Measurements of canine aqueous humor inflammatory mediators and the effect of carprofen following anterior chamber paracentesis
Bibliographic record
Abstract
OBJECTIVES: Phase I: To evaluate levels of prostaglandin E(2) (PGE(2) ), nitrites and nitrates (NO(x) ), tumor necrosis factor-alpha (TNF-α) and expression of inducible cyclo-oxygenase (COX-2), nitric oxide synthase (NOS-2), and matrix metalloproteinases (MMP-3 and -9) in canine aqueous humor following repeated anterior chamber paracenteses (ACP). Phase II: to evaluate the effect of carprofen on PGE(2) , NO(x) , and TNF-α in canine aqueous humor following ACP. ANIMALS STUDIED: Four beagles in phase I and 8 beagles in phase II. PROCEDURES: Phase I: ACP was performed at time (T) 0, 4 and 8 h. Phase II: A randomized, placebo-controlled cross-over design with four dogs per group where carprofen was given 4.4 mg/kg/day on day (D) 1, 2 and 3. ACP was performed at T0 and T1.5 on D3. Statistical analysis was performed with repeated measures anova and post hoc Tukey-Kramer multiple-comparison procedure. In phase II, TNF-α level was analyzed with a Wilcoxon signed-rank test. RESULTS: Phase I: PGE(2) significantly increased (P < 0.0001) to plateau at T4. NO(X) was decreased at T4 (P < 0.06), but increased at T8 (P < 0.0001). COX-2 showed detectable expression only at T8. TNF-α, NOS-2, MMP-3 and -9 were undetectable at all time points. Phase II: At T1.5, PGE(2) was significantly elevated in both groups but was lower in the carprofen group (P = 0.037). NO(x) and TNF-α did not statistically increase in either group. CONCLUSIONS: Following ACP, significant increases in PGE(2) levels confirmed inflammation characterized by a rise of COX-2. The NO(x) pathway took longer to induce as compared with PGE(2) . Carprofen decreased PGE(2) levels and could help control intraocular inflammation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".