Intra-arterial injection of mesenchymal stem cells to accelerate neointima formation after endovascular stenting in a rabbit model
Bibliographic record
Abstract
OBJECTIVE: Delayed neointima formation over a neurovascular stent is associated with thrombotic complications that can lead to stroke. The purpose of this study was to evaluate whether an intra-arterial injection of mesenchymal stem cells (MSCs) after stent placement leads to improved neointima and reduced thrombus formation over the device. METHODS: Solitaire stents were placed into the aortas of rabbits that were divided into MSC and control groups. The MSC group received an intra-arterial injection of MSCs through the same microcatheter used for stent deployment. Optical coherence tomography (OCT) was used to evaluate and compare neointima and thrombus formation in a blinded fashion. Explanted specimens were also imaged with scanning electron microscopy (SEM) and evaluated by observers blinded to group allocation using an endothelialization scoring system. RESULTS: The 3-day MSC group was similar to the 7-day controls in terms of stent strut coverage ratio and maximum neointimal thickness, but these values were significantly higher than the 3-day control group based on a hierarchical mixed-effects linear regression analysis. SEM revealed a significantly higher endothelialization score for the MSC group compared with controls at the same time point. There was no difference in thrombus formation between any of the groups. CONCLUSIONS: The intra-arterial injection of MSCs after endovascular stenting accelerated early neointima formation but had no effect on thrombus formation in this study. Larger studies are required to verify these findings and determine the durability and mechanism of this effect.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".