Evidence map›Paper›PMID 36037223›Full record

ArticlePLoS computational biology2022

A novel logical model of COVID-19 intracellular infection to support therapies development.

Elena Piretto, Gianluca Selvaggio, Damiano Bragantini, Enrico Domenici, Luca Marchetti

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 56% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 3 institutions in 1 country.

Elena PirettoEuropean Institute of Oncology IRCCS, Milan, Italy.ORCID 0000-0002-1609-7332
Gianluca SelvaggioFondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Trento, Italy.ORCID 0000-0001-8911-7288
Damiano BragantiniInfectious Diseases Unit, Pederzoli Hospital, Peschiera del Garda, Italy.
Enrico DomeniciFondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Trento, Italy.
Luca MarchettiFondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Trento, Italy.ORCID 0000-0001-9043-7705
University of Trento · ITCasa di Cura Villa Garda · ITEuropean Institute of Oncology · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, a logical-based mathematical model of the cellular pathways involved in the COVID-19 infection has been developed to study various drug treatments (single or in combination), in different illness scenarios, providing insights into their mechanisms of action. Drug simulations suggest that the effects of single drugs are limited, or depending on the scenario counterproductive, whereas better results appear combining different treatments. Specifically, the combination of the anti-inflammatory Baricitinib and the anti-viral Remdesivir showed significant benefits while a stronger efficacy emerged from the triple combination of Baricitinib, Remdesivir, and the corticosteroid Dexamethasone. Together with a sensitivity analysis, we performed an analysis of the mechanisms of the drugs to reveal their impact on molecular pathways.

Indexed as

COVID-19 Drug TreatmentAdenosine MonophosphateAlanineAnti-Inflammatory AgentsAntiviral AgentsHumansSARS-CoV-2Adenosine MonophosphateAlanineAnti-Inflammatory AgentsAntiviral Agents

Identifiers

PMID36037223
PMCPMC9462742
OpenAlexW4293469830

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.