Injectable Shear‐Thinning Hydrogels with Sclerosing and Matrix Metalloproteinase Modulatory Properties for the Treatment of Vascular Malformations
Bibliographic record
Abstract
Abstract Sac embolization of abdominal aortic aneurysms (AAAs) remains clinically limited by endoleak recurrences. These recurrences are correlated with recanalization due to the presence of endothelial lining and matrix metalloproteinases (MMPs)‐mediated aneurysm progression. This study incorporates doxycycline (DOX), a well‐known sclerosant and MMPs inhibitor, into a shear‐thinning biomaterial (STB)‐based vascular embolizing hydrogel. The addition of DOX is expected to improve embolizing efficacy while preventing endoleaks by inhibiting MMP activity and promoting endothelial removal. The results show that STBs containing 4.5% w/w silicate nanoplatelet and 0.3% w/v of DOX are injectable and have a twofold increase in storage modulus compared to those without DOX. STB‐DOX hydrogels also reduced clotting time by 33% compared to untreated blood. The burst release of DOX from the hydrogels show sclerosing effects after 6 h in an ex vivo pig aorta model. Sustained release of DOX from hydrogels on endothelial cells shows MMP inhibition (approximately an order of magnitude larger than control groups) after 7 days. The hydrogels successfully occlude a patient‐derived abdominal aneurysm model at physiological blood pressures and flow rates. The sclerosing and MMP inhibition characteristics in the engineered multifunctional STB‐DOX hydrogels may provide promising opportunities for the efficient embolization of aneurysms in blood vessels.
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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) | 0.000 | 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".