Animal Models of LED-Induced Phototoxicity. Short- and Long-Term In Vivo and Ex Vivo Retinal Alterations
Bibliographic record
Abstract
Phototoxicity animal models have been largely studied due to their degenerative communalities with human pathologies, e.g., age-related macular degeneration (AMD). Studies have documented not only the effects of white light exposure, but also other wavelengths using LEDs, such as blue or green light. Recently, a blue LED-induced phototoxicity (LIP) model has been developed that causes focal damage in the outer layers of the superior-temporal region of the retina in rodents. In vivo studies described a progressive reduction in retinal thickness that affected the most extensively the photoreceptor layer. Functionally, a transient reduction in a- and b-wave amplitude of the ERG response was observed. Ex vivo studies showed a progressive reduction of cones and an involvement of retinal pigment epithelium cells in the area of the lesion and, in parallel, an activation of microglial cells that perfectly circumscribe the damage in the outer retinal layer. The use of neuroprotective strategies such as intravitreal administration of trophic factors, e.g., basic fibroblast growth factor (bFGF), brain-derived neurotrophic factor (BDNF), ciliary neurotrophic factor (CNTF) or pigment epithelium-derived factor (PEDF) and topical administration of the selective alpha-2 agonist (Brimonidine) have demonstrated to increase the survival of the cone population after LIP.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".