Design and applications of self-assembled polypeptide matrices in wound healing
Bibliographic record
Abstract
With an estimated prevalence of over two cases per 1,000 patients, chronic wounds represent a massive burden on healthcare systems around the globe. Such wounds often lead to major complications, including amputations, that greatly affect the living conditions of patients. Typical therapeutic approaches include skin grafts and topical application of therapeutic molecules such as growth factors. Current limitations of grafts include the availability of healthy tissues and risks of rejection, while the efficiency of therapeutic molecules is limited by their short half-life in the wound environment. Interestingly, porous matrices such as hydrogels have emerged as promising materials by acting simultaneously as a scaffold for skin cell proliferation and as a delivery system for therapeutic molecules, protecting them from degradation and/or elimination. Self-assembling polypeptides have revealed interesting properties for the fabrication of such materials, notably their ability to mimic the extracellular matrix of the skin, tunable mechanical properties and ease of conjugation to bioactive sequences. In this context, the present review aims at highlighting the diversity of self-assembled protein and peptide-based matrices, natural and synthetic, that have been evaluated as wound healing scaffolds. After briefly describing the most common bioactive protein sequences used within these matrices, examples of nature-inspired and synthetic self-assembled proteinaceous matrices studied for wound healing will be presented. Finally, strategies for modulating the mechanical properties of the hydrogels are discussed. Despite the number of studies published on the subject, the expanding number of self-assembling protein sequences and the constantly improving strategies for modulating the mechanical properties of resulting matrices should further drive the development of improved protein-based hydrogels for wound healing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".