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1.
IJID Reg ; 3: 106-113, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1899827

ABSTRACT

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.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-317957

ABSTRACT

In January 2020, the novel Coronavirus Disease-2019 (COVID-19) epidemic spread to Italy. The ensuing high rates of patients with pulmonary disease due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections, overwhelmed the Italian health services. Management of inpatients was based on World Health Organization (WHO) and other public health bodies’ and specialist societies’ clinical, diagnostic and therapeutic protocols developed with very low-quality evidence base at that time. Over time, management guidelines and protocols were progressively modified and adapted based on the evolving first hand clinical management experience, and the evidence, which has slowly accumulated from clinical large cohort studies and clinical trials. As of August 9th, 2020, there have been 250.103 confirmed COVID-19 cases (with 35.203 deaths) reported from Italy. We present chronological evolution of the clinical and scientific evidence-based management guidelines to date, and their influence on the health care workers management of patients with COVID-19 disease.Funding Statement: This research was supported by funds to National Institute for Infectious Diseases ‘Lazzaro Spallanzani’ IRCCS from Line one-Ricerca Corrente ‘Infezioni Emergenti e Riemergenti’ and by Progetto COVID 2020 12371675 both funded by Italian Ministry of Health and from European Commission – Horizon 2020 (EXSCALATE4CoV).Sir Zumla and Prof Ippolito are co-PIs of the Pan-African Network on Emerging and Re-Emerging Infections (PANDORA-ID-NET – https://www.pandora-id.net/) funded by the European and Developing Countries Clinical Trials Partnership. Sir Zumla is in receipt of a National Institutes of Health Research senior investigator award.Declaration of Interests: EN received grants from Gilead science for educational purpose. Al other authors have no conflicts of interest to declareEthics Approval Statement: The authors stated that Ethical approval was not required.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-314865

ABSTRACT

Background: More 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 help 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. Nine CBC parameters as a continuous variable were studied [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 and difference 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. Neutrophil 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. Reverse temporal trends were observed for lymphocyte and neutrophil counts in survivors and non-survivors. Average platelets counts (P<0.001) and median platelets volume (P<0.001) were significantly different in survivors and in non-survivors. The differences were time dependent and consistent with acute inflammation followed either by recovery or by death. Anaemia with anisocytosis were observed in the later phase of COVID-19 disease in non-survivors only. Mortality was significantly higher in patients with diabetes (p=0.005), obesity (p=0.010), chronic renal failure (p=0.001), COPD (p=0.033) cardiovascular diseases (p=0.001) and those >60 years(p=0.001). Age (p=0.042), obesity (p=0.002), chronic renal failure (p=0.002) and cardiovascular diseases (p=0.009) were independently associated with poor patient clinical outcome at 30 day after symptom onset. Interpretation: Increased neutrophil counts, reduced lymphocyte counts, higher median platelet volume, anemia with anisocytosis, in association with obesity, chronic renal failure, COPD, cardiovascular diseases and age >60 years predict poor prognosis in COVID19 patients.Funding Statement: Ricerca Corrente e Finalizzata Italy Ministry of Health, AIRC (IG2018-21880);Regione Lazio (Gruppi di ricerca, E56C18000460002).Declaration of Interests: The authors declare no competing interest.Ethics Approval Statement: This study was approved by the IRB of Italian National Institute for Infectious Diseases “Lazzaro Spallanzani” (INMI), in Rome (Italy).

4.
J Transl Med ; 19(1): 501, 2021 12 07.
Article in English | MEDLINE | ID: covidwho-1560461

ABSTRACT

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.


Subject(s)
COVID-19 , Humans , Immunity, Innate , Pandemics , SARS-CoV-2
6.
Cell Death Dis ; 12(8): 788, 2021 08 12.
Article in English | MEDLINE | ID: covidwho-1356553

ABSTRACT

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.


Subject(s)
COVID-19/metabolism , COVID-19/virology , SARS-CoV-2/metabolism , Host Microbial Interactions , Immunity/immunology , Protein Interaction Maps/physiology , Proteome , Proteomics/methods , SARS-CoV-2/pathogenicity , Severity of Illness Index , Viral Proteins/metabolism , Viral Regulatory and Accessory Proteins/metabolism
7.
Viruses ; 13(7)2021 07 06.
Article in English | MEDLINE | ID: covidwho-1302499

ABSTRACT

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.

8.
BMJ Open ; 11(1): e043418, 2021 01 25.
Article in English | MEDLINE | ID: covidwho-1048683

ABSTRACT

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.


Subject(s)
COVID-19/psychology , Coercion , Health Personnel/psychology , Pandemics , Perception , Adaptation, Psychological , COVID-19/epidemiology , Europe/epidemiology , Focus Groups , Humans , Mental Health , Physical Distancing , Psychological Distress , SARS-CoV-2 , Stress, Psychological
9.
PLoS One ; 15(12): e0244129, 2020.
Article in English | MEDLINE | ID: covidwho-999830

ABSTRACT

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.


Subject(s)
COVID-19/blood , Biomarkers/blood , Blood Cell Count , COVID-19/immunology , Cohort Studies , Demography/methods , Erythrocyte Indices/immunology , Female , Humans , Inflammation/blood , Inflammation/immunology , Leukocyte Count/methods , Longitudinal Studies , Lymphocytes/immunology , Male , Mean Platelet Volume/methods , Middle Aged , Neutrophils/immunology , Prognosis , Rome , Survivors
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