Fenretinide's preventive effect on the development of osteoprosis in Cystic Fibrosis
Bibliographic record
Abstract
Cystic Fibrosis (CF) is the most common autosomal recessive disease affecting the Caucasian population. This devastating disease is caused by any one of 1500 mutations identified in the Cystic Fibrosis Transmembrane Regulator Conductance (cftr) gene. Chronic inflammation is a hallmark of CF and it affects all systems including respiratory, gastrointestinal, reproductive and skeletal. Although the exact molecular link between the CFTR dysfunction and various phenotypes remains to be delineated, many phenotypes seem to be linked to inadequate nutritional absorption of essential fatty acids and vitamins, which leads to an imbalance between the essential fatty acids docosahexaenoic acid (DHA) and arachidonic acid (AA). The skeletal system does not only serve as mechanical support, but also functions as an active organ that regulates balance and interactions between both local and systemic hormones, cytokines and prostaglandins. Previously our laboratory has shown that fenretinide [ N-(4-hydroxyphenyl) retinamide] corrects the essential fatty acid imbalance. We hypothesized that correcting the DHA/AA ratio in the plasma of Cftr-KO mice could avoid the early-onset osteoporosis. This thesis presents our novel results describing how fenretinide prevents osteoporosis. We found that twice a week treatment with fenretinide over a period of four weeks dramatically increased trabecular bone volume and quality in Cftr-KO mice. The results of this thesis strongly suggest that fenretinide might have potential for the treatment of cystic fibrosis patients by preventing the reduction of trabecular bone mineral density.
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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) | 0.003 | 0.001 |
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".