Heterogeneous expression of CFTR in insulin-secreting β-cells of the normal human islet
Bibliographic record
Abstract
Cystic fibrosis (CF) is due to mutations in the CF-transmembrane conductance regulator (CFTR) and CF-related diabetes (CFRD) is its most common co-morbidity, affecting ~50% of all CF patients, significantly influencing pulmonary function and longevity. Yet, the complex pathogenesis of CFRD remains unclear. Two non-mutually exclusive underlying mechanisms have been proposed in CFRD: i) damage of the endocrine cells secondary to the severe exocrine pancreatic pathology and ii) intrinsic β-cell impairment of the secretory response in combination with other factors. The later has proven difficult to determine due to low expression of CFTR in β-cells, which results in the general perception that this Cl-channel does not participate in the modulation of insulin secretion or the development of CFRD. The objective of the present work is to demonstrate CFTR expression at the molecular and functional levels in insulin-secreting β-cells in normal human islets, where it seems to play a role. Towards this end, we have used immunofluorescence confocal and immunofluorescence microscopy, immunohistochemistry, RT-qPCR, Western blotting, pharmacology, electrophysiology and insulin secretory studies in normal human, rat and mouse islets. Our results demonstrate heterogeneous CFTR expression in human, mouse and rat β-cells and provide evidence that pharmacological inhibition of CFTR influences basal and stimulated insulin secretion in normal mouse islets but not in islets lacking this channel, despite being detected by electrophysiological means in ~30% of β-cells. Therefore, our results demonstrate a potential role for CFTR in the pancreatic β-cell secretory response suggesting that intrinsic β-cell dysfunction may also participate in the pathogenesis of CFRD.
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 | 0.000 |
| 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".