A laminin mimetic peptide SIKVAV-conjugated chitosan hydrogel promoting wound healing by enhancing angiogenesis, re-epithelialization and collagen deposition
Bibliographic record
Abstract
Angiogenesis and re-epithelialization are critical factors in skin wound healing. Growth factors and stem cells have demonstrated their active roles in promoting these two processes. Peptides show similar effects with growth factors with lower cost and controllable properties. Here we report a biomimetic fragment of the laminin -Ser-Ile-Lys-Val-Ala-Val (SIKVAV)-conjugated chitosan hydrogel that can promote skin regeneration. In vitro we found that this peptide-conjugated hydrogel significantly promoted BMSC adhesion and proliferation. In vivo, this hydrogel accelerated wound contraction. The subcutaneous implantation test and H&E staining results revealed that the peptide-modified chitosan hydrogel dramatically led to the formation of new blood vessels. Moreover, Masson staining showed that many newborn collagen fibers appeared in the peptide hydrogel group, while only a few newborn collagen fibers were found in control and chitosan hydrogel groups. The peptide chitosan hydrogel also re-epithelialized quickly, while the control and chitosan hydrogel took more time to complete. These results suggest that the SIKVAV peptide is an effective motif to significantly improve the function of chitosan in angiogenesis and re-epithelialization of skin.
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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".