Investigating the differential effects of platelet-derived growth factor (PDGF) isoforms in lung and dermal fibroblasts
Bibliographic record
Abstract
Background: Systemic scleroderma-associated interstitial lung disease (SSc-ILD) is the primary cause of mortality in patients with SSc due to limited therapeutics, none of which are capable of reversing or inhibiting lung fibrosis. Patients exhibit excessive extracellular matrix (ECM) deposition through fibroblast dysfunction and upregulation of profibrotic growth factors such as PDGF. Aim: To investigate the effects of the five PDGF isoforms (AA, AB, BB, CC, and DD) on lung and dermal fibroblast functions. Methods: Human lung and dermal fibroblast cell lines were treated with the PDGF isoforms at 25 ng/mL and assessed for interleukin (IL)-11 using ELISA, collagen-I, fibronectin, and α-smooth muscle actin (α-SMA) using western blot, and proliferation. Results: In dermal and lung fibroblasts, PDGF-BB induced a 3.3-fold and 3.9-fold increase in the release of IL-11 versus control (p<0.01). Isoforms AB, CC, and DD induced IL-11 release in lung fibroblasts to a lesser extent (p<0.05). In dermal fibroblasts, only PDGF-BB induced a 1.9-fold increase in proliferation (p<0.0001); no isoform affected lung fibroblast proliferation. Isoforms BB and DD induced a dose-dependent increase of fibronectin in lung fibroblasts, but not in dermal fibroblasts. No isoform induced α-SMA expression. Isoforms AB, BB, and CC caused a dose-dependent decrease of collagen-I in dermal fibroblasts, with AB and CC inducing a similar pattern in lung fibroblasts. Conclusion: PDGF-BB induced the profibrotic cytokine IL-11, fibronectin ECM expression, and proliferation of lung and dermal fibroblasts, indicating PDGF-BB-targeted therapies are likely to have beneficial effects in lung and skin fibrosis.
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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".