Plumbagin attenuates Bleomycin-induced lung fibrosis in mice
Bibliographic record
Abstract
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fatal fibrotic lung disease with limited treatment options. Plumbagin (PL) is an herbal extract with diverse pharmacological effects that have been recently used to treat various types of cancer. This study aims to explore the anti-fibrotic effect of PL and possible underlying mechanisms in IPF. METHODS: We used a bleomycin-induced experimental mouse model of lung fibrosis to assess the potential anti-fibrotic effect of PL. Histological analysis of lung tissue samples by H&E and Masson's trichrome staining and hydroxyproline assay was performed to evaluate the fibrotic alterations. ELISA and real-time quantitative PCR were conducted to determine the amount of tumor necrosis factor-alpha (TNFα), tumor growth factor-beta (TGF-β), connective tissue growth factor (CTGF), and endothelin-1 (ET-1). RESULTS: Bleomycin exposure induced lung fibrosis, which was indicated by inflammation, collagen deposition, and structural damage. PL remarkably prevented bleomycin-induced lung fibrosis. Furthermore, PL significantly inhibited TNF-α and TGF-β production. PL also diminished the upregulated expression of CTGF and ET-1 induced by bleomycin. CONCLUSION: Overall, our findings suggest PL as an anti-fibrotic agent acting via down-regulation of TGF-β/CTGF or ET-1 axis, as well as TNF-α, to improve lung fibrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| 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 | 0.001 |
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".