segmentation, classification and reconstruction, was used to detect COVID-19 patient and segment COVID-19 lesion from chest CT images Moreover, transfer learning has been applied for the early diagnosis of coronaviruses based on X-ray imaging. Apostolopoulos and Bessiana proposed a system for the automatic diagnosis of COVID-19 cases in which five CNN variants  were used to analyze a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents Lung Ultrasonography (LUS) has been used for the detection and management of acute respiratory disorders. A recent study shows that DL techniques may assist clinician for the analysis of LUS images from COVID-19 patients AI has great potential and opportunity for rapid analysis of large amounts of data. It has played an important role in the prevention of COVID-19 outbreak It is noteworthy that although some patients infected with COVID-19 are asymptomatic, they have the potential to become transmitters of the virus Biomedical imaging  enables to visualize symptoms of pneumonia. Image processing techniques are attractive in the areas of biomedicine and cancer diagnosis False negative rate is usually high with the laboratory tests, such as nucleotide RT-PCT AI model may be as accurate as experienced physicians at diagnosing COVID-19. In this review, we discuss the challenges and perspectives of ML and DL in the COVID-19 as well as the need for further research. The clinical application of AI in the diagnosis of COVID-19 is promising, and additional extensive research is required.


After more than one year of the COVID-19 pandemic, antiviral treatment options against SARS-CoV-2 are still severely limited. High hopes that had initially been placed on antiviral drugs like remdesivir have so far not been fulfilled. While individual case reports provide striking evidence for the clinical efficacy of remdesivir in the right clinical settings, major trials failed to demonstrate this. Here, we highlight and discuss the key findings of these studies and underlying reasons for their failure. We elaborate on how such shortcomings should be prevented in future clinical trials and pandemics. We suggest in conclusion that any novel antiviral agent that enters human trials should first be tested in a post-exposure setting to provide rapid and solid evidence for its clinical efficacy before initiating further time-consuming and costly clinical trials for more advanced disease. In the COVID-19 pandemic this might have established remdesivir early on as an efficient antiviral agent at a more suitable disease stage which would have saved many lives, in particular in large outbreaks within residential care homes. With the rise of the SARS-CoV-2 pandemic, substantial hope was initially placed in repurposing existing antivirals, in particular the nucleoside analog remdesivir, for the potential treatment of COVID-19 . The inhThe clinical benefits observed with remdesivir show striking similarities to the known effects of the neuraminidase inhibitor oseltamivir, which is used for treating influenza A virus (IAV) infections. Despite oseltamivir’s high antiviral activity against IAV in vitro, large clinical studies have demonstrated that the drug has no measurable benefit on the course of disease if treatment is initiated later than two days after the onset of symptoms . StartinAll acute viral infections are characterized by a rapid increase in the number of virus-infected cells upon virus encounter, followed by a gradual elimination of the infected cells by the innate and adaptive immune system. The onset of clinical symptoms is directly linked to the activation of the innate immune system, in particular the production of types I, II, and III interferons (IFN) ,14. TheiSimilarly to IAV infection, the highest viral load and infectivity for SARS-CoV-2 are observed +/−1 day around the day of symptom onset . Both thAn important difference between oseltamivir and remdesivir which has to be considered is their different modes of action: oseltamivir inhibits viral neuraminidase activity, thereby interfering with the release of new virus particles and infection of new cells. Viral gene expression and replication is not affected. In contrast, remdesivir reversibly inhibits the viral RNA-dependent RNA polymerase, which is required for both viral genome replication and expression of all viral mRNAs ,9. Remde6-fold decrease in viral load. After a rebound in viral load after the first 10-day course, this eventually led to clinical resolution and viral clearance in this persistently infected patient [Unfortunately, the key question of whether remdesivir has sufficient antiviral activity against SARS-CoV-2 for successful preemptive therapy or in early disease still remains unanswered. The observed shortening of the time to recovery observed in clinical studies provides strong evidence that it efficiently inhibits SARS-CoV-2 replication in COVID-19 patients. This is further supported by a preclinical study that demonstrated the efficacy of remdesivir in rhesus macaques when used 12 h after inoculation . In addi patient . This siIn general, the following therapeutic approaches for SARS-CoV-2 can be considered: (i) post-exposure prophylaxis (PEP) during the incubation period after documented SARS-CoV-2 exposure, (ii) early treatment after confirmed SARS-CoV-2 infection without any or with early symptoms, or (iii) treatment of advanced COVID-19. Antiviral approaches are most effective in the first two settings. Currently, no drug is approved for SARS-CoV-2 PEP. While hydroxychloroquine did not demonstrate any efficacy in a PEP setting , MK-4482During the first year of the pandemic, the majority of COVID-19-related deaths resulted from large outbreaks in residential care homes. These were commonly associated with death rates of 10–20% and higher . Due to The availability of vaccines heralds the end of the pandemic. However, it will take many months, if not years, until herd immunity is achieved worldwide, and spike protein mutations of virus escape variants make herd immunity by effective vaccination even more challenging. Furthermore, not everyone will be vaccinated, and the role of vaccine non-responders is still unclear. With lockdowns and contact restrictions presumably coming to an end in early summer 2021 in the western hemisphere, we will inevitably see an increasing number of infections in children and non-vaccinated or unsuccessfully vaccinated adults next winter. With vaccination progressing rapidly particularly in the residential care homes, the number of high-risk patients that can be identified very early in infection or even prior to disease onset is rapidly decreasing. Similar to IAV infection, more and more patients will thus only be diagnosed > 48 h after the onset of symptoms. In September 2020, Gilead initiated a phase III clinical trial (NCT04501952) to evaluate the efficacy and safety of remdesivir treatment of early stage COVID-19 in an outpatient setting. However, in April 2021, Gilead made the decision to stop recruitment for this study based on changes in the COVID-19 landscape, the necessity of multiple day intravenous infusion treatment, and promising oral antivirals against SARS-CoV-2 in clinical trials. Patients already enrolled in the study will continue to be followed and the study remains blinded. We may thus soon learn whether remdesivir could have been a major game changer by substantially reducing COVID-19-related deaths in residential care homes.Any antiviral agent will be most effective when applied early in SARS-CoV2 infection as it can then interfere with multiple rounds of virus replication. A key take-home message to ensure effective control of future pandemics is that any novel antiviral agent entering human trials should first be tested in a post-exposure setting, id est in PCR-positive individuals prior to the onset of symptoms. Here, clinical efficacy can be demonstrated (i) much more rapidly, (ii) based on a reduction in infection-related death (rather than a shortening of disease duration by a few days) and (iii) based on a much smaller number of patients, for example, in 150–200 inhabitants of a single residential care home that is experiencing a major outbreak associate with a 10–20% death rate. Only then should new antivirals be tested for clinical efficacy in more advanced viral disease stages.


Thus, echocardiographic investigations should be revised for the presence/absence of TTS.Echocardiographic images in Figure 2 strongly suggest the presence of Takotsubo syndrome (TTS).A further limitation of the study is that myocardial infarction was not excluded by coronary catheter angiography or coronary computed tomography. When suspecting TTS, it is crucial to demonstrate that systolic dysfunction is not due to coronary heart disease.A further limitation is that nothing is reported about the degree of lung involvement and about right ventricular function. We should be told if the patient required artificial ventilation because of cardiac compromise or because of SARS-CoV-2 pneumonia or acute respiratory distress syndrome. Elevated inflammatory parameters are more likely attributable to pulmonary infection than to myocarditis.2 we should know if the patient underwent neurologic workup or not. Of particular interest are the results of cerebral MRI, electroencephalography, and eventually cerebrospinal fluid investigations. Is it conceivable that systolic dysfunction was attributable to brain-heart interactions? Emotional stress is a well-appreciated trigger of TTS,3 and SARS-CoV-2 is frequently associated with TTS.4On hospital day 4, the patient is reported to have become agitated, disorientated, and confused. Because cerebral involvement is a frequent complication of SARS-CoV-2 infections,5 The patient was treated on hospital day 1 with azithromycin, ceftriaxone, and prednisolone. We should be told why 2 antibiotics were applied simultaneously.The rationale for application of colchicine is unclear. Colchicine for putative SARS-CoV-2–associated myopericarditis is ineffective, particularly if no proactive anti-inflammatory therapy is given prior to colchicine.Overall, the case report has several limitations which challenge the results and their interpretation. The diagnosis of myopericarditis remains unsupported. The patient most likely had SARS-CoV-2–associated TTS, and inflammatory parameters were attributable to the pulmonary infection. The pandemic is no reason to refrain from thorough diagnostic workup and appropriate exclusion of differentials.


Testing and isolation have been crucial for controlling the COVID-19 pandemic. Venezuela has one of the weakest testing infrastructures in Latin America and the low number of reported cases in the country has been attributed to substantial underreporting. However, the Venezuelan epidemic seems to have lagged behind other countries in the region, with most cases occurring within the capital region and four border states. Here, we describe the spatial epidemiology of COVID-19 in Venezuela and its relation to the population mobility, migration patterns, non-pharmaceutical interventions and fuel availability that impact population movement. Using a metapopulation model of SARS-CoV-2 transmission dynamics, we explore how movement patterns could have driven the observed distribution of cases. Low within-country connectivity most likely delayed the onset of the epidemic in most states, except for those bordering Colombia and Brazil, where high immigration seeded outbreaks. NPIs slowed early epidemic growth and subsequent fuel shortages appeared to be responsible for limiting the spread of COVID-19 across the country. The incidence of reported COVID-19 cases in Venezuela is one of the lowest in Latin America. As of 17 May 2021, a total of 216,415 cases (0.9% of the population) and 2411 deaths have been officially confirmed. Testing, however, has also been among the lowest in the region   and fuel availability during the first wave of the epidemic (13 March–21 December 2020). Using a susceptible-exposed-infectious (SEI) metapopulation model, we explore mobility patterns and their relationship with the spatial distribution of observed cases. We further analyzed the effect of mobility restrictions (shelter-in-place orders) and fuel shortages to identify the major drivers of the Venezuelan epidemic.https://www.qgis.org/es/site/ (accessed on 25 June 2021)) and the heatmap was constructed in the ggplot package from R free-software . We further estimated the instantaneous reproductive numbers, Data from Venezuela were obtained from official reports and corrected using the percent positivity to account for substantial underreporting  8]. To . To 8].  package  for R (Fcoronavirus.jhu.edu/map.html (accessed on 25 June 2021)) and were modelled with no delay.To infer SARS-CoV-2 dynamics, we adapted a metapopulation model with a susceptible- exposed-infectious (SEI) compartmental structure that accounted for undocumented infections  10]. Th. Th10]. function   of 3% . For ColTo explore values of daily travelers that would reproduce the observed local epidemics, we investigated a range of daily movement rates by modifying the proportionality constant, enezuela , of a grd Brazil , to gened Brazil .We explored the effects of NPIs and gasoline availability on population mobility and contagion rates. Interventions in Venezuela included shelter-in-home orders, closure of parks and public offices and prohibition of commercial or social activities in addition to a substantial reduction of domestic air and terrestrial traffic. A 7 × 7 intermittent scheme consisting of seven-day lockdowns every 15 days was implemented from mid-June to mid-November during 2020. The residence time from Google Mobility was used as an indicator of population mobility and measures the excess hours spent at home (sleeping location), relative to that time spent at home before COVID-19 restrictions. As a proxy for gasoline availability, we estimated the fraction of gas stations open in a given week from country-wide surveillance [A wavelet analysis was performed to investigate the periodicity of the residence time and the reproductive number using Wavelet Test for MATLAB . This paChanges in mobility can be driven by lockdown/relaxation switches, or by longer term responses not associated with these NPIs. We therefore decomposed the residence time into its trend and periodical signal using moving averages estimated by local polynomial regression fitting (stl package in R). We assumed that the periodic component resulted from the variation in mobility associated with compliance to NPIs, while the trend describes longer term variation independent of NPIs. By comparing regression models using these two variables, we assessed the relative contribution of the bi-weekly interventions and any longer-term changes in residence time to the instantaneous reproductive number The official record describes the epidemic curve in Venezuela initially at low incidence between March and mid-May 2020, followed by a steady increase from 15 May onwards, which levelled off from mid-August 2020. Declines in case incidence were evident from late September until the end of 2020 a.The first wave of the COVID-19 epidemic showed substantial geographical heterogeneity . The epiIn states where the epidemic initially started , model simulations seeded as described in The wavelet power spectrum of the residence time revealed high-power signals with periods of 7 and 15 days between mid-June and mid-November 2020 when the 7 × 7 shelter-in-place scheme was implemented a. The 7-Comparison between regression models indicated that excess time at home was a weaker explanatory variable for the reproductive number than its trend component. A lower root mean square error for the trend component (RMSE = 0.1786) compared to the original variable (RMSE = 0.1944) indicates that the performance of the regression model improved when the periodic component was removed from the time series. This suggests that variations observed in The trend component in residence time decreased significantly as gasoline became available in a greater fraction of stations a. A bett at home b, wherea at home c. This sIsolation of symptomatic and asymptomatic persons and restrictions on population mobility have been critical for controlling the COVID-19 pandemic. In Venezuela, where contact tracing has been poor due to limited testing capacity, national cyclic lockdowns were adopted by the government as the main NPI for interrupting transmission. Despite low compliance to these national stay-at-home orders, the epidemic in Venezuela was substantially delayed, compared to other countries in the region. Although the substantial underreporting of infections and deaths indicate that a largely unobserved COVID-19 epidemic occurred in Venezuela, we show that severe shortages of gasoline delayed the initial within-country spread of SARS-CoV-2, mitigating its impact during the first epidemic wave.In many countries, COVID-19 initially spread from focal epicenters into neighboring regions, generating a gradual decrease of cases with distance ,17,18. OAlthough statistics on absolute mobility within Venezuela are lacking, several factors are thought to have substantially reduced mobility, including paralysis of ground transportation due to fuel shortages and an absence of vehicle replacement parts  . In April 2021, the number of deaths due to respiratory infections reported in hospitals almost doubled that observed during the first peak in August 2020 . We therefore caution that unless steps are taken to rebuild the failing public health infrastructure, the death toll due to COVID-19 is expected to increase. Moreover, Venezuela is falling behind in vaccination. While concerns are mounting about the rise of more contagious variants , the vac


Coronavirus disease 2019 (COVID-19) pandemic has posed a major public health crisis all over the world. The role of chest imaging, especially computed tomography (CT), has evolved during the pandemic paralleling the accumulation of scientific evidence. In the early stage of the pandemic, the performance of chest imaging for COVID-19 has widely been debated especially in the context of comparison to real-time reverse transcription polymerase chain reaction. Current evidence is against the use of chest imaging for routine screening of COVID-19 contrary to the initial expectations. It still has an integral role to play, however, in its work up and staging, especially when assessing complications or disease progression. Chest CT is gold standard imaging modality for COVID-19 pneumonia; in some situations, chest X-ray or ultrasound may be an effective alternative. The most important role of radiologists in this context is to be able to identify those patients at greatest risk of imminent clinical decompensation by learning to stratify cases of COVID-19 on the basis of radiologic imaging in the most efficient and timely fashion possible. The present availability of multiple and more refined CT grading systems and classification is now making this task easier and thereby contributing to the recent improvements achieved in COVID-19 treatment and outcomes. In this article, evidence of chest imaging regarding diagnosis, management and monitoring of COVID-19 will be chronologically reviewed. Chest CT is gold standard imaging modality for COVID-19 pneumonia; in some situations, chest X-ray or ultrasound may be an effective alternative.Chest imaging is not recommended for routine screening of COVID-19 in a resource-abundant environment.Chest imaging is useful for assessing complications, disease progression, and prognostication of COVID-19.Proposed chest CT classification systems are useful for stratifying cases suspected of COVID-19.The outbreak of coronavirus disease 2019 (COVID-19) has begun in Wuhan, Hubei province, China, and rapidly spread all over the world .The role of chest imaging, especially computed tomography (CT), has evolved during the pandemic paralleling the accumulation of scientific evidence. The performance of chest imaging for COVID-19 has widely been debated since the early period of the pandemic especially in the context of comparison to real-time reverse transcription polymerase chain reaction (RT-PCR). In this article, evidence of chest imaging regarding diagnosis, management and monitoring of COVID-19 will be chronologically reviewed.Patients with SARS-Cov-2 infection can experience a diverse range of clinical presentations, from no symptoms to acute respiratory distress syndrome (ARDS), septic shock, and/or multiple organ failure , 5. SARSGiven these limitations of RT-PCR, the use of chest CT was widely debated since the early period of the pandemic, especially in the context of replacing RT-PCR as a diagnostic tool , 13. CheHowever, problems were noted in many of the early studies published within 1 months of the pandemic: (1) Patient background was not specified; (2) disease severity of the cohort was biased toward severe and hospitalized cases; (3) the indications for performing chest CT scan were not described; (4) a definition of positive chest CT findings was not provided ; (5) nonuniformity of the reference standard . AntitheBased on a risk–benefit analysis including diagnostic performance, medical cost, precaution issue, and risk of radiation exposure, medical specialty societies published position statements against the use of chest CT for screening of COVID-19 including the American College of Radiology (ACR), the Society of Thoracic Radiology (STR), and the American Society of Emergency Radiology (ASER) in March to April, 2020 , 25, 26.Chest X-ray used to be deemed less useful than chest CT because of their lower sensitivity in the diagnosis of subtle parenchymal abnormalities and limited ability to help differentiate parenchymal patterns . Chest CThe sensitivity of chest X-ray depends mainly on two factors, i.e., symptom severity and disease stage , 34. In In summary, for the general population, chest X-ray is not recommended as the first-line imaging modality for early disease or asymptomatic or mildly symptomatic patients because of limited sensitivity compared to CT , 38. In With experience lung ultrasonography can be as useful as chest CT and superior to standard chest X-ray for evaluation of pneumonia and/or adult respiratory distress syndrome . It has Ultrasonography artefacts arising from the chest wall and pleural surfaces can provide valuable information about diverse lung pathologies either correlating or not correlating with the existing lung pathology of COVID-19 . The norA meta-analysis by Barssoum et al. showed a sensitivity of lung ultrasound of 68–93.3% and of NPV of 52–94.1%, highlighting the value of lung ultrasound as a screening test to rule out COVID-19 pneumonia –47. In cOne unavoidable issue of the early radiological studies of COVID-19 was the subjectivity of image interpretation because of the insufficient evidence available . In addiAccumulation of evidence has enabled radiologists to classify imaging findings by different specificities and establish standard reporting and CT categorization systems based on the probability of COVID-19 . To dateOn March 21, 2020, the Radiological Society of North America (RSNA) advocated an expert consensus statement on reporting a standard nomenclature and imaging classification for COVID-19 pneumonia (RSNA classification) made up of four categories   On April 27, a working group of the Dutch Radiological Society proposed the COVID-19 reporting and data system (CO-RADS) to facilitate the advances in and worldwide dissemination of COVID-19 related information and tools . As showOn April 28, researchers of the University of Southern California devised a different structured reporting system based on a review of 37 published papers on the chest CT findings of COVID-19 entitled the COVID-19 imaging reporting and data system (COVID-RADS) that divides the CT findings into five categories  .The diagnostic performance of BSTI classification of chest X-ray for symptomatic patients was evaluated in 4 studies from the UK –58. The The diagnostic performance of BSTI classification of chest CT was investigated in 2 studies from the UK and Japan , 55. A cInterobserver agreements for BSTI classification were studied by one study each for chest X-ray and chest CT , 55. ForDiagnostic performance of the RSNA classification was evaluated for symptomatic patients with reference to RT-PCR as summarized in Table Interobserver agreements could not be directly compared because of the difference in methodology, indicators, or observer experiences among studies , 64, 68.Factors underlying interobserver disagreements were investigated in these studies. One study revealed that the presence of co-existing lung diseases affected interobserver agreements because COVID-19 pneumonia often mimics acute aggravation of interstitial pneumonia (IP) or emphysema when superimposed on them . AnotherDiagnostic performance of CO-RADS was evaluated in both symptomatic and asymptomatic patients with reference to RT-PCR as summarized in Table Regarding the diagnostic performance for asymptomatic patients, a retrospective cohort study from a pandemic area in Belgium included 859 asymptomatic and 1138 symptomatic patients with suspected COVID-19 . The senIn relation to the duration of symptoms, CO-RADS achieved the highest diagnostic performance in patients with a symptom duration of 2–7 days, followed by those with symptom durations of more than 7 days and less than 2 days . Abdel-TInterobserver agreements of CO-RADS also vary in methods among studies. A cohort study by de Bellini et al. recruited 9 radiologists and reported substantially varied results between different levels of experience with a Fleiss’ kappa of 0.38–0.66 . A cohorCO-RADS is a detailed categorization system but has the one potential limitation of complexity of its GGO categorization . To resoAnother possible solution to avoid the complexity of CO-RADS categorization is to an artificial intelligence (AI)-based algorithm created by the authors of CO-RADS original article . This soOnly a single study evaluated the performance of COVID-RADS . The senThe proportion of negative CT findings for pneumonia  was roughly 5–15% in symptomatic patients with COVID-19 . This peEach of the proposed standard reporting system has its own pros and cons. The RSNA and BSTI classifications are easy to interpret and categorize based on gestalt imaging interpretation of CT, as their categories are assigned according to specific patterns . One disDefining disease stage and predicting and monitoring disease progression are major roles of chest imaging. A typical pattern of disease progression of COVID-19 starts as subpleural non-segmented GGOs or small rounded GGOs in the middle layer of the lung parenchyma that distribute unilaterally or bilaterally, culminating in a crazy-paving pattern and subsequent consolidation , 82. AccSome imaging findings were revealed to be useful to predict the clinical course or prognosis of COVID-19. Columbi et al. evaluated the percentage of well-aerated lung area visually or with a computer-aided method, in which normal opacity area less than 71–73% was associated with ICU admission or death with odds ratio (OR) of 5.4 . They alSimilar results were obtained from studies that investigated chest X-ray on admission. Toussie et al. scored chest X-ray on a 7-point scale for each lung in young and middle-aged patients . They shOne notable fact to keep in mind when interpreting evidence regarding the prognostic value of the initial chest CT or X-ray is that these studies were based on patients with moderate-to-severe lung involvement. The relationship between the degree of lung involvement in the initial chest imaging and prognosis is mostly applicable to asymptomatic or mildly symptomatic patients from the authors’ experience. However, in the authors’ experience, in some patients with mild symptoms but especially with risk factors, acute aggravation is often experienced after about 1 week of symptom onset, often resulting in death inside or outside the hospital. Therefore, caution is needed when applying the evidence to individual patients, and close monitoring is recommended for high-risk patients whose symptoms were even mild or the initial chest imaging findings were minimal.CO, which was more frequent in those with fibrotic changes on CT [129Xe MRI in dyspneic patients at 3-months after COVID-19 pneumonia [CT abnormalities may persist after recovery of COVID-19. Han and Fan et al. reported 6-month follow-up results of severe COVID-19 pneumonia . Surprises on CT . Grist eneumonia . They shneumonia .Chest imaging is not recommended for routine screening of COVID-19 in a normal clinical situation. It still has an integral role to play, however, in its work up and staging, especially when assessing complications or disease progression. The most important role of radiologists in this context is to be able to identify those patients at greatest risk of imminent clinical decompensation by learning to stratify cases of COVID-19 on the basis of radiologic imaging in the most efficient and timely fashion possible. The present availability of multiple and more refined CT grading systems and classification is now making this task easier and thereby contributing to the recent improvements achieved in COVID-19 treatment and outcomes.