Transient Overexpression of Gremlin Results in Epithelial Activation and Reversible Fibrosis in Rat Lungs
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic disease of the lung parenchyma, without curative treatment. Gremlin is a bone morphogenic protein (BMP) antagonist, its expression being increased in IPF lungs. It has been implicated in promoting myofibroblast accumulation, likely through inhibited fibroblast apoptosis and epithelial-to-mesenchymal transition. In the current study, we examined the effects of selective adenovirus-mediated overexpression of Gremlin in rat lungs. We show that transient Gremlin overexpression results in activation of alveolar epithelial cells with proliferation and apoptosis, as well as partly reversible lung fibrosis. We found myofibroblasts arranged in fibroblastic foci. Fibroblast proliferation occurred delayed as compared with epithelial changes. Fibrotic pathology significantly declined after Day 14, the reversal being associated with an increase of the epithelium-protective element, fibroblast growth factor (FGF)-10. Our data indicate that Gremlin-mediated BMP inhibition results in activation of epithelial cells and transient fibrosis, but also induction of epithelium-protective FGF10. A Gremlin-BMP-FGF10 loop may explain these results, and demonstrate that the interactions between different factors are quite complex in fibrotic lung disease. Increased Gremlin expression in human IPF tissue may be an expression of continuing epithelial injury, and Gremlin may be part of activated repair mechanisms.
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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 | 0.001 |
| 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".