Photomodulated Extrusion as a Localized Endovascular Hydrogel Deposition Method
Bibliographic record
Abstract
Minimally invasive endovascular embolization is used to treat a wide range of diseases in neurology, oncology, and trauma where the vascular morphologies and corresponding hemodynamics vary greatly. Current techniques based on metallic coils, flow diverters, liquid embolics, and suspended microspheres are limited in their ability to address a wide variety of vasculature and can be plagued by complications including distal migration, compaction, and inappropriate vascular remodeling. Further, these endovascular devices currently offer limited therapeutic functions beyond flow control such as drug delivery. Herein, a novel in situ microcatheter-based photomodulated extrusion approach capable of dynamically tuning the physical and morphological properties of injectable hydrogels, optimizing for local hemodynamic environment and vascular morphology, is proposed and demonstrated. A shear thinning and photoactivated poly(ethylene glycol diacrylate)-nanosilicate (PEGDA-nSi) hydrogel is used to demonstrate multiple extrusion modes which are controlled by photokinetics and device configurations. Real-time photomodulation of injected hydrogel viscosity and modulus is successfully used for embolization in various vasculatures, including high-flow large vessels and arterial-to-arterial capillary shunts. Furthermore, a generalizable therapeutic delivery platform is proposed by demonstrating a core-shell structured extrusion encapsulating doxorubicin to achieve a more sustained release compared to unencapsulated payload.
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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".