Treatment With LAU-7b Complements CFTR Modulator Therapy by Improving Lung Physiology and Normalizing Lipid Imbalance Associated With CF Lung Disease
Bibliographic record
Abstract
Cystic fibrosis (CF) is the most common autosomal recessive genetic disease in Caucasians, affecting more than 100,000 individuals worldwide. It is caused by pathogenic variants in the gene encoding CFTR, an anion channel at the plasma membrane of epithelial and other cells. Many CF pathogenic variants disrupt the biosynthesis and trafficking of CFTR or reduce its ion channel function. The most frequent mutation, loss of a phenylalanine at position 508 (F508del), leads to misfolding, retention in the endoplasmic reticulum, and premature degradation of the protein. The therapeutics available for treating CF lung disease include antibiotics, mucolytics, bronchodilators, physiotherapy, and most recently CFTR modulators. To date, no cure for this life shortening disease has been found. Treatment with the Triple combination drug therapy, TRIKAFTA®, is composed of three drugs: Elexacaftor (VX-445), Tezacaftor (VX-661) and Ivacaftor (VX-770). This therapy, benefits persons with CF, improving their weight, lung function, energy levels (as defined by reduced fatigue), and overall quality of life. We examined the effect of combining LAU-7b oral treatment and Triple therapy combination on lung function in a F508deltm1EUR mouse model that displays lung abnormalities relevant to human CF. We assessed lung function, lung histopathology, protein oxidation, lipid oxidation, and fatty acid and lipid profiles in F508deltm1EUR mice.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".