Down‐regulation of dysferlin and myoferlin in human airway epithelium: differential effects on cell morphology and adhesion
Bibliographic record
Abstract
Dysferlin and myoferlin belong to Ferlin protein family known to mediate endomembrane fusion events. Mutations of dysferlin lead to muscular dystrophy. Patients with dysferlinopathy show cardiopulmonary complications, suggesting that dysferlin may have functional roles in the pulmonary system. The high homology of myoferlin to dysferlin proposes that myoferlin may also play a role in lung pathology. Objectives To examine dysferlin and myoferlin expression in human lungs and investigate the effects of silencing these genes in human airway epithelial cells (HAEC). Methods Dysferlin and myoferlin expression was evaluated by immunohistochemistry in human airway sections and by Western blot analysis in HAEC. Localization of dysfelrin and myoferlin in HAEC was studied using immunofluorescent staining. Effects of dysferlin and myoferlin knockdown on the morphology and adhesion of HAEC were also assessed. Results Both dysferlin and myoferlin were present in the epithelium of human airway tissues and cultured HAEC. Dysferlin and myoferlin expression localized at the membrane and cytoplasm of HAEC. Knockdown of myoferlin, but not dysferlin, resulted in cell morphological changes, deficient cell adhesion and decreased the expression of zonular occludens 1 in HAEC. Conclusion Presence of dysferlin and myoferlin suggests they play potential roles in the adhesion of human airway epithelial cells.
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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 | 0.000 |
| 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".