The effects of fenretinide, a vitamin a derivative, on phenotypes of cystic fibrosis
Bibliographic record
Abstract
Cystic fibrosis (CF) is an autosomal recessive disease caused by mutations in the CF Transmembrane Conductance Regulator (CFTR). Individuals are usually diagnosed in infancy and are burdened with extensive medical treatments throughout their lives. The treatments currently available attempt to preserve lung function by addressing bacterial infections and physiological defects of the CF lung. There are many CF phenotypes not addressed by current therapies such as high levels of inflammation, high oxidative/nitrosative stress, abnormal levels of polyunsaturated fatty acids (PUFA) and defects in ceramide. Based on our preliminary studies, we hypothesized that fenretinide could address these gaps in CF treatment. Our previous studies demonstrated the protective effects of fenretinide, a derivative of vitamin A, in CF mice. The drug treatment in CF mice improved clearance of lung infections and normalized the levels of ceramide, which were abnormally low in CF. In this thesis, we show that the abnormal levels of PUFAs, arachidonic acid (AA) and docosahexaenoic acid (DHA), are associated with the defects in ceramide in CF patients. Fenretinide treatment in CF mice improved the PUFA imbalance systemically and in CF-related organs, lung, ileum, pancreas and liver. The drug also reduced the expression of inflammatory genes, IL-1β and S100A8, in the lung of CF mice. Fenretinide had prolonged normalizing effects on PUFA and ceramide levels in mice with one dose improving the lipids up to 90 hours after treatment. It also reduced the levels of malondialdehyde, a marker of lipid peroxidation, and nitrotyrosine, a marker of nitrosative stress. High levels of lipid peroxidation and nitrosative stress were correlated with greater defects in lipids. Lipids from leukocytes isolated from CF patients responded to fenretinide treatment in similar ways with a reduction in AA, and increased DHA and ceramide. Lipid peroxidation also decreased in CF cells. We also tested different formulations of the drug in monkeys and demonstrated a drug effect in yet another animal species. CF patients periodically experience worsening of their pulmonary symptoms and these events are called pulmonary exacerbations (PEx). These events impact the progression of lung disease however they are nearly impossible to prevent. We followed a cohort of CF patients during stable disease to evaluate markers associated with the risk of a future PEx. We determined that at stable disease, a worse clinical picture and a lower score on the patient's quality of life assessment were related to a higher risk of a PEx. After treatment for PEx, patients with high levels of inflammation had rapidly re-exacerbated. We discovered that the aggressive treatments used during PEx decreased inflammatory markers but also improved the defects in PUFA by decreasing AA and increasing DHA. Additionally, treatments for PEx reduced the levels of malondialdehyde and nitrotyrosine, however they are not feasible for chronic use unlike fenretinide. Fenretinide has been used in long-term cancer prevention trials. The reported side-effects were minimal and reversible with short drug-free intervals. The results in this thesis show the impact of fenretinide on improving lipid defects, reducing inflammation and normalizing high levels of lipid peroxidation and nitrosative stress. These phenotypes are not currently addressed in routine CF therapy thus fenretinide is a good candidate for future clinical trials.
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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 | 0.000 | 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 | 0.001 |
| 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".