Growth Factors Stimulate Neointimal Cells In Vitro and Increase the Thickness of the Neointima Formed at the Neck of Porcine Aneurysms Treated by Embolization
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Growth factors (GFs) may favor the healing of aneurysms treated with endovascular techniques by stimulating neointima formation. METHODS: Bilateral carotid aneurysms were constructed with venous pouches in 50 pigs and embolized intraoperatively with collagen sponges with and without GFs (platelet-derived growth factor-BB [PDGF-BB] 0.15 or 1.5 microg or transforming growth factor-beta(1) [TGF-beta(1)] 60 or 600 ng) in each animal. DNA synthesis, cell proliferation, and collagen secretion assays were performed to assess the in vitro effects of GFs on neointimal cells harvested from the treated aneurysms. (125)I-PDGF-BB was used to study in vivo GF release from sponges. The thickness of the neointima at the surface of the sponges was measured 2 weeks after surgery. Since porcine aneurysms tend to heal after collagen sponge embolization, this experiment was repeated in dogs, which have shown a propensity for recurrence with the same technique, with 600 ng TGF-beta(1) or platelet extracts. RESULTS: PDGF-BB stimulated DNA synthesis and cell proliferation, while TGF-beta(1) strongly increased collagen synthesis of neointimal cells in vitro. Clearance of (125)I-PDGF-BB from the sponges followed a biphasic curve, with 1.5% of exogenous PDGF-BB remaining at 1 week. The local delivery of PDGF-BB (0.15 or 1.5 microg) and TGF-beta(1) (600 ng) significantly increased neointimal thickness at the neck of porcine aneurysms, while 60 ng of TGF-beta(1) had no demonstrable effect. TGF-beta(1) (600 ng) or platelet extracts had no influence on canine aneurysms. CONCLUSIONS: PDGF-BB and TGF-beta(1) can stimulate neointimal cells in vitro and neointima formation in vivo, but TGF-beta(1) and platelet extracts do not compensate for deficient thrombosis in canine aneurysms. Effects on the long-term results of embolization remain speculative.
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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".