Optimization and characterization of injectable chitosan‐iodixanol‐based hydrogels for the embolization of blood vessels
Bibliographic record
Abstract
Chitosan-thermosensitive hydrogels present interesting features for the embolization of blood vessels, but need to be better characterized and optimized. Chitosan polymer (degree of deacetylation (DDA) of 94%) was purified and combined with Visipaque (VIS), a nonionic isotonic contrast agent composed of iodixanol. A beta-glycerolphosphate (βGP) solution was then added to induce gelation at body temperature. The gelation process was monitored by rheometry, measuring the evolution of the sample storage (G') and loss (G″) moduli as a function of VIS and βGP concentration. Adding VIS significantly slowed down gelation kinetics, but a 12% and higher βGP concentration provided a radiopaque solution, which at 37°C, gels immediately. A custom-made in vitro embolization bench test was developed to assess the gel's occlusive properties, and its injectability through a small diameter catheter was verified. Results show that the short-term occlusive properties of the gel were insufficient when using a βGP concentration of 12% w/v (about 0.4M), but that increasing the βGP to 20% (0.6M) allowed an acceleration of the gelation and the immediate blocking of flow above physiological pressure. The contrast agent was rapidly released in solution, such that it would not interfere with future follow-up imaging. In accordance with the literature data, the cytotoxicity of gel extracts increased with βGP concentration and to a lesser extent with VIS concentration. Preliminary in vivo testing showed easy injection by catheter and good visibility under fluoroscopy. These results suggest that radiopaque CH/βGP20%/VIS hydrogels present significant potential as embolizing agents for blood vessels and aneurysms. © 2015 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 104B: 1551-1562, 2016.
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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".