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The pandemic of coronavirus disease (COVID)-19 is a global threat, causing high mortality, especially in the elderly. The main symptoms and the primary cause of death are related to interstitial pneumonia. Viral entry also into myocardial cells mainly via the angiotensin converting enzyme type 2 (ACE2) receptor and excessive production of pro-inflammatory cytokines, however, also make the heart susceptible to injury. In addition to the immediate damage caused by the acute inflammatory response, the heart may also suffer from long-term consequences of COVID-19, potentially causing a post-pandemic increase in cardiac complications. Although the main cause of cardiac damage in COVID-19 remains coagulopathy with micro- (and to a lesser extent macro-) vascular occlusion, open questions remain about other possible modalities of cardiac dysfunction, such as direct infection of myocardial cells, effects of cytokines storm, and mechanisms related to enhanced coagulopathy. In this opinion paper, we focus on these lesser appreciated possibilities and propose experimental approaches that could provide a more comprehensive understanding of the cellular and molecular bases of cardiac injury in COVID-19 patients. We first discuss approaches to characterize cardiac damage caused by possible direct viral infection of cardiac cells, followed by formulating hypotheses on how to reproduce and investigate the hyperinflammatory and pro-thrombotic conditions observed in the heart of COVID-19 patients using experimental in vitro systems. Finally, we elaborate on strategies to discover novel pathology biomarkers using omics platforms. [ABSTRACT FROM AUTHOR]

The cardiovascular system is significantly affected in coronavirus disease-19 (COVID-19). Microvascular injury, endothelial dysfunction, and thrombosis resulting from viral infection or indirectly related to the intense systemic inflammatory and immune responses are characteristic features of severe COVID-19. Pre-existing cardiovascular disease and viral load are linked to myocardial injury and worse outcomes. The vascular response to cytokine production and the interaction between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and angiotensin-converting enzyme 2 receptor may lead to a significant reduction in cardiac contractility and subsequent myocardial dysfunction. In addition, a considerable proportion of patients who have been infected with SARS-CoV-2 do not fully recover and continue to experience a large number of symptoms and post-acute complications in the absence of a detectable viral infection. This conditions often referred to as 'post-acute COVID-19' may have multiple causes. Viral reservoirs or lingering fragments of viral RNA or proteins contribute to the condition. Systemic inflammatory response to COVID-19 has the potential to increase myocardial fibrosis which in turn may impair cardiac remodelling. Here, we summarize the current knowledge of cardiovascular injury and post-acute sequelae of COVID-19. As the pandemic continues and new variants emerge, we can advance our knowledge of the underlying mechanisms only by integrating our understanding of the pathophysiology with the corresponding clinical findings. Identification of new biomarkers of cardiovascular complications, and development of effective treatments for COVID-19 infection are of crucial importance. [ABSTRACT FROM AUTHOR]

Background The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning–based model built to identify common topics/themes within the publications. Results and Conclusions We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning–based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 virus has, up to the time of this article, resulted in >770 million cases of COVID-19 illness worldwide, and approximately 7 million deaths, including >1.1 million in the United States. Although defined as a respiratory virus, early in the pandemic, it became apparent that considerable numbers of people recovering from COVID-19 illness experienced persistence or new onset of multi-system health problems, including neurologic and cognitive and behavioral health concerns. Persistent multi-system health problems are defined as Post-COVID-19 Condition (PCC), Post-Acute Sequelae of COVID-19, or Long COVID. A significant number of those with PCC report cognitive problems. This paper reviews the current state of scientific knowledge on persisting cognitive symptoms in adults following COVID-19 illness. A brief history is provided of the emergence of concerns about persisting cognitive problems following COVID-19 illness and the definition of PCC. Methodologic factors that complicate clear understanding of PCC are reviewed. The review then examines research on patterns of cognitive impairment that have been found, factors that may contribute to increased risk, behavioral health variables, and interventions being used to ameliorate persisting symptoms. Finally, recommendations are made about ways neuropsychologists can improve the quality of existing research. [ABSTRACT FROM AUTHOR]

Background: In 2020, SARS-CoV-2 and the COVID-19 pandemic had a huge impact on the access to and provision of ART treatments. Gradually, knowledge of the virus and its transmission has become available, allowing ART activities to resume. Still, questions on the impact of the virus on human gametes and fertility remain.Objective and Rationale: This article summarizes published data, aiming to clarify the impact of SARS-CoV-2 and the COVID-19 disease on human fertility and assisted reproduction, as well as the impact of vaccination, and from this, provide answers to questions that are relevant for people contemplating pregnancy and for health care professionals.Search Methods: PUBMED/MEDLINE and the WHO COVID-19 database were searched from inception to 5 October 2022 with search terms focusing on 'SARS-CoV-2' and gametes, embryos, reproductive function, fertility and ART. Non-English studies and papers published prior to 2020 were excluded, as well as reviews and non-peer reviewed publications. Full papers were assessed for relevance and quality, where feasible.Outcomes: From the 148 papers included, the following observations were made. The SARS-CoV-2-binding proteins, angiotensin-converting enzyme 2 (ACE2) and type II transmembrane serine protease (TMPRSS2), are expressed in the testis, but co-expression remains to be proven. There is some evidence of SARS-CoV-2 RNA in the ejaculate of COVID-19 patients with severe disease, but not in those with mild/moderate disease. SARS-CoV-2 infection can impair spermatogenesis, but this seems to resolve after one spermatogenic cycle. Testosterone levels seem to be lower during and after COVID-19, but long-term data are lacking; disease severity may be associated with testosterone levels. COVID-19 cannot be considered a sexually transmitted disease. There is no co-expression of ACE2 and TMPRSS2 in the myometrium, uterus, ovaries or fallopian tubes. Oocytes seem to have the receptors and protease machinery to be susceptible to SARS-CoV-2 infection; however, viral RNA in oocytes has not been detected so far. Women contemplating pregnancy following COVID-19 may benefit from screening for thyroid dysfunction. There is a possible (transient) impact of COVID-19 on menstrual patterns. Embryos, and particularly late blastocysts, seem to have the machinery to be susceptible to SARS-CoV-2 infection. Most studies have not reported a significant impact of COVID-19 on ovarian reserve, ovarian function or follicular fluid parameters. Previous asymptomatic or mild SARS-CoV-2 infection in females does not seem to negatively affect laboratory and clinical outcomes of ART. There are no data on the minimum required interval, if any, between COVID-19 recovery and ART. There is no evidence of a negative effect of SARS-CoV-2 vaccination on semen parameters or spermatogenesis, ovarian function, ovarian reserve or folliculogenesis. A transient effect on the menstrual cycle has been documented. Despite concerns, cross reactivity between anti-SARS-CoV-2 spike protein antibodies and Syncytin-1, an essential protein in human implantation, is absent. There is no influence of mRNA SARS-CoV-2 vaccine on patients' performance during their immediate subsequent ART cycle. Pregnancy rates post-vaccination are similar to those in unvaccinated patients.Wider Implications: This review highlights existing knowledge on the impact of SARS-CoV-2 infection or COVID-19 on fertility and assisted reproduction, but also identifies gaps and offers suggestions for future research. The knowledge presented should help to provide evidence-based advice for practitioners and couples contemplating pregnancy alike, facilitating informed decision-making in an environment of significant emotional turmoil. [ABSTRACT FROM AUTHOR]

Background: There has been a large influx of COVID-19 seroprevalence studies, but comparability between the seroprevalence estimates has been an issue because of heterogeneities in testing platforms and study methodology. One potential source of heterogeneity is the response or participation rate.Methods: We conducted a review of participation rates (PR) in SARS-CoV-2 seroprevalence studies collected by SeroTracker and examined their effect on the validity of study conclusions. PR was calculated as the count of participants for whom the investigators had collected a valid sample, divided by the number of people invited to participate in the study. A multivariable beta generalized linear model with logit link was fitted to determine if the PR of international household and community-based seroprevalence studies was associated with the factors of interest, from 1 December 2019 to 10 March 2021.Results: We identified 90 papers based on screening and were able to calculate the PR for 35 out of 90 papers (39%), with a median PR of 70% and an interquartile range of 40.92; 61% of the studies did not report PR.Conclusions: Many SARS-CoV-2 seroprevalence studies do not report PR. It is unclear what the median PR rate would be had a larger portion not had limitations in reporting. Low participation rates indicate limited representativeness of results. Non-probabilistic sampling frames were associated with higher participation rates but may be less representative. Standardized definitions of participation rate and data reporting necessary for the PR calculations are essential for understanding the representativeness of seroprevalence estimates in the population of interest. [ABSTRACT FROM AUTHOR]

Background Early reports on the pandemic nature of coronavirus disease 2019 (COVID-19) directed the nephrology community to develop infection prevention and control (IPC) guidance. We aimed to make an inventory of strategies that dialysis centres followed to prevent infection with COVID-19 in the first pandemic wave. Methods We analyzed IPC measures taken by hemodialysis centres treating patients presenting with COVID-19 between 1 March 2020 and 31 July 2020 and that completed the European Renal Association COVID-19 Database centre questionnaire. Additionally, we made an inventory of guidelines published in European countries to prevent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in dialysis centres. Results Data from 73 dialysis units located in and bordering Europe were analyzed. All participating centres implemented IPC measures to mitigate the impact of SARS-CoV-2 during the first pandemic wave. Measures mentioned most often included triage with questions before entering the dialysis ward, measuring body temperature, hand disinfection, masking for all patients and staff, and personal protective equipment for staff members. These measures were also recommended in most of the 14 guidelines that were identified in the inventory of national guidelines and were also scored as being among the most important measures by the authors of this paper. Heterogeneity existed between centres and national guidelines regarding the minimal distance between dialysis chairs and recommendations regarding isolation and cohorting. Conclusions Although variation existed, measures to prevent transmission of SARS-CoV-2 were relatively similar across centres and national guidelines. Further research is needed to assess causal relationships between measures taken and spread of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics. [ABSTRACT FROM AUTHOR]

Predicting protein properties from amino acid sequences is an important problem in biology and pharmacology. Protein–protein interactions among SARS-CoV-2 spike protein, human receptors and antibodies are key determinants of the potency of this virus and its ability to evade the human immune response. As a rapidly evolving virus, SARS-CoV-2 has already developed into many variants with considerable variation in virulence among these variants. Utilizing the proteomic data of SARS-CoV-2 to predict its viral characteristics will, therefore, greatly aid in disease control and prevention. In this paper, we review and compare recent successful prediction methods based on long short-term memory (LSTM), transformer, convolutional neural network (CNN) and a similarity-based topological regression (TR) model and offer recommendations about appropriate predictive methodology depending on the similarity between training and test datasets. We compare the effectiveness of these models in predicting the binding affinity and expression of SARS-CoV-2 spike protein sequences. We also explore how effective these predictive methods are when trained on laboratory-created data and are tasked with predicting the binding affinity of the in-the-wild SARS-CoV-2 spike protein sequences obtained from the GISAID datasets. We observe that TR is a better method when the sample size is small and test protein sequences are sufficiently similar to the training sequence. However, when the training sample size is sufficiently large and prediction requires extrapolation, LSTM embedding and CNN-based predictive model show superior performance. [ABSTRACT FROM AUTHOR]

Understanding the T-cell response to SARS-CoV-2 is key in patients who lack antibody production. We demonstrate the applicability of a functional assay to measure the T-cell response in a cohort of patients with immunodeficiency. T-cell responses to COVID are now linked to improved outcomes. This paper demonstrates a method to robustly test responses for routine diagnostic use in healthy controls and those unable to make a detectable antibody response due to underlying immune deficiency (primary or acquired). [ABSTRACT FROM AUTHOR]

We read with interest the paper by Lee I et al i .[1] in which they describe widespread microvascular damage marked by endothelial cell damage/activation, microthrombi, complement activation, and disruption of the blood-brain barrier (BBB). Endothelial cell damage is the central part of COVID-19 and a mouse model induced by injection of the S1 subunit of the spike protein. [Extracted from the article]

Coronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized. [ABSTRACT FROM AUTHOR]

The haemagglutination test (HAT)-field protocol described here is an optimization of the recently published HAT, for the detection of antibodies directed against the receptor binding domain (RBD) of the SARS-Cov-2 virus. HAT and HAT-field are both based on haemagglutination triggered by a single reagent, the IH4-RBD recombinant protein. A sample of IH4-RBD sufficient for several thousand tests or a plasmid encoding IH4-RBD can be obtained from the authors of our first paper. Using titration of IH4-RBD, HAT-field now allows a quantitative assessment of antibody levels in a single step, using a few microliters of whole blood, such as can be obtained by finger prick, and requires only very simple disposable equipment. Because it is based on a single soluble reagent, the test can be adapted very simply and rapidly to detect antibodies against variants of the SARS-CoV-2, or conceivably against different pathogens. HAT-field appears well suited to provide quantitative assessments of the serological protection of populations as well as individuals, and given its very low cost, the stability of the IH4-RBD reagent in the adapted buffer and the simplicity of the procedure, could be deployed pretty much anywhere, including in the poorest countries and the most remote corners of the globe. [ABSTRACT FROM AUTHOR]

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Two thousand nineteen novel coronavirus SARS-CoV-2, the pathogen of COVID-19, has caused a catastrophic pandemic, which has a profound and widespread impact on human lives and social economy globally. However, the molecular perturbations induced by the SARS-CoV-2 infection remain unknown. In this paper, from the perspective of omnigenic, we analyze the properties of the neighborhood perturbed by SARS-CoV-2 in the human interactome and disclose the peripheral and core regions of virus-host network (VHN). We find that the virus-host proteins (VHPs) form a significantly connected VHN, among which highly perturbed proteins aggregate into an observable core region. The non-core region of VHN forms a large scale but relatively low perturbed periphery. We further validate that the periphery is non-negligible and conducive to identifying comorbidities and detecting drug repurposing candidates for COVID-19. We particularly put forward a flower model for COVID-19, SARS and H1N1 based on their peripheral regions, and the flower model shows more correlations between COVID-19 and other two similar diseases in common functional pathways and candidate drugs. Overall, our periphery-core pattern can not only offer insights into interconnectivity of SARS-CoV-2 VHPs but also facilitate the research on therapeutic drugs. [ABSTRACT FROM AUTHOR]

Discovering drug–target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug–target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences. [ABSTRACT FROM AUTHOR]