Lipidome Alterations Induced by Cystic Fibrosis, CFTR Mutation, and Lung Function
Bibliographic record
Abstract
gene. Although the classical presentation of the condition is well known, there is still a need for a better characterization of metabolic alterations related to cystic fibrosis and different genotypic mutations. We employed untargeted, comprehensive lipidomics of blood serum samples to investigate alterations in the lipid metabolism related to the pathology, mutation classes, and lung function decline. Six unique biomarker candidates were able to independently differentiate diseased individuals from healthy controls with excellent performance. Cystic fibrosis patients showed dyslipidemia for most lipid subclasses, with significantly elevated odd-chain and polyunsaturated fatty acyl lipids. Phosphatidic acids and diacylglycerols were particularly affected by different genotypic mutation classes. We selected a biomarker panel composed of four lipids, including two ceramides, one sphingomyelin, and one fatty acid, which correctly classified all validation samples from classes III and IV. A biomarker panel of five oxidized lipids was further selected to differentiate patients with reduced lung function, measured as predicted FEV1%. Our results indicate that cystic fibrosis is deeply related to lipid metabolism and provide new clues for the investigation of the disease mechanisms and therapeutic targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".