Evidence map›Paper›PMID 38127835›Full record

ArticlePLoS computational biology2023

An agent-based modeling approach for lung fibrosis in response to COVID-19.

Mohammad Aminul Islam, Michael Getz, Paul Macklin, Ashlee N Ford Versypt

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
4.1field-weighted citation impact, top 5% 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

16 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Spatially structured models of viral dynamics: a scoping review.Microbiology and molecular biology reviews : MMBR · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. SIRT3/6/7: promising therapeutic targets for pulmonary fibrosis.Frontiers in cell and developmental biology · 2025
    Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Mohammad Aminul IslamDepartment of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York, United States of America.ORCID 0000-0002-3707-0315
Michael GetzDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.ORCID 0000-0001-9886-8454
Paul MacklinDepartment of Intelligent Systems Engineering, Indiana University, Bloomington, Indiana, United States of America.ORCID 0000-0002-9925-0151
Ashlee N Ford VersyptDepartment of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York, United States of America.ORCID 0000-0001-9059-5703
Indiana University Bloomington · USUniversity at Buffalo, State University of New York · US

Funding

Quantitative Systems Biomedicine and Pharmacology for Multiscale Tissue DamageR35GM133763 · NIGMS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI Ashlee Nicole Ford Versypt · 2019 to 2026
$2.7M
NIGMS NIH HHS R35 GM133763
6 · The paper itself

Abstract

The severity of the COVID-19 pandemic has created an emerging need to investigate the long-term effects of infection on patients. Many individuals are at risk of suffering pulmonary fibrosis due to the pathogenesis of lung injury and impairment in the healing mechanism. Fibroblasts are the central mediators of extracellular matrix (ECM) deposition during tissue regeneration, regulated by anti-inflammatory cytokines including transforming growth factor beta (TGF-β). The TGF-β-dependent accumulation of fibroblasts at the damaged site and excess fibrillar collagen deposition lead to fibrosis. We developed an open-source, multiscale tissue simulator to investigate the role of TGF-β sources in the progression of lung fibrosis after SARS-CoV-2 exposure, intracellular viral replication, infection of epithelial cells, and host immune response. Using the model, we predicted the dynamics of fibroblasts, TGF-β, and collagen deposition for 15 days post-infection in virtual lung tissue. Our results showed variation in collagen area fractions between 2% and 40% depending on the spatial behavior of the sources (stationary or mobile), the rate of activation of TGF-β, and the duration of TGF-β sources. We identified M2 macrophages as primary contributors to higher collagen area fraction. Our simulation results also predicted fibrotic outcomes even with lower collagen area fraction when spatially-localized latent TGF-β sources were active for longer times. We validated our model by comparing simulated dynamics for TGF-β, collagen area fraction, and macrophage cell population with independent experimental data from mouse models. Our results showed that partial removal of TGF-β sources changed the fibrotic patterns; in the presence of persistent TGF-β sources, partial removal of TGF-β from the ECM significantly increased collagen area fraction due to maintenance of chemotactic gradients driving fibroblast movement. The computational findings are consistent with independent experimental and clinical observations of collagen area fractions and cell population dynamics not used in developing the model. These critical insights into the activity of TGF-β sources may find applications in the current clinical trials targeting TGF-β for the resolution of lung fibrosis.

Indexed as

COVID-19Pulmonary FibrosisAnimalsCollagenFibroblastsFibrosisHumansLungMicePandemicsSARS-CoV-2Transforming Growth Factor betaCollagenTransforming Growth Factor beta

Identifiers

PMID38127835
PMCPMC10769079
OpenAlexW4390046004

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.