Sphingolipid distribution changes with age in the human lens
Bibliographic record
Abstract
The formation of an internal barrier to the diffusion of small molecules in the lens during middle age is hypothesized to be a key event in the development of age-related nuclear (ARN) cataract. Changes in membrane lipids with age may be responsible. In this study, we investigated the effect of age on the distribution of sphingomyelins, the most abundant lens phospholipids. Human lens sections were initially analyzed by MALDI mass spectrometry imaging. A distinct annular distribution of the dihydrosphingomyelin, DHSM (d18:0/16:0), in the barrier region was observed in 64- and 70-year-old lenses but not in a 23-year-old lens. An increase in the dihydroceramide, DHCer (d18:0/16:0), in the lens nucleus was also observed in the older lenses. These findings were supported by ESI mass spectrometry analysis of lipid extracts from lenses dissected into outer, barrier, and nuclear regions. A subsequent analysis of 18 lenses ages 20-72 years revealed that sphingomyelin levels increased with age in the barrier region until reaching a plateau at approximately 40 years of age. Such changes in lipid composition will have a significant impact on the physical properties of the fiber cell membranes and may be associated with the formation of a barrier.
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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 | 0.001 | 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".