Transient expression of IL-1β induces acute lung injury and chronic repair leading to pulmonary fibrosis
Bibliographic record
Abstract
IL-1beta is one of a family of proinflammatory cytokines thought to be involved in many acute and chronic diseases. Although it is considered to participate in wound repair, no major role has been attributed to IL-1beta in tissue fibrosis. We used adenoviral gene transfer to transiently overexpress IL-1beta in rat lungs after intratracheal administration. The high expression of IL-1beta in the first week after injection was accompanied by local increase of the proinflammatory cytokines IL-6 and TNF-alpha and a vigorous acute inflammatory tissue response with evidence of tissue injury. The profibrotic cytokines PDGF and TGF-beta1 were increased in lung fluid samples 1 week after peak expression of IL-1beta. Although PDGF returned to baseline in the third week, TGF-beta1 showed increased concentrations in bronchoalveolar lavage fluid for up to 60 days. This was associated with severe progressive tissue fibrosis in the lung, as shown by the presence of myofibroblasts, fibroblast foci, and significant extracellular accumulations of collagen and fibronectin. These data directly demonstrate how acute tissue injury in the lung, initiated by a highly proinflammatory cytokine, IL-1beta, converts to progressive fibrotic changes. IL-1beta should be considered a valid target for therapeutic intervention in diseases associated with fibrosis and tissue remodeling.
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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".