UTP increases wound healing in the self assembled skin substitute (SASS)
Bibliographic record
Abstract
The therapeutic potential of purinergic signaling has been explored for a wide variety of diseases, including those related to the skin. In this study, we used the self-assembled skin substitutes (SASS), a highly functional reconstructed human skin model, which shares many properties with normal human skin, to study the impact of purinergic receptors agonists, such as ATP, UTP and a P2Y receptor antagonist, Reactive Blue 2 during wound healing. After treating the wounded skins, we evaluated the wound area, reepithelialization, length of migrating tongues toward the wound, quality of the skins through the cytokeratin 10 and laminin-5 expression, epidermal and dermal cell proliferation. In addition, the expression of the main ectoenzymes capable of hydrolyzing nucleotides were investigated through the wounded SASS regions: unwounded region, wound margin, intermediate region and migrating epidermal tongue. After 3 days, under the UTP treatment, the wounded SASS showed an increase in the reepithelialization and in the proliferation of keratinocytes and fibroblasts, without altering the quality of the skin. We also identified the presence of the ectoenzymes NTPDase1 and NPP1 in the reconstructed human skin model, suggesting their involvement in wound healing. Considering the need for new therapies capable of promoting healing in complex wounds, although these results are still preliminary, they suggest the involvement of extracellular nucleotides in human skin healing and the importance to understand their role in this mechanism. New experiments it will be necessary to determine the mechanisms by which the purinergic signaling is involved in the skin wound healing.
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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".