Modeling Mechanical Activation of Macrophages During Pulmonary Fibrogenesis for Targeted Anti-Fibrosis Therapy
Bibliographic record
Abstract
Pulmonary fibrosis, as seen in idiopathic pulmonary fibrosis (IPF) and COVID-induced pulmonary fibrosis, is an often-fatal lung disease. Increased numbers of immune cells such as macrophages were shown to accumulate in the fibrotic lung, but it is unclear how they contribute to the development of fibrosis. To recapitulate the macrophage mechanical activation in the fibrotic lung tissue microenvironment, we developed a fibrotic microtissue model with cocultured human macrophages and fibroblasts. We show that profibrotic macrophages seeded on topographically controlled stromal tissue constructs become mechanically activated. The resulting co-alignment of macrophages, collagen fibers and fibroblasts promote widespread fibrogenesis in micro-engineered lung tissues. Anti-fibrosis treatment using pirfenidone disrupts the polarization and mechanical activation of profibrotic macrophages, leading to fibrosis inhibition. Pirfenidone inhibits the mechanical activation of macrophages by suppressing integrin αMβ2 (CD11b/CD18) and Rho-associated kinase 2, which is a previously unknown mechanism of action of the drug. Together, these results demonstrate a potential pulmonary fibrogenesis mechanism at the tissue level contributed by mechanically activated macrophages. We propose the coculture, force-sensing microtissue model as a powerful tool to study the complex immune-stromal cell interactions and the mechanism of action of anti-fibrosis drugs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".