Employing the Sirolimus-Eluting Poly (Propylene Carbonate) Mesh for the Prevention of Arteriovenous Graft Stenosis in Rats
Bibliographic record
Abstract
Poly (propylene carbonate, PPC) is a new member of the aliphatic polyester family. An outstanding feature of PPC is that it produces mainly water and carbon dioxide when degraded in vivo, causing minimal side effects. This unique property together with excellent biocompatibility and biodegradability makes PPC a promising material for drug delivery. In this study, we explored the effect of the sirolimus (an inhibitor of cell growth)-eluting PPC mesh on graft stenosis and its possible mechanisms in a rat arteriovenous grafting model. The PPC mesh was prepared by electrospinning. A jugular vein to abdominal aortic autograft transplantation model was established in rats. The graft was then treated by wrapping with the drug mesh or the drug-free mesh or left untreated. Four weeks posttransplantation, neointima was measured with hematoxylin and eosin staining, matrix metalloproteinase-2 (MMP-2), and MMP-9, and proliferating cell nuclear antigen (PCNA) in the grafts were assayed by Western blotting and immunohistochemistry, respectively. In vitro rat aortic adventitial fibroblast cell (RAAFC) migration was assessed using the Boyden chamber assay, and phospho-mammalian target of rapamycin (mTOR) levels in RAAFCs were determined by Western blotting. Animals with the drug mesh had an intimal area index of 4.87% ± 0.98%, significantly lower than that of the blank group (14.21% ± 2.56%) or the PPC group (15.03% ± 2.35%, both P < .05). The sirolimus mesh markedly suppressed MMP-2 and MMP-9 expression, decreased PCNA-positive cell numbers, inhibited RAAFC migration, and reduced phospho-mTOR levels. Our data suggest that the sirolimus-eluting PPC mesh might be potentially applied for the management of grafting stenosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".