Sjögren-Larsson syndrome: A biochemical rationale for using aldehyde-reactive therapeutic agents
Bibliographic record
Abstract
that result in deficient fatty aldehyde dehydrogenase (FALDH) activity and impaired fatty aldehyde and fatty alcohol oxidation. The pathogenesis of SLS is thought to involve accumulation of long-chain fatty aldehydes and alcohols and/or metabolically-related ether glycerolipids. Fatty aldehydes are particularly toxic molecules that can covalently react with proteins and certain amino-containing lipids such as phosphatidylethanolamine (PE), generating an unusual aldehyde adduct, N-alkyl-PE (NAPE). Using Faldh-deficient Chinese hamster ovary cells (FAA-K1A) as a cellular model for SLS, we investigated the ability of an aldehyde trapping agent, ADX-102 [2-(3-amino-6-chloro-quinolin-2-yl)-propan-2-ol], to mitigate the harmful effects of fatty aldehydes. FAA-K1A cells were protected from octadecanal (C18:0-al) induced cytotoxicity and apoptosis by ADX-102. Metabolism of C18:0-al to fatty alcohol (octadecanol) was also inhibited by ADX-102. FAA-K1A cells accumulated 5-fold more NAPE with C16- and C18-linked N-alkyl chains compared to wild-type cells, but NAPE levels decreased to normal after growth for 4 days with 50 μM ADX-102. Our results suggest that small aldehyde-reactive molecules, such as ADX-102, should be explored as novel therapeutic agents for SLS by preventing aldehyde adduct formation with critical cellular targets and inhibiting fatty aldehyde metabolism to fatty alcohol.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".