Interdependence of HIF1α /TGFβ1 activity in induction of pulmonary fibrosis
Bibliographic record
Abstract
Introduction: Pulmonary Fibrosis is a chronic lung disease likely involving multiple microinjuries to alveolar epithelium, resulting in excess deposition of extracellular matrix (ECM) and hardening/stiffening of tissue. Transforming growth factor β1 (TGFβ1) is a key regulator of ECM expression and is linked with the development of pulmonary fibrosis. Activation of Hypoxia inducible factor-1a (HIF1α) under hypoxic conditions in fibrotic lung is expected and could promote perpetuation of the disease. Objective: Evaluate if concomitant overexpression of HIF1α / TGFβ1 worsens or improves pulmonary fibrosis. Methods: Adenoviral vectors each 6 X 10 8 pfu of AdHIF1α /AdDL-70 (control), AdTGFβ1/AdDL-70 and AdHIF1α /AdTGFβ1 or AdDL70/AdDL70 in combination were administered intratracheally to rats. Lung extracts ELISA, western blot, RT-PCR, collagen assay and lung histopathology, elastance measures and HIF1α DNA binding assays were carried out to assess tissue fibrosis. Results: Concomitant administration of AdHIF1α/AdTGFβ1 enhances the fibrotic response. TGFβ1 is able to induce fibrosis with induction of HIF1α. Over expression of HIF1α alone induces limited fibrosis but enhances the ability of TGFβ1 to further develop sustained fibrosis. This study highlights a crucial role of TGFβ1 in mediating the effect of HIF1α and vice versa. Conclusion: Combined activation of HIF1α/TGF β1 in lungs enhances inflammation, marked distortion of lung structure, over expression of smooth muscle actin and plasminogen activator inhibitor-1, accumulation of collagen and increases lung elastance, indicating that HIF1α activation may therefore have a role in progression of chronic fibrotic diseases such as IPF.
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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".