Peptide‐modified methacrylated glycol chitosan hydrogels as a cell‐viability supporting pro‐angiogenic cell delivery platform for human adipose‐derived stem/stromal cells
Bibliographic record
Abstract
Abstract Cell‐based therapies involving the injection of adipose‐derived stem/stromal cells (ASCs) within rationally designed biomaterials are a promising approach for stimulating angiogenesis. With this focus, the current work explored the effects of incorporating integrin‐binding RGD or IKVAV peptides within in situ‐gelling N‐methacrylate glycol chitosan (MGC) hydrogels on the response of encapsulated human ASCs. Initial studies focused on hydrogel characterization to validate that the MGC, MGC‐RGD, and MGC‐IKVAV hydrogels had similar biomechanical properties. ASC viability following encapsulation and culture under 2% O2 was significantly impaired in the MGC‐IKVAV group relative to the MGC and MGC‐RGD groups. In contrast, sustained viability, along with enhanced cell spreading and metabolic activity were observed in the MGC‐RGD group. Investigation of angiogenic transcription suggested that the incorporation of the peptide groups did not substantially alter the pro‐angiogenic gene expression profile of the encapsulated ASCs after 7 days of culture under 2% O2. Consistent with the in vitro findings, preliminary in vivo characterization following subcutaneous implantation into NOD/SCID mice showed that ASC retention was enhanced in the MGC‐RGD hydrogels relative to the MGC‐IKVAV group at 14 days. Further, the encapsulated ASCs in the MGC and MGC‐RGD groups promoted murine CD31+ endothelial cell recruitment to the peri‐implant region. Overall, the results indicate that the MGC‐RGD and MGC hydrogels are promising platforms for ASC delivery, and suggest that strategies that support long‐term ASC viability can augment in vivo angiogenesis through paracrine mechanisms. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 107A: 571–585, 2019.
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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".