Cystic Fibrosis: Modifier Genes
Bibliographic record
Abstract
Abstract Cystic fibrosis (CF) is a rare autosomal recessive disease that causes early death through respiratory failure. Although it is known that CF is caused by mutations in the cystic fibrosis transmembrane conductance regulator gene ( CFTR ) it has been found that other genetic factors make a large contribution to the severity of lung disease as well as several other CF traits. Several large‐scale genomewide association studies have been performed for CF lung disease severity, body mass index/nutritional status, CF‐related diabetes, meconium ileus and age of onset of chronic Pseudomonas aeruginosa infection. Several genes have been identified that modulate CF disease severity. In particular, apical membrane constituents have been associated with several phenotypes including lung function, meconium ileus and age of onset of P. aeruginosa infection. The results of these studies have provided insight into the pathophysiology of CF and have identified novel therapeutic targets. Key Concepts Lung disease severity in CF, along with several other CF traits, is influenced by non‐ CFTR genetic factors. Genomewide association studies and linkage analysis have identified several genes associated with CF severity. Meta‐analysis of several patient cohorts is required to identify reproducible findings. Apical membrane constituents, in particular SLC6A14 , SLC26A9 and SLC9A3 , are a class of molecules associated with several CF phenotypes. Exome‐wide sequencing can be used as an alternative approach to discover novel modifier genes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".