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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".