Eosinophils and T Lymphocytes Possess Distinct Roles in Bleomycin-Induced Lung Injury and Fibrosis
Bibliographic record
Abstract
Leukocyte infiltration is characteristic of lung injury and fibrosis, and its role during tissue repair and fibrosis is incompletely understood. We found that overexpression of IL-5 in transgenic mice (IL-5(TG)) or by adenoviral gene transfer increased bleomycin (blm)-induced lung injury, fibrosis, and eosinophilia. Surprisingly, blm-treated IL-5-deficient (IL-5(-/-)) mice also developed pronounced pulmonary fibrosis but characterized by marked T lymphocyte infiltration and absence of eosinophilia. In both murine strains however, induction of lung TGF-beta expression was evident. Purified lung eosinophils from blm-treated IL-5(TG) mice stimulated alpha-smooth muscle actin and collagen expression in mouse lung fibroblasts, without affecting proliferation. Furthermore instillation of purified eosinophils into murine lungs resulted in extension of blm-induced lung fibrosis, thus confirming a role for eosinophils. However, lung T lymphocytes from blm-treated IL-5(-/-) mice were able to stimulate fibroblast proliferation but not alpha-smooth muscle actin or collagen expression. Blocking T cell influx by anti-CD3 Abs abrogated lung fibrosis, thus also implicating T lymphocytes as a key participant in fibrosis. Pulmonary fibrosis in IL-5(TG) mice was preferentially associated with type 2 cytokines (IL-4 and IL-13), whereas fibrotic lesions in IL-5(-/-) animals were accompanied by proinflammatory cytokine (TNF-alpha, IL-1beta, and IFN-gamma) expression. We suggest that eosinophils and T cells contribute distinctly to the development of blm-induced lung fibrosis potentially via their production of different cytokine components, which ultimately induce TGF-beta expression that is intimately involved with the fibrosis.
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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".