P.191 Development and Testing of a Novel Hydrogel Embolization Treatment for Neurovascular Diseases: Preliminary Animal Results
Bibliographic record
Abstract
Background: Embolization represents a minimally invasive treatment modality for arteriovenous malformations (AVMs), tumors, aneurysms, and vessel sacrifice, but can be limited by currently available embolization agents. Discovery of new and improved agents could lead to better treatment outcomes. The goal of this project was to develop and test a novel embolization agent using hydrogels, a class of materials which may be bioengineered to suit a variety of indications. Methods: We devised a method of liquid hydrogel embolization with photo-modulated crosslinking for intravascular solidification, using a custom microcatheter set-up. We tested this in swine blood vessels (n=3), the swine renal arterial trees as a vascular tumor model (n=5), and the swine arterial-arterial networks of the rete mirabile as an AVM model (n=3). Hydrogel embolization was assessed for treatment efficacy and safety. Follow-up angiography was performed at 2-4 week intervals. Results: Hydrogel embolization was technically successful in all animals, with full occlusion of the vascular target immediately following embolization and at follow-up. There were no instances of clinical or angiographic complications. Conclusions: We demonstrated a novel method of dynamic photomodulation and delivery of bioengineered hydrogels to address current limitations of endovascular embolization therapies. This promising technology will be investigated further with longer-term comparative animal trials.
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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) | 0.000 | 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) | 0.003 | 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".