The effect of blue light exposure and use of intraocular lenses on human uveal melanoma cell lines
Bibliographic record
Abstract
Little is known about the effect of blue light on inducing melanocytic malignant transformation. We chose to investigate the effect of blue light (475 nm wavelength) on the proliferation rates of uveal melanoma cells. In addition, we tested two different intraocular lenses to determine the possible effects of ultraviolet absorbing and blue light filtering intraocular lenses on the changes in proliferation. Four human uveal melanoma cell lines (92.1, MKT-BR, OCM-1, SP6.5) were exposed to blue light with and without the presence of ultraviolet absorbing and blue light filtering intraocular lenses. Cells covered by aluminum foil were used as a control. The proliferation rate of the cells compared with the control was then assessed using the Sulforhodamine-B proliferation assay. Cells exposed to blue light showed a statistically significant (P<0.05) increase in proliferation. Those exposed to blue light through a standard ultraviolet absorbing intraocular lens showed a smaller increase in proliferation, whereas those exposed with a blue light filtering intraocular lens showed no increase in proliferation than the control in all four cell lines. The exposure of cells to blue light led to an increase in proliferation in all cell lines compared with the control. The use of blue light filtering intraocular lenses abolished these increases in proliferation in the four cell lines. These results indicate that blue light filtering intraocular lenses may have a protective effect on the proliferation rates of uveal melanoma cells exposed to blue light.
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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) | 0.003 | 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".