Iron Overload Decreases Ca <sub>V</sub> 1.3-Dependent L-Type Ca <sup>2+</sup> Currents Leading to Bradycardia, Altered Electrical Conduction, and Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Chronic iron overload (CIO) is associated with blood disorders such as thalassemias and hemochromatosis. A major prognostic indicator of survival in patients with CIO is iron-mediated cardiomyopathy characterized by contractile dysfunction and electrical disturbances, including slow heart rate (bradycardia) and heart block. METHODS AND RESULTS: We used a mouse model of CIO to investigate the effects of iron on sinoatrial node (SAN) function. As in humans, CIO reduced heart rate (≈20%) in conscious mice as well as in anesthetized mice with autonomic nervous system blockade and in isolated Langendorff-perfused mouse hearts, suggesting that bradycardia originates from altered intrinsic SAN pacemaker function. Indeed, spontaneous action potential frequencies in SAN myocytes with CIO were reduced in association with decreased L-type Ca(2+) current (I(Ca,L)) densities and positive (rightward) voltage shifts in I(Ca,L) activation. Pacemaker current (I(f)) was not affected by CIO. Because I(Ca,L) in SAN myocytes (as well as in atrial and conducting system myocytes) activates at relatively negative potentials due to the presence of Ca(V)1.3 channels (in addition to Ca(V)1.2 channels), our data suggest that elevated iron preferentially suppresses Ca(V)1.3 channel function. Consistent with this suggestion, CIO reduced Ca(V)1.3 mRNA levels by ≈40% in atrial tissue (containing SAN) and did not lower heart rate in Ca(V)1.3 knockout mice. CIO also induced PR-interval prolongation, heart block, and atrial fibrillation, conditions also seen in Ca(V)1.3 knockout mice. CONCLUSIONS: Our results demonstrate that CIO selectively reduces Ca(V)1.3-mediated I(Ca,L), leading to bradycardia, slowing of electrical conduction, and atrial fibrillation as seen in patients with iron overload.
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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".