Browse
Submit Data
Databases
API
Help

Dataset Information

5 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Spatial and Temporal Virus Load Dynamics of SARS-CoV-2: A Single-Center Cohort Study.

ABSTRACT: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing pandemic. Reverse transcription polymerase chain reaction (RT-PCR) is the gold standard for the detection of SARS-CoV-2 and has been applied to different specimen types. Understanding the virus load and virus detection frequency in different specimen types is important to improve diagnosis and estimate the duration of potential infectivity. We conducted a retrospective single-center cohort study on hospitalized and outpatients with SARS-CoV-2 infection. We analyzed the frequency of virus detection, virus load, and duration of the virus excretion in upper and lower respiratory specimens as well as stool and plasma. We found that the frequency of SARS-CoV-2 detection, the virus load, and duration of virus excretion was higher in lower respiratory tract (LRT) than in upper respiratory tract (URT) specimens. The duration of virus excretion was longer in patients requiring intensive care unit (ICU) admission. In conclusion, LRT specimens are the most appropriate specimen type for the detection and follow-up of SARS-CoV-2 infection. Duration of virus excretion is longer in severe cases of SARS-CoV-2 infection.

SUBMITTER: Alidjinou EK

PROVIDER: S-EPMC7999170 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Modelling upper respiratory viral load dynamics of SARS-CoV-2.

Project description:Relationships between viral load, severity of illness, and transmissibility of virus are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with the control of the viral load. Neutralising antibodies correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralising antibodies. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions.

| S-EPMC8755404 | biostudies-literature

Viral Load Dynamics in SARS-CoV-2 Omicron Breakthrough Infections.

Project description:To determine viral dynamics in Omicron breakthrough infections, we measured severe acute respiratory syndrome coronavirus 2 RNA in 206 double-vaccinated or boostered individuals. During the first 3 days following the onset of symptoms, viral loads were significantly higher (cycle threshold [Ct], 21.76) in vaccinated compared to boostered (Ct, 23.14) individuals (P = .029). However, by performing a longitudinal analysis on 32 individuals over 14 days, no difference in the viral load trajectory was observed between double-vaccinated and boostered patients. Our results indicate that booster immunization results in a reduction in detectable viral loads without significantly changing viral load dynamics over time.

| S-EPMC9384619 | biostudies-literature

Spatial and temporal dynamics of SARS-CoV-2 in COVID-19 patients: A systematic review and meta-analysis.

Project description:BackgroundThe spatial and temporal dynamics of SARS-CoV-2 have been described in case series and retrospective studies. In this study, we provide a coherent overview of the duration of viral detection and viral RNA load in COVID-19 patients, stratified by specimen type, clinical severity, and age.MethodWe systematically searched PubMed/MEDLINE and Cochrane review database for studies published between 1.11.2019 and 23.04.2020. We pooled the data of selected studies (22/7226 (650 patients) for meta-analysis) to estimate duration of viral detection and visualized viral load over time.FindingsOur analysis showed consistent viral detection from specimen from the upper respiratory tract (URT), the lower respiratory tract (LRT), and faeces, irrespective of the clinical severity of COVID-19. Our analysis suggests that SARS-CoV-2 persists for a longer duration in the LRT compared to the URT in adult patients (5•7 days in mild; 5•9 days in moderate-severe patients). The differences in the duration of viral detection between mild and moderate-severe patients is limited in the LRT, but an indication of longer duration of viral detection for moderate-severe patients was observed in feces (15 days in mild vs. 21 days in moderate-severe patients) and the URT (12 days in mild vs. 16 days in moderate-severe patients). Further, viral load was demonstrated to peak in earlier stages of infection in the URT compared to LRT.InterpretationThis review may aid mathematical modelling and help in defining appropriate endpoints for clinical trails with antivirals in COVID-19.FundingThe project has received funding support from Innovation Fund Denmark.

| S-EPMC7374142 | biostudies-literature

Temporal and Spatial Heterogeneity of Host Response to SARS-CoV-2 Pulmonary Infection

Project description:This SuperSeries is composed of the SubSeries listed below.

2020-11-01 | GSE159788 | GEO

Single-molecule diffusion and conformational dynamics by spatial integration of temporal fluctuations.

Project description:Single-molecule localization and tracking has been used to translate spatiotemporal information of individual molecules to map their diffusion behaviours. However, accurate analysis of diffusion behaviours and including other parameters, such as the conformation and size of molecules, remain as limitations to the method. Here, we report a method that addresses the limitations of existing single-molecular localization methods. The method is based on temporal tracking of the cumulative area occupied by molecules. These temporal fluctuations are tied to molecular size, rates of diffusion and conformational changes. By analysing fluorescent nanospheres and double-stranded DNA molecules of different lengths and topological forms, we demonstrate that our cumulative-area method surpasses the conventional single-molecule localization method in terms of the accuracy of determined diffusion coefficients. Furthermore, the cumulative-area method provides conformational relaxation times of structurally flexible chains along with diffusion coefficients, which together are relevant to work in a wide spectrum of scientific fields.

| S-EPMC4205855 | biostudies-other

Spatial-Temporal Variations in Atmospheric Factors Contribute to SARS-CoV-2 Outbreak.

Project description:The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) has reached over five million confirmed cases worldwide, and numbers are still growing at a fast rate. Despite the wide outbreak of the infection, a remarkable asymmetry is observed in the number of cases and in the distribution of the severity of the COVID-19 symptoms in patients with respect to the countries/regions. In the early stages of a new pathogen outbreak, it is critical to understand the dynamics of the infection transmission, in order to follow contagion over time and project the epidemiological situation in the near future. While it is possible to reason that observed variation in the number and severity of cases stems from the initial number of infected individuals, the difference in the testing policies and social aspects of community transmissions, the factors that could explain high discrepancy in areas with a similar level of healthcare still remain unknown. Here, we introduce a binary classifier based on an artificial neural network that can help in explaining those differences and that can be used to support the design of containment policies. We found that SARS-CoV-2 infection frequency positively correlates with particulate air pollutants, and specifically with particulate matter 2.5 (PM2.5), while ozone gas is oppositely related with the number of infected individuals. We propose that atmospheric air pollutants could thus serve as surrogate markers to complement the infection outbreak anticipation.

| S-EPMC7354543 | biostudies-literature

Spatial and temporal distribution of SARS-CoV-2 diversity circulating in wastewater.

Project description:Wastewater-based epidemiology (WBE) has proven to be an effective tool for epidemiological surveillance of SARS-CoV-2 during the current COVID-19 pandemic. Furthermore, combining WBE together with high-throughput sequencing techniques can be useful for the analysis of SARS-CoV-2 viral diversity present in a given sample. The present study focuses on the genomic analysis of SARS-CoV-2 in 76 sewage samples collected during the three epidemiological waves that occurred in Spain from 14 wastewater treatment plants distributed throughout the country. The results obtained demonstrate that the metagenomic analysis of SARS-CoV-2 in wastewater allows the detection of mutations that define the B.1.1.7 lineage and the ability of the technique to anticipate the detection of certain mutations before they are detected in clinical samples. The study proves the usefulness of sewage sequencing to track Variants of Concern that can complement clinical testing to help in decision-making and in the analysis of the evolution of the pandemic.

| S-EPMC8702378 | biostudies-literature

Spatial and temporal effects on severe acute respiratory coronavirus virus 2 (SARS-CoV-2) contamination of the healthcare environment.

Project description:BackgroundThe spatial and temporal extent of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) environmental contamination has not been precisely defined. We sought to elucidate contamination of different surface types and how contamination changes over time.MethodsWe sampled surfaces longitudinally within COVID-19 patient rooms, performed quantitative RT-PCR for the detection of SARS-CoV-2 RNA, and modeled distance, time, and severity of illness on the probability of detecting SARS-CoV-2 using a mixed-effects binomial model.ResultsThe probability of detecting SARS-CoV-2 RNA in a patient room did not vary with distance. However, we found that surface type predicted probability of detection, with floors and high-touch surfaces having the highest probability of detection: floors (odds ratio [OR], 67.8; 95% credible interval [CrI], 36.3-131) and high-touch elevated surfaces (OR, 7.39; 95% CrI, 4.31-13.1). Increased surface contamination was observed in room where patients required high-flow oxygen, positive airway pressure, or mechanical ventilation (OR, 1.6; 95% CrI, 1.03-2.53). The probability of elevated surface contamination decayed with prolonged hospitalization, but the probability of floor detection increased with the duration of the local pandemic wave.ConclusionsDistance from a patient's bed did not predict SARS-CoV-2 RNA deposition in patient rooms, but surface type, severity of illness, and time from local pandemic wave predicted surface deposition.

| S-EPMC8755533 | biostudies-literature

SARS-CoV-2 viral RNA load dynamics in the nasopharynx of infected children.

Project description:It is important to understand the temporal trend of the paediatric severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load to estimate the transmission potential of children in schools and communities. We determined the differences in SARS-CoV-2 viral load dynamics between nasopharyngeal samples of infected asymptomatic and symptomatic children. Serial cycle threshold values of SARS-CoV-2 from the nasopharynx of a cohort of infected children were collected for analysis. Among 17 infected children, 10 (58.8%) were symptomatic. Symptomatic children, when compared to asymptomatic children, had higher viral loads (mean cycle threshold on day 7 of illness 28.6 vs. 36.7, P = 0.02). Peak SARS-CoV-2 viral loads occurred around day 2 of illness in infected children. Although we were unable to directly demonstrate infectivity, the detection of significant amount of virus in the upper airway of asymptomatic children suggest that they have the potential to shed and transmit SARS-CoV-2. Our study highlights the importance of contact tracing and screening for SARS-CoV-2 in children with epidemiological risk factors regardless of their symptom status, in order to improve containment of the virus in the community, including educational settings.

| S-EPMC7847743 | biostudies-literature

Temporal and Spatial Heterogeneity of Host Response to SARS-CoV-2 Pulmonary Infection.

Project description:The relationship of SARS-CoV-2 lung infection and severity of pulmonary disease is not fully understood. We analyzed autopsy specimens from 24 patients who succumbed to SARS-CoV-2 infection using a combination of different RNA and protein analytical platforms to characterize inter- and intra- patient heterogeneity of pulmonary virus infection. There was a spectrum of high and low virus cases that was associated with duration of disease and activation of interferon pathway genes. Using a digital spatial profiling platform, the virus corresponded to distinct spatial expression of interferon response genes and immune checkpoint genes demonstrating the intra-pulmonary heterogeneity of SARS-CoV-2 infection.

| S-EPMC7402055 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data