Faculty Opinions recommendation of Painting blood vessels and atherosclerotic plaques with an adhesive drug depot.
Bibliographic record
Abstract
The treatment of diseased vasculature remains challenging, in part because of the difficulty in implanting drug-eluting devices without subjecting vessels to damaging mechanical forces.Implanting materials using adhesive forces could overcome this challenge, but materials have previously not been shown to durably adhere to intact endothelium under blood flow.Marine mussels secrete strong underwater adhesives that have been mimicked in synthetic systems.Here we develop a drug-eluting bioadhesive gel that can be locally and durably glued onto the inside surface of blood vessels.In a mouse model of atherosclerosis, inflamed plaques treated with steroid-eluting adhesive gels had reduced macrophage content and developed protective fibrous caps covering the plaque core.Treatment also lowered plasma cytokine levels and biomarkers of inflammation in the plaque.The drugeluting devices developed here provide a general strategy for implanting therapeutics in the vasculature using adhesive forces and could potentially be used to stabilize rupture-prone plaques.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.727 | 0.534 |
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".