PBI-compound, a novel first-in-class anti-inflammatory/fibrotic compound, reduces bleomycin-induced pulmonary fibrosis by regulating inflammatory cytokines in bronchoalveolar fluid
Bibliographic record
Abstract
Background : We recently reported that PBI-Compound demonstrated anti-inflammatory and anti-fibrotic activities in acute and chronic kidney disease models. Inflammatory cytokines play a key role in the pathogenesis of pulmonary fibrosis. Aims : To determine the effect of PBI-Compound on bleomycin-induced lung fibrosis at the pro-inflammatory/fibrotic cytokine level and histological lesions. Methods : C57BL/6 mice received bleomycin by intratracheal instillation on day 0, and then were treated with oral administration of PBI-Compound from day 7 to 21. Mice were euthanized on day 21 and protein level of IFN-γ, IL-1β and TNF-α was quantified in the bronchoalveolar lavage fluid (BALF). Results : The results show that intratracheal instillation of bleomycin induced a significant increase in CTGF, IL-1β and TNF-α in BALF. PBI-Compound treatment significantly decreased the amount of CTGF close to the level observed in the control group. IL-1β and TNF-α were also reduced by 20-30%. Regulation of these cytokines correlated with the histological observations from HEP and Masson’s trichrome staining of the lung tissue. Bleomycin-induced widening and filling of alveolar spaces with collagen fibers indicated proliferative fibroblastic lesions that were significantly reduced with the oral treatment of PBI-Compound. Conclusions : The data suggests that treatment with PBI Compound may be beneficial in preventing the progression of lung injury by reducing tissue fibrosis and regulating key cytokines.
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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".