Evidence map›Paper›PMID 39695178›Full record

ArticleScientific reports2024

A biomathematical model of SARS-CoV-2 in Syrian hamsters.

Sibylle Schirm, Geraldine Nouailles, Holger Kirsten, Jakob Trimpert, Emanuel Wyler, Luiz Gustavo Teixeira Alves, Markus Landthaler, Peter Ahnert, Norbert Suttorp, Martin Witzenrath and 1 more

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Sibylle SchirmInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, 04107, Leipzig, Germany. sibylle.schirm@imise.uni-leipzig.de.
Geraldine NouaillesDepartment of Infectious Diseases and Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany.
Holger KirstenInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, 04107, Leipzig, Germany.
Jakob TrimpertInstitute of Virology, Freie Universität Berlin, 14163, Berlin, Germany.
Emanuel WylerBerlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 10115, Berlin, Germany.
Luiz Gustavo Teixeira AlvesBerlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 10115, Berlin, Germany.
Markus LandthalerBerlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 10115, Berlin, Germany.
Peter AhnertInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, 04107, Leipzig, Germany.
Norbert SuttorpDepartment of Infectious Diseases and Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany.
Martin WitzenrathDepartment of Infectious Diseases and Respiratory Medicine and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany.
Markus ScholzInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, 04107, Leipzig, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When infected with SARS-CoV-2, Syrian hamsters (Mesocricetus auratus) develop moderate disease severity presenting key features of human COVID-19. We here develop a biomathematical model of the disease course by translating known biological mechanisms of virus-host interactions and immune responses into ordinary differential equations. We explicitly describe the dynamics of virus population, affected alveolar epithelial cells, and involved relevant immune cells comprising for example CD4+ T cells, CD8+ T cells, macrophages, natural killer cells and B cells. We also describe the humoral response dynamics of neutralising antibodies and major regulatory cytokines including CCL8 and CXCL10. The model is developed and parametrized based on experimental data collected at days 2, 3, 5, and 14 post infection. Pulmonary cell composition and their transcriptional profiles were obtained by lung single-cell RNA (scRNA) sequencing analysis. Parametrization of the model resulted in a good agreement of model and data. The model can be used to predict, for example, the time course of the virus population, immune cell dynamics, antibody production and regeneration of alveolar cells for different therapy scenarios or after multiple-infection events. We aim to translate this model to the human situation in the future.

Indexed as

COVID-19Disease Models, AnimalMesocricetusSARS-CoV-2AnimalsAntibodies, NeutralizingAntibodies, ViralCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCricetinaeCytokinesHumansLungAntibodies, NeutralizingAntibodies, ViralCytokinesB cellsCCL8CXCL10Immune responseMacrophagesMathematical modelNatural killer cellsNeutralising antibodiesSARS-CoV-2T-cells

Identifiers

PMID39695178
PMCPMC11655638

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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 OpenQuestion graph.