Role of cyclooxygenase (COX)‐1 and COX‐2 in a rabbit model of lipopolysaccharide (LPS)‐induced ocular inflammation
Bibliographic record
Abstract
Objective To evaluate relative contribution of COX‐1 and COX‐2 to the LPS‐induced ocular inflammation in rabbits. Methods At hour 0, 27 rabbits received either artificial tears (Refresh Tears ® ; Allergan, Inc.; Irvine, CA) (3 drops 20 min apart), COX‐1 inhibitors trans ‐resveratrol 0.1% (TR) (3 drops 20 min apart) or FR122047 0.1% (FR) (1 drop), COX‐2 inhibitor nimesulide 0.1% (1 drop), or COX‐1 and COX‐2 inhibitor ketorolac 0.45% (Acuvail ® ; Allergan, Inc.; Irvine, CA) (3 drops 20 min apart). At hour 1, rabbits received intravenous injections of LPS and fluorescein isothiocyanate (FITC)‐dextran. At hour 2, aqueous samples were collected for analysis. Six additional rabbits received only FITC‐dextran 2 hours before sample collection. Results LPS increased aqueous prostaglandin E 2 (PGE 2 ) and FITC‐dextran levels by 4.2‐ and 137.8‐fold, respectively. Ketorolac 0.45% and COX‐1 inhibitors (TR and FR) significantly reduced PGE 2 elevation ( P < .01) while COX‐2 inhibitor (nimesulide) was ineffective. Of all inhibitors, only ketorolac 0.45% significantly inhibited FITC‐dextran elevation ( P < .01). Inefficacy of TR and FR in reducing FITC‐dextran leakage may be due to their poor penetration into iris–ciliary body. Conclusion COX‐1 was the primary isoenzyme responsible for early inflammatory response to LPS. Ketorolac 0.45% suppressed ocular inflammation by inhibiting PGE 2 elevation and FITC‐dextran leakage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".