Neuropeptide Substance P Released from a Nonswellable Laponite-Based Hydrogel Enhances Wound Healing in a Tissue-Engineered Skin In Vitro
Bibliographic record
Abstract
Chronic wounds associated with diabetes remain a worldwide clinical challenge. Substance P (SP), which is an 11-amino acid neuropeptide secreted in the skin mainly by sensory neurons, has been reported to promote diabetic wound healing. However, like many bioactive peptides, the low stability of SP in the protease-rich wound environment limits its therapeutic topical application. To provide protection to SP and enable its sustained release in the wound, we have prepared an injectable Laponite nanodiscs-based hydrogel loaded with SP and evaluated its wound healing ability in a human tissue-engineered skin model. This hydrogel is nonswellable, self-standing, biodegradable and biocompatible, and the simple fabrication process with mild conditions have enabled the encapsulation of controlled concentrations of SP. Rheological experiments further showed the self-healing and shear-thinning behavior of this system. Histological analysis showed that the application of hydrogel did not alter the aspect or the differentiation of nearby epidermis, confirming its biocompatibility. Remarkably, one-time application experiments demonstrated that the hydrogel HSP2 containing SP at 10 –5 M induced 98% wound closure within 16 days, whereas the control sample did not achieve full reepithelialization. This result indicated that SP was successfully released from the hydrogel and was available for keratinocytes to stimulate the reepithelialization process. Thus, the obtained data suggested that our SP-loaded hydrogel promoted wound healing, making it a potential formulation to be used as chronic wound dressing.
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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".