Evaluation of nicotinamide as an anti-inflammatory and anti-angiogenic agent in uveal melanoma cell lines
Bibliographic record
Abstract
PURPOSE:: To investigate the effect of nicotinamide on the secretion of pro-an giogenic and pro-inflammatory cytokines in uveal melanoma cell lines. METHODS:: Two human uveal melanoma cell lines (92.1 and OCM-1) were treated with nicotinamide (10 mmol/L) or control media for 48 hours in culture. The su perna tant from each culture was used in sandwich enzyme-linked immuno sorbent assay-based angiogenesis and inflammation arrays to evaluate the effects of exogenously administered nicotinamide on the secretion of a total of 20 pro-an gio genic and pro-inflammatory proteins. RESULTS:: Seven pro-angiogenic cytokines were detected under control conditions for both uveal melanoma cell lines. Treatment with nicotinamide resulted in a significant decrease in secretion of the following pro-angiogenic cytokines: angiogenin, angiopoietin-2, epidermal growth factor, and vascular epithelial growth factor-A in the 92.1 cells; basic fibroblast growth factor in the OCM-1 cells; and placenta growth factor in both cell lines. Among the pro-inflammatory proteins, monocyte chemotactic protein-1 and interleukin-8 were expressed in both untreated cell lines and both were significantly reduced when treated with nicotinamide. CONCLUSIONS:: Results from this in vitro model suggest that nicotinamide may have anti-inflammatory and anti-angiogenic properties, which may open the possibility of using it as a chemopreventive agent for uveal melanoma; however, further studies including animal models are warranted.
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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 | 0.000 | 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 | 0.000 |
| 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".