Evidence map›Paper›PMID 32686248›Full record

ReviewReviews in medical virology2020

Novel coronavirus SARS-CoV-2 (Covid-19) dynamics inside the human body.

Asit Saha, Barsha Saha

Abstract readReview
In one paragraph

Review in Reviews in medical virology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
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.

Asit SahaThe Lambda Academy of Science, Success, Western Australia, Australia.ORCID https://orcid.org/0000-0003-3139-1959
Barsha SahaThe Lambda Academy of Science, Success, Western Australia, Australia.

Funding

University College LondonWorld Health Organization
6 · The paper itself

Abstract

A knowledge-based cybernetic framework model representing the dynamics of SARS-CoV-2 inside the human body has been studied analytically and in silico to explore the pathophysiologic regulations. The following modeling methodology was developed as a platform to introduce a predictive tool supporting a therapeutic approach to Covid-19 disease. A time-dependent nonlinear system of ordinary differential equations model was constructed involving type-I cells, type-II cells, SARS-CoV-2 virus, inflammatory mediators, interleukins along with host pulmonary gas exchange rate, thermostat control, and mean pressure difference. This formalism introduced about 17 unknown parameters. Estimating these unknown parameters requires a mathematical association with the in vivo sparse data and the dynamic sensitivities of the model. The cybernetic model can simulate a dynamic response to the reduced pulmonary alveolar gas exchange rate, thermostat control, and mean pressure difference under a very critical condition based on equilibrium (steady state) values of the inflammatory mediators and system parameters. In silico analysis of the current cybernetical approach with system dynamical modeling can provide an intellectual framework to help experimentalists identify more active therapeutic approaches.

Indexed as

Nonlinear DynamicsAcute-Phase ProteinsAngiotensin-Converting Enzyme 2Anti-Inflammatory AgentsAntiviral AgentsBetacoronavirusBody TemperatureCoronavirus InfectionsCOVID-19CytokinesEpithelial CellsGene Expression RegulationHost-Pathogen InteractionsHumansLungMacrophages, AlveolarACE2 protein, humanAcute-Phase ProteinsAngiotensin-Converting Enzyme 2Anti-Inflammatory AgentsAntiviral AgentsCytokinesPeptidyl-Dipeptidase ASpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2Covid-19cyberneticsgas exchangeinflammatory mediatorspressure differencethermostat

Identifiers

PMID32686248
PMCPMC7404608

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.