Updates on sphingolipids: Spotlight on retinopathy
Bibliographic record
Abstract
The sphingolipids ceramide (Cer), ceramide-1-phosphate (C1P), sphingosine (Sph), and sphingosine-1-phosphate (S1P)) are key signaling molecules that regulate many patho-biological processes. During the last decade, they have gained increasing attention since they may participate in important and numerous retinal processes, such as neuronal survival and death, proliferation and migration of neuronal and vascular cells, inflammation, and neovascularization. Cer for instance has emerged as a key mediator of inflammation and death of neuronal and retinal pigment epithelium cells in experimental models of retinopathies such as glaucoma, age-related macular degeneration (AMD), and retinitis pigmentosa. S1P may have opposite biological actions, preventing photoreceptor and ganglion cell degeneration but also promoting inflammation, fibrosis, and neovascularization in AMD, glaucoma, and pro-fibrotic disorders. Alterations in Cer, S1P, and ceramide 1- phosphate may also contribute to uveitis. Furthermore, use of inhibitors that either prevent Cer increase or modulate S1P signaling, such as Myriocin, desipramine, and Fingolimod (FTY720), have been shown to preserve neuronal viability and retinal function. Collectively, the expanding role for these sphingolipids in the modulation of vital processes in retina cell types and in their dysregulation in retinal degenerations makes them attractive therapeutic targets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".