Regulatory T Cells Limit Pneumococcus-Induced Exacerbation of Lung Fibrosis in Mice
Bibliographic record
Abstract
Patients with idiopathic pulmonary fibrosis (IPF) can experience life-threatening episodes of acute worsening of their disease, termed acute exacerbation of IPF, which may be caused by bacterial and/or viral infections. The potential for regulatory T cells (Tregs) to limit disease progression in bacterially triggered fibrosis exacerbation has not been explored so far. In the current study, we show that the number of Tregs was significantly increased in mice with established AdTGF-β1-induced lung fibrosis and further increased in mice with pneumococcal infection-induced lung fibrosis exacerbation. Diphtheria toxin-induced depletion of Tregs significantly worsened infection-induced fibrosis exacerbation as determined by increased lung collagen deposition, lung histology, and elevated pulmonary Th1/Th2 cytokine levels. Conversely, IL-2 complex-induced Treg expansion in wild-type mice with established lung fibrosis completely inhibited pneumococcal infection-induced fibrosis exacerbation as efficaciously as antibiotic treatment while preserving lung antibacterial immunity in mice. Collectively, these findings demonstrate the efficacy of Tregs as "silencers," suppressing infection-induced exacerbation of lung fibrosis in mice, and their expansion may offer a novel adjunctive treatment to limit acute exacerbations in patients with IPF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".