Evidence map›Paper›PMID 34198530›Full record

ArticleViruses2021

Modeling within-Host SARS-CoV-2 Infection Dynamics and Potential Treatments.

Mehrshad Sadria, Anita T Layton

Open access · goldAbstract read
In one paragraph

Article in Viruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
4.6field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 79 citations in OpenAlex.

  1. Review
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  10. Machine Learning and COVID-19: Lessons from SARS-CoV-2.Advances in experimental medicine and biology · 2023
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Mehrshad SadriaDepartment of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Anita T LaytonDepartment of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
University of Waterloo · CA

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2019-03916
6 · The paper itself

Abstract

The goal of this study was to develop a mathematical model to simulate the actions of drugs that target SARS-CoV-2 virus infection. To accomplish that goal, we have developed a mathematical model that describes the control of a SARS-CoV-2 infection by the innate and adaptive immune components. Invasion of the virus triggers the innate immunity, whereby interferon renders some of the target cells resistant to infection, and infected cells are removed by effector cells. The adaptive immune response is represented by plasma cells and virus-specific antibodies. The model is parameterized and then validated against viral load measurements collected in COVID-19 patients. We apply the model to simulate three potential anti-SARS-CoV-2 therapies: (1) Remdesivir, a repurposed drug that has been shown to inhibit the transcription of SARS-CoV-2, (2) an alternative (hypothetical) therapy that inhibits the virus' entry into host cells, and (3) convalescent plasma transfusion therapy. Simulation results point to the importance of early intervention, i.e., for any of the three therapies to be effective, it must be administered sufficiently early, not more than a day or two after the onset of symptoms. The model can serve as a key component in integrative platforms for rapid in silico testing of potential COVID-19 therapies and vaccines.

Indexed as

Adaptive ImmunityCOVID-19 Drug TreatmentImmunity, InnateModels, TheoreticalAdenosine MonophosphateAlanineAntibodies, ViralAntiviral AgentsCOVID-19COVID-19 SerotherapyHost-Pathogen InteractionsHumansImmunization, PassiveVirus InternalizationAdenosine MonophosphateAlanineAntibodies, ViralAntiviral Agentsremdesivirconvalescent plasma transfusionCOVID-19immune responsemathematical modelingRemdesivir

Identifiers

PMID34198530
PMCPMC8231802
OpenAlexW3167263937

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.