PBI-compound, a novel first-in-class anti-inflammatory/fibrotic compound, reduces bleomycin-induced idiopathic pulmonary fibrosis by regulation of extracellular matrix remodelling
Bibliographic record
Abstract
Background : We recently reported that PBI-Compound demonstrated anti-inflammatory and anti-fibrotic activities in acute and chronic kidney disease models. Aims : To determine the effect of PBI-Compound on bleomycin-induced lung fibrosis. Methods : C57BL/6 mice received bleomycin by intratracheal instillation on day 0 and then treated with oral administration of PBI-Compound from day 7 to 21. Mice were euthanized on day 21 and fibrotic markers were quantified by RT-PCR. Histological grading was determined according to Ashcroft’s score. Results : RT-PCR analysis showed that intratracheal instillation of bleomycin induced a significant increase in collagen I, collagen III, fibronectin, SPARC and MMP-2 mRNA (fibrotic and remodeling markers). Expression of these markers was reduced in the mice treated with PBI-Compound. HEP and Masson’s trichrome staining showed that alveolar spaces in the lung tissue were widened and filled with collagen fibers, indicating proliferative fibroblastic lesions in bleomycin-induced lung fibrosis in mice. These lesions were significantly reduced with an oral treatment of PBI-Compound. Furthermore, treatment with PBI-Compound significantly reduced the percentage of lungs affected by bleomycin (60% for control versus 25% for PBI-Compound-treated mice). Conclusions : The data suggest that treatment with PBI-Compound may be beneficial in preventing the progression of lung injury by reducing tissue fibrosis and downregulating the transcription of extracellular matrix proteins including collagen I, collagen III, fibronectin, SPARC and MMP-2.
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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) | 0.003 | 0.001 |
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".