Gum Arabic and Eugenol in a Silica-Induced Inflammatory Angiogenesis Model in Wistar Rat
Bibliographic record
Abstract
This work aims to evaluate the effects of gum arabic and eugenol on inflammatory angiogenesis in a subcutaneous silica inoculum model. Ninety-six Wistar rats were distributed over 16 groups. The substances administered by gavage included distilled water, gum arabic (500 mg/100g weight), and eugenol (10mg/100g weight). Control groups received 0.2 mL of sterile distilled water subcutaneously. The experimental groups were subjected to an inoculum of 0.2 mL of silica solution (50 mg/mL) subcutaneously. For seven or 14 days, the groups received distilled water or gum arabic or eugenol or gum arabic plus eugenol. There was no difference between the histological analysis of slides stained with hematoxylin-eosin. There was a reduction in vascular density in animals that received only gum arabic for 7 (0.09 µm² ± 0.02) or 14 (0.08 µm² ± 0.03) days and only eugenol for seven days (0.08 µm² ± 0.03), but this was not statistically significant. The microvascular density significantly increased in the group treated with eugenol for 14 days (0.14 µm² ± 0.02). Eugenol reduced inflammatory angiogenesis when administered for seven days and stimulated it when administered for 14 days. Gum arabic had a potential inhibitory effect on this model. The study of inflammatory angiogenesis, induced by a silica inoculum in the subcutaneous tissue of rats, is a new and reproducible model for evaluating angiogenesis and inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".