Anti-inflammatory and anti-fibrotic effects of sirolimus on bleomycin-induced pulmonary fibrosis in rats
Bibliographic record
Abstract
PURPOSE: Pulmonary fibrosis is a devastating disease with a poor prognosis. Although the diagnosis and pathophysiology of this disease have been better characterized over the past few years, there is no effective therapy for the disease. The aim of this study was to evaluate the anti-inflammatory and anti-fibrotic effects of sirolimus (SRL), which is a potential anti-fibrotic agent, by using bleomycin (BLM)-induced pulmonary fibrosis model in rats. METHODS: A single intra-tracheal injection of BLM (2.5 U/kg) was administered and sirolimus (2.5 mg/kg/day) was given orally, beginning either one day before (early SRL) or nine days after (late SRL) the BLM administration. The effect of SRL on fibrosis was studied by analysis of cytokine levels in BAL fluid, measurement of lung tissue hydroxyproline (HPL) content and histopathological examination. RESULTS: Both early and late SRL administrations caused a decrease in the levels of IL-13, PDGF-A and TGF-β1 (p=0.001) and an increase in IFN-γ levels (p=0.001) in BAL fluid. Early and late SRL also caused a decrease in HPL content (p=0.001). Early sirolimus caused a significant decrease in fibrosis score (p=0.001), while late SRL did not. CONCLUSION: Sirolimus was effective in BLM-induced pulmonary fibrosis model, especially in the early phases of the disease.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".