The cardiac TRPA1 channel drives calcium-mediated mechano-arrhythmogenesis
Bibliographic record
Abstract
SUMMARY PARAGRAPH Maintenance of cardiac function involves a regulatory loop in which electrical excitation causes the heart to contract through excitation-contraction coupling (ECC), 1 and the mechanical state of the heart directly affects its electrical activity through mechano-electric coupling (MEC). 2 However, in pathological states such as acute ischaemia that alter early or late electro-mechanical coordination ( i.e. , disturbances in ECC or repolarisation-relaxation coupling, RRC), MEC may contribute to the initiation and / or sustenance of arrhythmias (mechano-arrhythmogenesis). 3 The molecular identity of specific factor(s) underlying mechano-arrhythmogenesis in acute ischaemia, however, remain undefined. 4 By rapid stretch of rabbit single left ventricular cardiomyocytes, we show that upon ATP-sensitive potassium channel-induced alterations of RRC, overall vulnerability to mechano-arrhythmogenesis is increased, with mechano-sensitive 5–11 transient receptor potential kinase ankyrin 1 (TRPA1) channels 12 acting as the molecular driver through a Ca 2+ -mediated mechanism. Specifically, TRPA1 activation drives stretch-induced excitation and creates a substrate for self-sustained arrhythmias, which are maintained by increased cytosolic free Ca 2+ concentration ([Ca 2+ ] i ) and spontaneous [Ca 2+ ] i oscillations. This TRPA1-dependent mechano-arrhythmogenesis involves microtubules, and can be prevented by block of TRPA1 or buffering of [Ca 2+ ] i . Thus, in cardiac pathologies with disturbed RRC dynamics and / or augmented TRPA1 activity, TRPA1 may represent an anti-arrhythmic target with untapped therapeutic potential. 13–17
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".