The Platelet-Derived Growth Factor Pathway in Pulmonary Arterial Hypertension: Still an Interesting Target?
Bibliographic record
Abstract
The lack of curative options for pulmonary arterial hypertension drives important research to understand the mechanisms underlying this devastating disease. Among the main identified pathways, the platelet-derived growth factor (PDGF) pathway was established to control vascular remodeling and anti-PDGF receptor (PDGFR) drugs were shown to reverse the disease in experimental models. Four different isoforms of PDGF are produced by various cell types in the lung. PDGFs control vascular cells migration, proliferation and survival through binding to their receptors PDGFRα and β. They elicit multiple intracellular signaling pathways which have been particularly studied in pulmonary smooth muscle cells. Activation of the PDGF pathway has been demonstrated both in patients and in pulmonary hypertension (PH) experimental models. Tyrosine kinase inhibitors (TKI) are numerous but without real specificity and Imatinib, one of the most specific, resulted in beneficial effects. However, adverse events and treatment discontinuation discouraged to pursue this therapy. Novel therapeutic strategies are currently under experimental evaluation. For TKI, they include intratracheal drug administration, low dosage or nanoparticles delivery. Specific anti-PDGF and anti-PDGFR molecules can also be designed such as new TKI, soluble receptors, aptamers or oligonucleotides.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".