Cyclooxygenase-2 inhibitor prevents radiation-enhanced infiltration of F98 glioma cells in brain of Fischer rat
Bibliographic record
Abstract
PURPOSE: Radiation induces a neuro-inflammation that is characterized by the expression of genes known to increase the invasion of cancer cells. In Fischer rats, brain irradiation increases the infiltration of cancer cells and reduced the median survival of the animals. In this study, we have determined whether these adverse effects of radiation can be prevented with the cyclooxygenase-2 (COX-2) inhibitor meloxicam. MATERIALS AND METHODS: Brain of Fischer rats treated or not with meloxicam were irradiated (15 Gy) and then implanted with the F98 glioma cells. The median survival of the animals, the infiltration of F98 cells, and the expression of inflammatory cytokines and pro-migration molecules were measured. RESULTS: Meloxicam reduced by 75% the production of prostaglandin E2 (bioproduct of COX-2) in irradiated brains validating its anti-inflammatory effect. Median survival was increased to control levels by the treatment of meloxicam following brain irradiation. This protective effect was associated with a reduction of the infiltration of F98 cells in the brain, a complete inhibition of radiation-enhancement of matrix metalloproteinase-2, and a significant reduction of tumor necrosis factor α (TNF-α) and tumor growth factor β1 (TGF-β1) expression. Using invasion chambers, interleukin-1β (IL-1β) stimulated by 5-fold the invasiveness of F98 cells, but this stimulation was completely inhibited by meloxicam. This suggests that a cooperation between IL-1β and COX-2 are involved in radiation-enhancement of F98 cell invasion. CONCLUSIONS: Our results indicate the importance of reducing the inflammatory response of normal brain tissue following irradiation in an effort to extend median survival in F98 tumor-bearing rats.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".