γδ T cell-stromal networks modulate matrix composition and vascularity in foreign body response
Bibliographic record
Abstract
Abstract Immune-stromal crosstalk governs tissue fibrosis, which is marked by dysregulated extracellular matrix (ECM) production and aberrant vasculature. Here, we investigate how γδ T cell interactions with stromal cells shape fibrosis in the foreign body response. During the acute reaction, type-1 (γδIFNγ) and type-17 (γδ17) effector subsets accumulated at the implant. While γδIFNγ decreased as fibrosis progressed, activated γδ17 persisted as dominant interleukin-17 producers. The γδ17 increased with aging and high-fat diet, both factors associated with chronic inflammation and fibrosis. Co-culture with γδ17 stimulated fibroblast expression of collagen genes and intercellular communication inference linked γδ T cell ligands to activation of ECM remodeling and vascular development programs in fibroblasts and endothelial cells. Finally, genetic deletion of γδ T cells altered expression of ECM components and increased vessel size within the fibrotic matrix. Altogether, our findings implicate γδ T cells in regulating stromal behavior to modulate composition and vascularity of fibrotic tissues.
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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 | 0.000 | 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".