Photosensitive Hydrogel-Based Embolic Agent Treatment of Wide-Necked Aneurysms: Preliminary Animal Results
Bibliographic record
Abstract
Background: The endovascular treatment of cerebral aneurysms has become widespread but may still be limited by recurrence rates or complications. The discovery of novel embolic strategies may help mitigate these concerns. Methods: We formulated a Photosensitive Hydrogel Polymer (PHP) embolic agent which is low-viscosity, shear-thinning, and radio-opaque. After the filling of an aneurysm with PHP with balloon assistance, we utilized photopolymerization to induce solidification. Different methods of light delivery for photopolymerization were assessed in silicone models of aneurysms and in four acute animal trials with venous anastomosis aneurysms in pigs. Then, balloon-assisted embolization with PHP and photopolymerization was performed in three aneurysms in pigs with a one-month follow-up. Filling volume, recurrence rates, and complications were recorded. Results: The PHP was found to be suitable for the intravascular delivery and treatment of cerebral aneurysms. It was found that light delivery through the balloon catheter, as opposed to light delivery through the injection microcatheter, led to higher rates of filling in the 3D model and acute animal model for cerebral aneurysms. Using the balloon-assisted embolization and light delivery strategy, three wide-necked aneurysms were treated without complication. One-month follow-up showed no recurrence or neck remnants. Conclusions: We demonstrated a novel method of balloon-assisted photosensitive hydrogel polymer embolization and photopolymerization, leading to complete aneurysm filling with no recurrence at 1 month in three wide-necked aneurysms in pigs. This promising methodology 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".