The effect of blue light exposure in an ocular melanoma animal model
Bibliographic record
Abstract
BACKGROUND: Uveal melanoma (UM) cell lines, when exposed to blue light in vitro, show a significant increase in proliferation. In order to determine if similar effects could be seen in vivo, we investigated the effect of blue light exposure in a xenograft animal model of UM. METHODS: Twenty New Zealand albino rabbits were injected with 1.0 x 10(6) human UM cells (92.1) in the suprachoroidal space of the right eye. Animals were equally divided into two groups; the experimental group was exposed to blue light, while the control group was protected from blue light exposure. The eyes were enucleated after sacrifice and the proliferation rates of the re-cultured tumor cells were assessed using a Sulforhodamine-B assay. Cells were re-cultured for 1 passage only in order to maintain any in vivo cellular changes. Furthermore, Proliferating Cell Nuclear Antigen (PCNA) protein expression was used to ascertain differences in cellular proliferation between both groups in formalin-fixed, paraffin-embedded eyes (FFPE). RESULTS: Blue light exposure led to a statistically significant increase in proliferation for cell lines derived from intraocular tumors (p < 0.01). PCNA expression was significantly higher in the FFPE blue light treated group when compared to controls (p = 0.0096). CONCLUSION: There is an increasing amount of data suggesting that blue light exposure may influence the progression of UM. Our results support this notion and warrant further studies to evaluate the ability of blue light filtering lenses to slow disease progression in UM patients.
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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) | 0.001 | 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".