Novel temperature responsive polymer based sealant for embolization
Bibliographic record
Abstract
A sealant has been developed that improves upon current catheter-based treatments in the following ways: 1) Efficient delivery system, 2) No in situ polymerization, 3) No harmful byproducts, and 4) Cost-effective formulation. During the development process, particular attention was given to materials that were tunable, safe, and effective sealant agents. The thermo-responsive properties of poly(N-isopropylacrylamide) (PNIPAM) provides an ideal foundation to develop an optimized solution. Through a combination of model-based and material testing, a hydrogel was developed that balances conformational factors to achieve a customized transition temperature, radiopacity suitable for visualization, mechanical properties suitable for delivery via 3Fr catheter, sufficient cohesion once applied to resist migration under physiological pressures and an improved safety profile. Two applications, embolization of lymphatic leakage and exclusions of the left atrial appendage (LAA), to eliminate LAA dead space to reduce the risk of thromboembolic events, were considered. The material and benchtop results for this product demonstrate the suitability of this new material not only for these applications but also for other potential healthcare applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".