SARS-CoV-2 B.1.214.1, B.1.214.2 and B.1.620 are predominant lineages between December 2020 and July 2021 in the Republic of Congo.

Background: : SARS-CoV-2 variants have been emerging and are shown to increase transmissibility, pathogenicity, and decreased vaccine efficacies. The objective of this study was to determine the distribution, prevalence, and dynamics of SARS-CoV-2 variants circulating in Brazzaville, the Republic of Congo (ROC). Methods: : Between December 2020 and July 2021, a total of n=600 oropharyngeal specimens collected in the community were tested for COVID-19. Of the samples tested, 317 (53%) were SARS-CoV-2 positive. All samples that had a threshold of Ct <30 (n=182) were sequenced by next-generation sequencing (NGS), and all complete sequenced genomes were submitted to GISAID; lineages were assigned using pangolin nomenclature and a phylogenetic tree was reconstructed. In addition, the global prevalence of the predominant lineages was analysed using data from GISAID and Outbreak databases. Results: : A total of 15 lineages circulated with B.1.214.2 (26%), B.1.214.1 (19%) and B.1.620 (18%) being predominant. The variants of concern (VOC) alpha (B.1.1.7) (6%) and for the first time in June delta (B.1.617.2) (4%) were observed. In addition, the B.1.214.1 lineage first reported from ROC was observed to be spreading locally and regionally. Phylogenetic analysis suggests that the B.1.620 variant (VUM) under observation may have originated from either Cameroon or the Central African Republic. SARS-CoV-2 lineages were heterogeneous, with the densely populated districts of Poto-Poto and Moungali likely the epicenter of spread. Conclusion: : Longitudinal monitoring and molecular surveillance across time and space are critical to understanding viral phylodynamics, which could have important implications for transmissibility and impact infection prevention and control measures.

Multi-omics approach to COVID-19: a domain-based literature review.

BACKGROUND: Omics data, driven by rapid advances in laboratory techniques, have been generated very quickly during the COVID-19 pandemic. Our aim is to use omics data to highlight the involvement of specific pathways, as well as that of cell types and organs, in the pathophysiology of COVID-19, and to highlight their links with clinical phenotypes of SARS-CoV-2 infection. METHODS: The analysis was based on the domain model, where for domain it is intended a conceptual repository, useful to summarize multiple biological pathways involved at different levels. The relevant domains considered in the analysis were: virus, pathways and phenotypes. An interdisciplinary expert working group was defined for each domain, to carry out an independent literature scoping review. RESULTS: The analysis revealed that dysregulated pathways of innate immune responses, (i.e., complement activation, inflammatory responses, neutrophil activation and degranulation, platelet degranulation) can affect COVID-19 progression and outcomes. These results are consistent with several clinical studies. CONCLUSIONS: Multi-omics approach may help to further investigate unknown aspects of the disease. However, the disease mechanisms are too complex to be explained by a single molecular signature and it is necessary to consider an integrated approach to identify hallmarks of severity.

Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2-host interactome.

In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein-protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS-CoV-2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID-19 patients.

Rationale and Criteria for a COVID-19 Model Framework.

Complex systems are inherently multilevel and multiscale systems. The infectious disease system is considered a complex system resulting from the interaction between three sub-systems (host, pathogen, and environment) organized into a hierarchical structure, ranging from the cellular to the macro-ecosystem level, with multiscales. Therefore, to describe infectious disease phenomena that change through time and space and at different scales, we built a model framework where infectious disease must be considered the set of biological responses of human hosts to pathogens, with biological pathways shared with other pathologies in an ecological interaction context. In this paper, we aimed to design a framework for building a disease model for COVID-19 based on current literature evidence. The model was set up by identifying the molecular pathophysiology related to the COVID-19 phenotypes, collecting the mechanistic knowledge scattered across scientific literature and bioinformatic databases, and integrating it using a logical/conceptual model systems biology. The model framework building process began from the results of a domain-based literature review regarding a multiomics approach to COVID-19. This evidence allowed us to define a framework of COVID-19 conceptual model and to report all concepts in a multilevel and multiscale structure. The same interdisciplinary working groups that carried out the scoping review were involved. The conclusive result is a conceptual method to design multiscale models of infectious diseases. The methodology, applied in this paper, is a set of partially ordered research and development activities that result in a COVID-19 multiscale model.

COVID-19 welbeing study: a protocol examining perceived coercion and psychological well-being during the COVID-19 pandemic by means of an online survey, asynchronous virtual focus groups and individual interviews.

INTRODUCTION: The COVID-19 pandemic has resulted in many countries applying restrictive measures, such as lockdown, to contain and prevent further spread. The psychological impact of lockdown and working as a healthcare worker on the frontline has been chronicled in studies pertaining to previous infectious disease pandemics that have reported the presence of depressive symptoms, anxiety, insomnia, and post-traumatic stress symptoms. Potentially linked to psychological well-being and not yet studied is the possibility that lockdown and working on the frontline of the pandemic are associated with perceptions of coercion. METHODS AND ANALYSIS: The present study aimed to examine perceived coercion in those who have experienced COVID-19-related lockdown and/or worked as a frontline healthcare worker across three European countries. It aimed to describe how such perceptions may impact on psychological well-being, coping and post-traumatic growth. It will employ an explanatory mixed-methods research methodology consisting of an online survey and online asynchronous virtual focus groups (AVFGs) and individual interviews. χ2 tests and analyses of variance will be used to examine whether participants from different countries differ according to demographic factors, whether there are differences between cohorts on perceived coercion, depression, anxiety and post-traumatic growth scores. The relationship between coercion and symptoms of distress will be assessed using multiple regression. Both the AVFGs and the narrative interviews will be analysed using thematic narrative analysis. ETHICS AND DISSEMINATION: The study has been approved by University College London's Research Ethics Committee under Project ID Number 7335/004. Results will be disseminated by means of peer-reviewed publications and at national and/or international conferences.

COVID-19 disease-Temporal analyses of complete blood count parameters over course of illness, and relationship to patient demographics and management outcomes in survivors and non-survivors: A longitudinal descriptive cohort study.

BACKGROUND: Detailed temporal analyses of complete (full) blood count (CBC) parameters, their evolution and relationship to patient age, gender, co-morbidities and management outcomes in survivors and non-survivors with COVID-19 disease, could identify prognostic clinical biomarkers. METHODS: From 29 January 2020 until 28 March 2020, we performed a longitudinal cohort study of COVID-19 inpatients at the Italian National Institute for Infectious Diseases, Rome, Italy. 9 CBC parameters were studied as continuous variables [neutrophils, lymphocytes, monocytes, platelets, mean platelet volume, red blood cell count, haemoglobin concentration, mean red blood cell volume and red blood cell distribution width (RDW %)]. Model-based punctual estimates, as average of all patients' values, and differences between survivors and non-survivors, overall, and by co-morbidities, at specific times after symptoms, with relative 95% CI and P-values, were obtained by marginal prediction and ANOVA- style joint tests. All analyses were carried out by STATA 15 statistical package. MAIN FINDINGS: 379 COVID-19 patients [273 (72% were male; mean age was 61.67 (SD 15.60)] were enrolled and 1,805 measures per parameter were analysed. Neutrophils' counts were on average significantly higher in non-survivors than in survivors (P<0.001) and lymphocytes were on average higher in survivors (P<0.001). These differences were time dependent. Average platelets' counts (P<0.001) and median platelets' volume (P<0.001) were significantly different in survivors and non-survivors. The differences were time dependent and consistent with acute inflammation followed either by recovery or by death. Anaemia with anisocytosis was observed in the later phase of COVID-19 disease in non-survivors only. Mortality was significantly higher in patients with diabetes (OR = 3.28; 95%CI 1.51-7.13; p = 0.005), obesity (OR = 3.89; 95%CI 1.51-10.04; p = 0.010), chronic renal failure (OR = 9.23; 95%CI 3.49-24.36; p = 0.001), COPD (OR = 2.47; 95% IC 1.13-5.43; p = 0.033), cardiovascular diseases (OR = 4.46; 95%CI 2.25-8.86; p = 0.001), and those >60 years (OR = 4.21; 95%CI 1.82-9.77; p = 0.001). Age (OR = 2.59; 95%CI 1.04-6.45; p = 0.042), obesity (OR = 5.13; 95%CI 1.81-14.50; p = 0.002), renal chronic failure (OR = 5.20; 95%CI 1.80-14.97; p = 0.002) and cardiovascular diseases (OR 2.79; 95%CI 1.29-6.03; p = 0.009) were independently associated with poor clinical outcome at 30 days after symptoms' onset. INTERPRETATION: Increased neutrophil counts, reduced lymphocyte counts, increased median platelet volume and anaemia with anisocytosis, are poor prognostic indicators for COVID19, after adjusting for the confounding effect of obesity, chronic renal failure, COPD, cardiovascular diseases and age >60 years.