Evidence map›Paper›PMID 40828805›Full record

ArticlePloS one2025

Agent-based modeling of macrophage-fibroblast interactions in the immune response to biomaterials.

Jennifer Riccio, Luca Presotto, Shir Bahiri, Liad Doniza, Donato Inverso, Laura Sironi, Uri Nevo, Giuseppe Chirico

Abstract read
In one paragraph

Article in PloS one, 2025. 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

8 authors.

Jennifer RiccioDepartment of Physics, Università degli Studi di Milano-Bicocca, Milan, Italy.ORCID https://orcid.org/0000-0002-6264-4229
Luca PresottoDepartment of Physics, Università degli Studi di Milano-Bicocca, Milan, Italy.ORCID https://orcid.org/0000-0002-8809-0877
Shir BahiriDepartment of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
Liad DonizaDepartment of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
Donato InversoDivision of Immunology, Transplantation and Infectious Diseases, Vita-Salute San Raffaele University, Milan, Italy.
Laura SironiDepartment of Physics, Università degli Studi di Milano-Bicocca, Milan, Italy.
Uri NevoDepartment of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.
Giuseppe ChiricoDepartment of Physics, Università degli Studi di Milano-Bicocca, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Foreign body reaction (FBR) denotes the reaction to the implantation of a biomaterial into the body. It triggers cascades of responses in the tissue and involves different cell types including, among others, macrophages, fibroblasts and endothelial cells. Macrophages regulate the inflammatory and healing processes. They exhibit a variety of functional phenotypes (or states) induced by the stimulus they receive and the microenvironment. This polarization process is governed by chemical mediators, known as cytokines, that are secreted by the macrophage itself and induce cellular activation and recruitment. Cytokines determine the macrophage phenotype within a heterogeneous range that spans between two extremes: pro-inflammatory or M1 and anti-inflammatory (or pro-healing) or M2. Fibroblasts are recruited in response to cytokine secretion and play a crucial role in tissue remodeling. These cells generate key components of the extracellular matrix (ECM), such as elastin, fibrin, and collagen, and have the ability to isolate the implanted biomaterial from surrounding tissue by encapsulating it within a fibrotic layer. The formation of this fibrotic capsule is a major factor contributing to the failure of many biomaterials. Macrophage and fibroblasts interact in tissues both in physiological and pathological conditions. One of the major signaling factor is the colony-stimulating factor 1 (CSF1) and its specific receptor (CSF1R). In the past, simulation works have focused only on the description of the phenotype transition from M1 to M2 for macrophages, possibly connected to physiological and pathological conditions (e.g. hypoxia), but neglecting the relevant macrophage/fibroblast interaction. Our long term aim is to exploit an agent-based (AB) modeling approach to develop a predictive digital twin for simulating the response over time of the cell populations involved in a FBR. Our first step in this direction is the explicit introduction of the interaction between macrophages and fibroblasts. To achieve this goal, we consider here at first the existing ordinary differential equation (ODE) and AB models, that simulate intra- and inter-cellular dynamics for macrophages, respectively. We validate them against in vitro data taken from experiments that recapitulate the reaction to a pathogen and in vivo data taken from the literature. This approach highlights a better agreement of the AB model over the ODE models taken into account in our study. Therefore, we propose a more advanced and comprehensive simulation platform based on AB modeling, which also includes fibroblasts and their mutual interaction with macrophages, as well as fibrosis resulting from the implantation of a biomaterial, allowing us to simulate in vivo scenarios. We validate this tool on experimental results from the literature finding a remarkable agreement. The application of this extended AB model allows us to replicate the kinetics of the cell populations involved, including, among others, the effect of different types of stimulus, chemotaxis, recruitment, and formation of the fibrotic capsule typical of the chronic FBR.

Indexed as

Biocompatible MaterialsCell CommunicationFibroblastsMacrophagesModels, BiologicalAnimalsCytokinesForeign-Body ReactionHumansBiocompatible MaterialsCytokines

Identifiers

PMID40828805
PMCPMC12364366

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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.