Synergistic interaction between docosahexaenoic acid and diclofenac on inflammation, nociception, and gastric security models in rats
Bibliographic record
Abstract
Abstract Preclinical Research & Development The addition of polyunsaturated fatty acids to nonsteroidal anti‐inflammatory drugs can increase their antinociceptive activity and produce a gastroprotective effect. The aim of the present study was to examine the effects of the interaction between docosahexaenoic acid (DHA) and diclofenac on inflammation (fixed ratios 1:1, 1:3, and 3:1), nociception (fixed ratio 1:3), and gastric injury in rats. DHA, diclofenac, or combinations of DHA and diclofenac produced anti‐inflammatory and antinociceptive effects in rat. The administration of diclofenac produced significant gastric damage, but this effect was not observed with either DHA or the DHA–diclofenac combinations. Effective dose (ED30) values were estimated for each individual drug and analyzed isobolographically. The anti‐inflammatory experimental ED30 values were 6.97 mg/kg (1:1 fixed ratio), 1.1 mg/kg (1:3 fixed ratio), and 11.34 mg/kg (3:1 fixed ratio). These values were significantly lower (p < .05) than the theoretical ED30 values: 67.94 mg/kg (1:1), 35.37 mg/kg (1:3), and 100.51 mg/kg (3:1). The antinociceptive experimental value was 1.25 mg/kg (1:3 fixed ratio). This value was lower (p < .05) than the theoretical ED30, which was predicted to be 15.92 mg/kg. These data indicate that the DHA–diclofenac combinations interact at the systemic level, produce minor gastric damage, and potentially have therapeutic advantages for the clinical treatment of inflammatory pain.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".