Engineering homologous platelet-rich plasma, platelet-rich plasma-derived exosomes, and mesenchymal stem cell-derived exosomes-based dual-crosslinked hydrogels as bioactive diabetic wound dressings
Bibliographic record
Abstract
The management of diabetic wounds remains a critical therapeutic challenge. Platelet-rich plasma (PRP) gel, PRP-derived exosomes (PRP-Exos), and mesenchymal stem cell-derived exosomes (MSC-Exos) have demonstrated therapeutic potential in wound treatment. Unfortunately, their poor mechanical properties, the short half-lives of growth factors (GFs), and the burst release of GFs and exosomes have limited their clinical applications. Furthermore, proteases in diabetic wounds degrade GFs, which hampers wound repair. Silk fibroin is an enzyme-immobilization biomaterial that could protect GFs from proteases. Herein, we developed novel dual-crosslinked hydrogels based on silk protein (SP) (sericin and fibroin), including [email protected], [email protected], and [email protected], to promote diabetic wound healing synergistically. [email protected] was prepared from PRP and SP using calcium gluconate/thrombin as agonist, while [email protected] and [email protected] were derived from exosomes and SP with genipin as crosslinker. SP provided improved mechanical properties and enabled the sustained release of GFs and exosomes, thereby overcoming the limitations of PRP and exosomes in wound healing. The dual-crosslinked hydrogels displayed shear-induced thinning, self-healing, and eradication of microbial biofilms in a bone-mimicking environment. In vivo, the dual-crosslinked hydrogels contributed to faster diabetic wound healing than PRP and SP by upregulating GFs expression, down-regulating matrix metalloproteinase-9 expression, and by promoting an anti-NETotic effect, angiogenesis, and re-epithelialization. Hence, these dual-crosslinked hydrogels have the potential to be translated into a new generation of diabetic wound dressings.
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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) | 0.000 | 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".