Proteoglycan expression in bleomycin lung fibroblasts: role of transforming growth factor-β<sub>1</sub>and interferon-γ
Bibliographic record
Abstract
Bleomycin (BM)-induced pulmonary fibrosis involves excess production of proteoglycans (PGs). Because transforming growth factor-beta(1) (TGF-beta(1)) promotes fibrosis, and interferon-gamma (IFN-gamma) inhibits it, we hypothesized that TGF-beta(1) treatment would upregulate PG production in fibrotic lung fibroblasts, and IFN-gamma would abrogate this effect. Primary lung fibroblast cultures were established from rats 14 days after intratracheal instillation of saline (control) or BM (1.5 units). PGs were extracted and subjected to Western blot analysis. Bleomycin-exposed lung fibroblasts (BLF) exhibited increased production of versican (VS), heparan sulfate proteoglycan (HSPG), and biglycan (BG) compared with normal lung fibroblasts (NLF). Compared with NLF, BLF released significantly increased amounts of TGF-beta(1). TGF-beta(1) (5 ng/ml for 48 h) upregulated PG expression in both BLF and NLF. Incubation of BLF with anti-TGF-beta antibody (1, 5, and 10 microg/ml) inhibited PG expression in a dose-dependent manner. Treatment of BLF with IFN-gamma (500 U. ml(-1) x 48 h) reduced VS, HSPG, and BG expression. Furthermore, IFN-gamma inhibited TGF-beta(1)-induced increases in PG expression by these fibroblasts. Activation of fibroblasts by TGF-beta(1) promotes abnormal deposition of PGs in fibrotic lungs; downregulation of TGF-beta(1) by IFN-gamma may have potential therapeutic benefits in this 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) | 0.000 | 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 | 0.000 |
| 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".