Indole-3-carbinol loaded-nanocapsules modulated inflammatory and oxidative damages and increase skin wound healing in rats
Bibliographic record
Abstract
This study evaluated the effects of topically applied hydrogels (HG) containing nanoencapsulated indol-3-carbinol (I3C) and its free form in a rat model of skin wounds. Formulations were topically applied twice a day for five days to the wounds. On days 1, 3, and 6, the wound area was measured to verify the % of regression. On the sixth day, the animals were euthanized for the analysis of the inflammatory and oxidative profile in wounds. The nanocapsules (NC) exhibited physicochemical characteristics compatible with this kind of suspension. After five hours of exposure to ultraviolet C, more than 78% of I3C content in the suspensions was still observed. The NC-I3C did not modify the physicochemical characteristics of HG when compared to the HG base. In the in vivo study, an increase in the size of the wound was observed on the 3rd experimental day, which was lower in the treated groups (mainly in HG-NC-I3C) compared to the control. On the 6th day, HG-I3C, HG-NC-B, and HG-NC-I3C showed lower regression of the wound compared to the control. Additionally, HG-NC-I3C exhibited an anti-inflammatory effect (as observed by decreased levels of interleukin-1B and myeloperoxidase), reduced oxidative damage (by decreased reactive species, lipid peroxidation, and protein carbonylation levels), and increased antioxidant defense (by improved catalase activity and vitamin C levels) compared to the control. The current study showed more satisfactory results in the HG-NC-I3C group than in the free form of I3C in decreasing acute inflammation and oxidative damage in wounds.
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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) | 0.000 | 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 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".