Gastroprotective effects of montelukast and <i>Nigella sativa</i> oil against corticosteroid-induced gastric damage: they are much more than antiasthmatic drugs
Bibliographic record
Abstract
Corticosteroids are used to treat a variety of diseases like bronchial asthma. However, long-term corticosteroids have a gastric ulcerogenic potential. Montelukast (MTK) and Nigella sativa oil (NSO) are used in treatment of bronchial asthma. Previous studies showed that MTK and NSO had gastroprotective effects in other models of gastric ulcer. The present study assesses synergistic gastroprotective effects of both drugs in dexamethasone (DXM)-induced gastric damage. Fifty male rats were divided into 5 groups: normal control (I), DXM group (II), MTK + DXM group (III), NSO + DXM group (IV), MTK + NSO + DXM group (V). After 7 days, stomachs were removed for biochemical analysis and histological examinations. Significant increases in malondialdehyde (MDA) level, superoxide dismutase (SOD) activity, myeloperoxidase (MPO) activity, and proliferating cell nuclear antigen (PCNA) positive cells, with significant decreases in mucus secretion were detected in DXM-treated group compared with group I. Meanwhile, significant decreases of MDA level, MPO activity, and PCNA positive cells and significant increases in mucus secretion were detected in treated groups compared with group II. SOD activity significantly decreased in group V compared with group II. We could conclude that administration of either MTK or NSO or both with DXM counteracts DXM-induced gastric lesions.
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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".