Effect of Propolis and Liquid Smoke Nanogel on TGF-β and Macrophage Activity in Rattus Norvegicus with Traumatic Ulcer
Bibliographic record
Abstract
Traumatic ulcers are a disorder of the oral mucosa, the incidence of which reaches 83.6% in both men and women.Chronic traumatic ulcers have malignant potential if not properly managed.Propolis is a natural ingredient derived from the resin collected by honeybees, which is widely used in alternative medicine because of its content.Liquid smoke is a compound resulting from the condensation of a hot reactor containing various chemical compounds that act as antioxidants, antiseptics, antibacterials, and as preservatives.This research aims to evaluate whether a nanogel combining propolis extract and liquid smoke can more effectively enhance the healing of chronic traumatic ulcers compared to propolis alone.This study involved 36 male rats aged 2-3 months, divided into three groups: a control group, a group given only propolis, and a group given a combination of propolis and liquid smoke, with treatments administered twice daily for 7 days.The expression of TGF- cells and macrophages in the healing process of traumatic oral ulcers in rats showed significant differences between the administration of nanogels with a combination of propolis extract and liquid smoke and the administration of propolis extract nanogels.The average number of TGF- cells and macrophages in the experimental group treated with the combined nanogel of propolis extract and liquid smoke increased significantly on the third day (P < 0.05), more rapidly than in the other groups.Propolis extract and liquid smoke combination nanogel accelerate the healing process of chronic traumatic ulcers to prevent malignancy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".