The use of a cyclooxygenase-2 inhibitor (Nepafenac) in an ocular and metastatic animal model of uveal melanoma
Bibliographic record
Abstract
The expression of cyclooxygenase-2 (COX-2) has been reported as an indicator of poor prognosis in a wide variety of human tumors, including colon, breast and uveal melanoma (UM). COX-2 inhibitors have shown promise in controlling the malignancy of several types of tumors. Previous studies have demonstrated the efficacy of a COX-2 inhibitor on the proliferation rates of human UM cells. The goal of this experiment was to investigate the efficiency of Nepafenac, a topically administered COX-2 inhibitor, in a rabbit model of UM. The animals were divided into two groups of 14 animals for the duration of the 12-week experiment. One animal per group was killed each week to evaluate disease progression and for histopathological studies. The experimental group received drops containing 0.3% Nepafenac solution. Intraocular tumor growth was evaluated weekly by fundoscopic examination and each animal was weighed prior to examination. Blood samples were taken weekly from all rabbits to detect circulating malignant cells (CMCs) throughout the experiment. After the second week of inoculation, the experimental group weighed significantly more than the control group. The control group developed more intraocular tumors and presented with metastases and higher detectable levels of CMCs before the treated group. These results indicate that the topical administration of a COX-2 inhibitor delayed the progression of this malignancy in our animal model. A clinical trail using an anti-COX-2 inhibitor for patients with UM should be considered.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".