Polydopamine-modified collagen sponge scaffold as a novel dermal regeneration template with sustained release of platelet-rich plasma to accelerate skin repair: A one-step strategy
Bibliographic record
Abstract
Although employed to release growth factors (GFs) for regenerative medicine, platelet-rich plasma (PRP) has been hindered by issues like burst effect. Based on collagen sponge scaffolds (CSSs) modified with polydopamine (pDA), a novel dermal regeneration template (DRT) was designed. However, whether it could efficiently deliver PRP and even foster wound healing remained unclear. In this work, after PRP was prepared and pDA-modified CSSs (pDA-CSSs) were fabricated, microscopic observation, GFs release assay and in-vitro biological evaluations of pDA-CSSs with PRP (pDA-CSS@PRP) were performed, followed by BALA-C/nu mice full-thickness skin defects implanted with pDA-CSS@PRP covered by grafted skins (termed as a One-step strategy). As a result, scanning electron microscope demonstrated more immobilized platelets on pDA-CSS' surface with GFs' controlled release via enzyme-linked immunosorbent assay, compared with CSSs. In line with enhanced in-vitro proliferation, adhesion and migration of keratinocytes & endothelial cells, pDA-CSS@PRP were histologically revealed to accelerate wound healing with less scar via rapid angiogenesis, arrangement of more mature collagen, guiding cells to spread, etc. In conclusion, pDA-CSSs have potential to serve as a novel DRT capable of delivering PRP, which may foster full-thickness skin defect healing by means of a One-step strategy.
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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".