COMPARISON OF ANTI-INFLAMMATORY ACTIVITY OF NIGELLA SATIVA AND DICLOFENAC SODIUM IN ALBINO RATS.
Bibliographic record
Abstract
BACKGROUND: Nigella sativa or "Kalonji" is a naturally occurring plant in Pakistan and other countries which possesses a wide range of medicinal properties, the anti-inflammatory property being one of these. Diclofenac sodium is a commonly used anti-inflammatory drug. The purpose of this study was to compare the anti-inflammatory effect of ethanolic extract of Nigella sativa seeds with that of diclofenac sodium in albino rats. METHODS: This laboratory randomized controlled trial (RCT) was conducted in the Physiology Department, Services Institute of Medical Sciences (SIMS), Lahore. The study was carried out on 90 male albino rats. Five percent formalin in a dose of 50 µl was injected into sub-plantar surface of right hind paw of each rat to produce inflammation. The rats were randomly divided into three groups of thirty each. Group A was given normal saline (control); group B was given Nigella sativa seed extract; and group C received diclofenac sodium, as a reference drug. Increase in paw diameter, and total and differential leukocyte counts were measured as markers of inflammation. RESULTS: Nigella sativa seeds extract caused significant (p<0.05) reduction in the paw inflammatory response in albino rats. The effect was longer in duration than the effect caused by diclofenac sodium; however, the extract was comparatively less potent than diclofenac sodium. The extract had no significant effect (p>0.05) on the total or differential leukocyte counts. CONCLUSION: Our results suggest that ethanolic extract of Nigella sativa seeds possesses potent anti-inflammatory effect, in albino rats however, this effect is comparatively less but prolonged than that produced by diclofenac sodium.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".