Hydroxybutyl Chitosan Polymer-Mediated CD133 Antibody Coating of Metallic Stents to Reduce Restenosis in a Porcine Model of Atherosclerosis
Bibliographic record
Abstract
Antibody-coated stents to capture circulating endothelial progenitor cells (EPCs) for re-endothelialization appear to be a novel therapeutic option for the treatment of atherosclerotic disease. Hydroxybutyl chitosan (HBC), a linear polysaccharide made from shrimps and other crustacean shells, is biocompatible, nontoxic, and hydrophilic, making it ideal for biomedical applications. In this study, HBC was explored for the immobilization of anti-CD133 antibodies. We demonstrated that CD133 antibodies mediated by HBC were successfully coated on cobalt-chromium alloy discs and metal stents. The coating was homogeneous and smooth as shown by electronic microscopy analysis. Balloon expansion of coated stents did not cause cracking or peeling. The HBC discs promoted CD133+ EPCs and human umbilical vein endothelial cell growth in vitro. The CD133 antibody-coated but not bare discs bound CD133+ EPCs in vitro. Implantation of CD133 antibody-coated stents significantly inhibited intimal hyperplasia and reduced restenosis compared with implantation of bare stents in a porcine model of atherosclerosis. These findings suggest HBC is a valuable anchoring agent that can be applied for bioactive coating of stents and that CD133 antibody-coated stents might be a potential therapeutic alternative for the treatment of atherosclerotic disease.
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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".