Evidence map›Paper›PMID 36851511›Full record

ArticleViruses2023

Stochastic Modelling of HIV-1 Replication in a CD4 T Cell with an IFN Response.

Igor Sazonov, Dmitry Grebennikov, Rostislav Savinkov, Arina Soboleva, Kirill Pavlishin, Andreas Meyerhans, Gennady Bocharov

Abstract read
In one paragraph

Article in Viruses, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Igor SazonovFaculty of Science and Engineering, Swansea University, Bay Campus, Fabian Way SA1 8EN, UK.ORCID 0000-0001-6685-2351
Dmitry GrebennikovMarchuk Institute of Numerical Mathematics of the RAS, 119333 Moscow, Russia.ORCID 0000-0002-7315-193X
Rostislav SavinkovMarchuk Institute of Numerical Mathematics of the RAS, 119333 Moscow, Russia.
Arina SobolevaDepartment of Control and Applied Mathematics, Moscow Institute of Physics and Technology (National Research University), 141701 Dolgoprudny, Russia.
Kirill PavlishinFaculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119991 Moscow, Russia.
Andreas MeyerhansI CREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain.ORCID 0000-0003-0620-5317
Gennady BocharovMarchuk Institute of Numerical Mathematics of the RAS, 119333 Moscow, Russia.ORCID 0000-0002-5049-0656

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A mathematical model of the human immunodeficiency virus Type 1 (HIV-1) life cycle in CD4 T cells was constructed and calibrated. It describes the activation of the intracellular Type I interferon (IFN-I) response and the IFN-induced suppression of viral replication. The model includes viral replication inhibition by interferon-induced antiviral factors and their inactivation by the viral proteins Vpu and Vif. Both deterministic and stochastic model formulations are presented. The stochastic model was used to predict efficiency of IFN-I-induced suppression of viral replication in different initial conditions for autocrine and paracrine effects. The probability of virion excretion for various MOIs and various amounts of IFN-I was evaluated and the statistical properties of the heterogeneity of HIV-1 and IFN-I production characterised.

Indexed as

HIV-1Interferon Type IAntibodiesCD4-Positive T-LymphocytesHumansVirus ReplicationAntibodiesInterferon Type IHIV life cycleMarkov chain Monte Carlo methodmathematical modelsensitivity analysisstochastic processesType I interferon (IFN-I)viral dynamics

Identifiers

PMID36851511
PMCPMC9966781

What OpenQuestion 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 OpenQuestion graph.