Melatonin accelerates the process of wound repair in full‐thickness incisional wounds
Bibliographic record
Abstract
The pineal gland hormone melatonin is known to have both anti-inflammatory and immunomodulatory effects. Given this, we propose that melatonin is an ideal candidate to enhance the process of wound healing. The present study assessed the effects of exogenously administered melatonin (1.2 mg/kg intra-dermal), on scar formation using a full-thickness incisional rat model of dermal wound healing. Melatonin treatment significantly improved the quality of scarring, both in terms of maturity and orientation of collagen fibres. An increase in nitric oxide synthase (NOS) activity and therefore nitric oxide production is detrimental during inflammation but is favourable during granulation tissue formation. Melatonin treatment significantly decreased inducible NOS (iNOS) activity during the acute inflammatory phase but significantly increased iNOS activity during the resolving phase. Cyclooxygenase-2, which has been shown to have anti-inflammatory effects, was elevated in the melatonin-treated rats following wounding. In addition, melatonin treatment also accelerated the angiogenic process, increasing the formation of new blood vessels and elevating the level of vascular endothelial growth factor protein expression during granulation tissue formation. Melatonin treatment increased arginase activity (which generates proline, a building block for collagen synthesis) from earlier time points. The protein profiles of hemoxygenase-1 (HO-1) and HO-2 isoforms, vital participants in the repair process, were also up-regulated upon melatonin treatment. This study has therefore demonstrated, for the first time, that melatonin can significantly improve the quality of wound healing and scar formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".