O-005 Mesenchymal stem cell-derived extracellular vesicles as a coiling adjunct to improve intracranial aneurysmal healing in a rabbit model
Bibliographic record
Abstract
Background Although endovascular coiling has become a standard of care for treatment of intracranial aneurysms, up to 30% of treated aneurysms will recur. Using mesenchymal stem cells (MSCs) as an adjunctive therapy can potentially improve aneurysm healing, but the injection of cells may be impractical for routine use. Moreover, increasing evidence has found that the therapeutic effects of MSCs may be due to their release of a heterogeneous population of lipid membrane-bound nanoparticles called extracellular vesicles (EVs). Similar to MSCs, EVs can also localize to areas of inflammation, but have many advantages over a cell-based therapy including a better safety profile, reduced immunogenicity, and simplified production and storage. (Yuana et al., 2013, Bang&Kim 2019; Natasha et al., 2014, Gonzalez-Gonzalez et al., 2020) The purpose of this study, therefore, was to determine the effect of MSC-derived EVs in an in vivo aneurysm model. Methods Aneurysms were created as previously described in two female New Zealand White rabbits (Belanger et al., 2021). Four weeks after creation, animals underwent digital subtraction angiography (DSA) to determine aneurysm size and patency. After the deployment of one to two framing coils in the aneurysm to stagnate flow, EVs from 6x107 adipose-derived MSCs were injected directly into the aneurysm sac using the same SL-10 catheter. Aneurysms were then coiled to completion with a goal packing density between 20% and 30%. Ninety days later, animals were sacrificed for histological processing and were compared to historical controls (Herting et al., 2019) in terms of aneurysm size, coil length per aneurysm, packing density, neointimal thickness, and histological healing score (Dai et al., 2006). Results For the experimental group, aneurysm size, coil length per aneurysm volume, and packing density were 61.78 mm3±22.11, 0.52 cm/mm3±0.036, and 26.24%±1.75 while Herting et al. reported 119.3 mm3±114.9, 0.417 cm/mm3±0.18, and 24.3%±7.8, respectively (mean±SD). There was no significant differences in these metrics between the two groups (p=0.53, 0.76, 0.50, respectively (Student’s T-test)). Comparison of neointimal thickness between the experimental group and historical controls was also not significantly different (0.03um±0.029 vs. 0.03um±0.01, p=0.99 (Student’s T-test)), although histological healing score was significantly higher in the experimental group (11.5±2.12 vs. 4.5±2.4, p=0.02) (figure 1). Conclusions MSC-derived EVs as an adjunct to endovascular coiling may improve the histological healing scores of aneurysms, potentially reducing the risk of aneurysm recurrence after endovascular coiling. Additional studies are needed to more rigorously investigate this effect. Disclosures B. Belanger: 1; C; NSERC Brain CREATE Program. J. Phelps: None. A. Bromley: None. A. Sen: 1; C; Natural Sciences and Engineering Research Council of Canada (NSERC), Office of the Vice President (Research), University of Calgary. A. Mitha: 1; C; Stryker Neurovascular, Fluid Biomedical. 2; C; Cerus Endovascular. 4; C; Fluid Biomedical.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".