Dynamic laser-based photomodulation of endovascular hydrogel embolization for the treatment of various cerebrovascular disorders
Bibliographic record
Abstract
Introduction: Endovascular embolization is becoming an increasingly utilized method of treating a variety of neurovascular disorders, including aneurysms, arteriovenous malformations (AVMs), and tumors. Many of the existing limitations of this treatment are related to the embolization agents currently available, including material compaction or migration, disease recurrence, or off-target embolization. Hydrogels are a promising class of materials that may be utilized to address some of these concerns. Methods: We compounded hydrogel formulations that were low-viscosity, shear thinning, photo-sensitive, and radioopaque. We developed a method of intravascular micro-catheter hydrogel delivery with dynamic modulation of hydrogel physical characteristics at the tip of the catheter, via photo-crosslinking with an integrated UV emitting optical fibre. This allowed for rapid transition from liquid to solid state to block blood flow at the vascular target, as well as dynamic modulation to suit the needs of a variety of neurovascular disorders. We performed preliminary testing of this novel methodology in animal models of neurovascular disease. Results: With dynamic modulation of photo-crosslinking, we were able to deliver hydrogels with a viscosity range of up to 10^4 Pa*s. The technique allowed for successful deposition of the hydrogel precursor in animal models for aneurysms, AVMS, and tumors. Post-procedural angiography demonstrated satisfactory occlusion of target vessels without evidence of complications. Conclusions: This novel embolization method holds promise in improving the safety and efficacy of the endovascular treatment of a variety of different pathologies and should be investigated further with direct comparative studies.
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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) | 0.000 | 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) | 0.001 | 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".