Anti-ulcerogenic activity of Gum Arabic in gastric mucosal injury induced by ethanol in male albino rats
Bibliographic record
Abstract
The present study was performed to evaluate the anti-ulcerogenic activity of Acacia senegal (Gum Arabic) against ethanol-induced gastric mucosal injury in rats. Thirty-six adult male albino rats were divided into 4 groups: group 1 served as a control; group 2 consisted of rats that received 15% of gum in drinking water for 2 weeks; group 3 comprised ulcerated animals administered 5 mL of ethanol/kg body weight by gavage; and group 4 consisted of rats received 15% of gum in drinking water for 2 weeks before ethanol administration. Superoxide dismutase (SOD) glutathione peroxidase (GPx), malondialdehyde (MDA), prostaglandin E2 (PGE2), tumor necrosis factor alpha (TNF-α), interleukin (IL)-B1), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total protein, and albumin were assayed in addition to histological study. The results revealed that ethanol decreased SOD, GPx, and PGE2 in tissue and serum total protein and albumin, while increased MDA in tissue, serum TNF-α, IL-B1, PGE2, ALT, AST, and ALP. Histological findings showed less edema and leucocytes infiltration compared with ulcer group. Furthermore, gum administration elevated PGE2, SOD, and GPx and significantly reduced MDA, TNF-α, and IL-B2. In conclusion, Gum Arabic can enhance gastric protection and sustain the integrity of the gastric mucosa. Novelty The selected dose of Gum Arabic has the ability to decrease the pro-inflammatory cytokines in plasma and gastric tissue, thus enhancing gastric protection and maintaining the integrity of the gastric mucosa. Gum Arabic can compensate for the loss of antioxidants.
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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 | 0.000 |
| 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".