Insights in wound healing properties of water-soluble composition of dihydroquercetin and L-lysine
Bibliographic record
Abstract
This study focuses on implementing a composition of the natural flavonoid dihydroquercetin (DHQ) with L-lysine in the treatment of thermal burns. The wound-healing activity of DHQ is well-known. The addition of amino acid to the composition increases the water solubility of the flavonoids, providing an opportunity to develop a spray dosage form. The research involved 60 male Wistar rats divided into five treatment groups. Sea buck oil served as a positive control. On day 14, the composition treatment group showed significant progress in wound healing, being 9.6 ± 2.0% ahead of the other groups in absolute terms. On day 35, treatment with the composition resulted in a significant decrease in relative wound area to 1.9 ± 0.9%, while in the negative and positive control groups, it was 10.7 ± 7.8% and 8.4 ± 4.9%, respectively. At the same time, the epidermal and dermal layers were found to be clearly distinguished in the composition treatment according to histological analysis. Numerous collagen fibres were clearly visible, and the active process of keloid scar formation was observed. An additive effect of the combined use of DHQ and L-lysine was observed (F = 0.21, p = 0.649). A natural next step is to develop the dosage form for the DHQ-L-lysine composition.
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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".