Calcitonin signalling system regulates function and arhythmogenicity of atrial cardiomyocytes
Bibliographic record
Abstract
Abstract Background Atrial fibrillation (AF) poses a significant therapeutic challenge due to myocardial remodelling, involving both structural and electrical alterations. Recent investigations have shed light on the role of atrial cardiomyocytes (CMs) in secreting Calcitonin (CT), a factor crucial for maintaining atrial tissue integrity. Dysregulated CT secretion in AF showed to contribute to fibrotic tissue production by atrial fibroblasts, exacerbating arrhythmogenesis [1]. However, the direct impact of CT on CM function and arrhythmogenicity remains unclear, driving the objectives of our study. Methods/Results Serum samples from 20 cardiac surgery patients revealed a noteworthy association between higher pre-operative CT levels and a significant ~2.8-fold reduction in the incidence of post-operative AF (poAF). Using freshly isolated atrial guinea pig CMs, confirmed (by qPCR and immunofluorescence) that CMs express CT-receptor (CTR) to enable CT actions in the cell. Functional studies found that CT administration inhibits spontaneous calcium (Ca2+)-release events induced by pacing and decreases the Ca2+ transients amplitude (at 2 Hz; IonOptix μstep system) in fura2-loaded CMs (n=22 cells) in a concentration-dependent manner. Atrial iPSC-CMs transduced with global RGECO sensor (Genetically encoded Ca2+ indicator) and treated with 15 pM CT showed a reduction in the beat rate when paced at 2Hz, and a significantly increased the time to 50% baseline. Evaluation of the expression and phosphorylation of selected Ca2+ handling proteins, which may potentially account for the observed changes in Ca2+ transients, showed no differences in total or phospho-(ser2808) ryanodine receptor, and SERCA2α in response to CT but an increase in phospho-(Ser16) Phospholamban. Conclusion Our findings highlight the effects of CT on atrial CM function. Maintaining physiological CT levels offer a promising approach for managing AF clinically, potentially through the use of already in clinical use CT-analogues.
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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".