Evaluation of a Collagen-Chitosan Hydrogel for Potential Use as a Pro-Angiogenic Site for Islet Transplantation
Bibliographic record
Abstract
Islet transplantation to treat type 1 diabetes (T1D) has shown varied long-term success, due in part to insufficient blood supply to maintain the islets. In the current study, collagen and collagen:chitosan (10:1) hydrogels, +/- circulating angiogenic cells (CACs), were compared for their ability to produce a pro-angiogenic environment in a streptozotocin-induced mouse model of T1D. Initial characterization showed that collagen-chitosan gels were mechanically stronger than the collagen gels (0.7 kPa vs. 0.4 kPa elastic modulus, respectively), had more cross-links (9.2 vs. 7.4/µm(2)), and were degraded more slowly by collagenase. After gelation with CACs, live/dead staining showed greater CAC viability in the collagen-chitosan gels after 18 h compared to collagen (79% vs. 69%). In vivo, collagen-chitosan gels, subcutaneously implanted for up to 6 weeks in a T1D mouse, showed increased levels of pro-angiogenic cytokines over time. By 6 weeks, anti-islet cytokine levels were decreased in all matrix formulations ± CACs. The 6-week implants demonstrated increased expression of VCAM-1 in collagen-chitosan implants. Despite this, infiltrating vWF(+) and CXCR4(+) angiogenic cell numbers were not different between the implant types, which may be due to a delayed and reduced cytokine response in a T1D versus non-diabetic setting. The mechanical, degradation and cytokine data all suggest that the collagen-chitosan gel may be a suitable candidate for use as a pro-angiogenic ectopic islet transplant site.
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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".