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-β 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 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".