Cystic Fibrosis Fatty Acid Imbalance Is Linked to Ceramide Deficiency and Corrected by Fenretinide
Bibliographic record
Abstract
Patients with cystic fibrosis (CF) and Cftr-knockout mice (CF mice) display an imbalance in fatty acids, with high arachidonic acid (AA) and low docosahexaenoic acid (DHA) concentrations. Our recent studies demonstrated defects in another class of lipids, ceramides, in patients with CF and in CF mice. This study investigates the relationship between ceramide, AA, DHA, and the correction of lipid imbalances in CF mice after treatment with fenretinide. Concentrations of AA, DHA, and ceramide were assessed in plasma from 58 adult patients with CF and 72 healthy control subjects. After 28 days of treatment with fenretinide, the same analysis was performed in wild-type and CF mice from plasma and organs (lung, ileum, pancreas, and liver). Low ceramide levels were associated with high AA and low DHA concentrations in patients with CF. No correlation was observed in healthy control subjects. Greater deficiencies were seen in patients with CF who were diagnosed before the age of 18, specifically with statistically significant higher levels of AA. Treatment with fenretinide (N-(4-hydroxyphenyl)retinamide; 4-HPR) normalized high levels of AA and low levels of ceramide, and increased the levels of DHA in CF mice. As in patients with CF, low ceramide levels correlated with higher AA and lower DHA levels in plasma of CF mice. Lipid abnormalities correlated with ceramide deficiencies in patients with CF and CF mice. We found that fenretinide treatment normalizes the fatty acid imbalance in CF mice with reducing AA to WT levels and increasing DHA. We propose that fenretinide treatment might improve this pathological phenotype in patients with CF.
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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) | 0.001 | 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".