Determination of expression of cyclooxygenase-1 and -2 isozymes in canine tissues and their differential sensitivity to nonsteroidal anti-inflammatory drugs
Bibliographic record
Abstract
OBJECTIVE: To evaluate cyclooxygenase isozyme distribution in tissues from dogs and determine the differential sensitivity of canine cyclooxygenase (COX)-1 and -2 isozymes to nonsteroidal anti-inflammatory drugs (NSAIDs). SAMPLE POPULATION: Canine tissue samples (stomach, duodenum, ileum, jejunum, colon, spleen, cerebral cortex, lung, ovary, kidney, and liver) were obtained from 2 dogs for northern and western blot analyses, and blood for whole blood COX assays was obtained from 15 dogs. PROCEDURE: 11 NSAIDs were evaluated to determine their COX-2 selectivity in whole blood assays. The concentrations of the drug needed to inhibit 50% of enzyme activity (IC50) were then calculated for comparison. Expression and tissue distribution of COX isozymes were determined by northern and western blot analysis. RESULTS: Aspirin, diclofenac, indomethacin, ketoprofen, meclofenamic acid, and piroxicam had little selectivity toward COX isozymes, whereas NS398, carprofen, tolfenamic acid, nimesulide, and etodolac had more than 5 times greater preference for inhibiting COX-2 than COX-1. All canine tissues examined, including those from the gastrointestinal tract, coexpressed COX-1 and -2 mRNA, although protein expression was observed only for COX-1. CONCLUSIONS AND CLINICAL RELEVANCE: Canine COX-2 was selectively inhibited by etodolac, nimesulide, and NS398; tolfenamic acid and carprofen also appeared to be preferential COX-2 inhibitors in dogs. The roles of COX-1 as a constitutive housekeeping enzyme and COX-2 as a proinflammatory inducible enzyme (as determined in humans) appear to apply to dogs; therefore, COX-2-selective inhibitors should prove useful in reducing the adverse effects associated with nonselective NSAIDs.
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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 | 0.001 | 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".