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Record W4393319429 · doi:10.1126/sciadv.adj9559

Modeling mechanical activation of macrophages during pulmonary fibrogenesis for targeted anti-fibrosis therapy

2024· article· en· W4393319429 on OpenAlexaff
Ying Xu, Linxuan Ying, Jennifer K. Lang, Boris Hinz, Ruogang Zhao

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNational Heart, Lung, and Blood Institute
KeywordsPirfenidoneFibrosisPulmonary fibrosisMacrophageStromal cellIdiopathic pulmonary fibrosisImmune systemCancer researchLungImmunologyMedicinePathologyChemistryInternal medicineIn vitro

Abstract

fetched live from OpenAlex

Pulmonary fibrosis is an often fatal lung disease. Immune cells such as macrophages were shown to accumulate in the fibrotic lung, but their contribution to the fibrosis development is unclear. To recapitulate the involvement of macrophages in the development of pulmonary fibrosis, we developed a fibrotic microtissue model with cocultured human macrophages and fibroblasts. We show that profibrotic macrophages seeded on topographically controlled stromal tissues became mechanically activated. The resulting co-alignment of macrophages, collagen fibers, and fibroblasts promoted 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 and Rho-associated kinase 2. These results demonstrate a potential pulmonary fibrogenesis mechanism at the tissue level contributed by macrophages. The cocultured microtissue model is a powerful tool to study the immune-stromal cell interactions and the anti-fibrosis drug mechanism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.300
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2024
Admission routes1
Has abstractyes

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