KCa3.1 Promotes the Migration of Macrophages From Epicardial Adipose Tissue to Induce Vulnerability to Atrial Fibrillation During Rapid Pacing
Bibliographic record
Abstract
Background The relationship between local epicardial adipose tissue (EAT) macrophages and atrial fibrillation (AF) remains unclear. The purpose of this study was to investigate the role of K Ca 3.1 in the migration of macrophages from EAT to adjacent atrial tissue during rapid pacing. Methods Part 1: Eighteen beagles were randomly divided into the sham group, pacing group, and pacing + clodronate liposome (CL) group. Part 2: Eighteen beagles were randomly divided into the sham group, pacing group, and pacing + TRAM-34 group. HL-1 cells and RAW264.7 cells were co-cultured to explore the specific migratory mechanism of macrophages. Results Depleting EAT macrophages significantly reduced macrophage infiltration in the adjacent atrium and the induction of AF in canines with rapid atrial pacing. TRAM-34 significantly inhibited the migration of macrophages from EAT to the adjacent atrium and electrical remodelling in canines with rapid atrial pacing. Compared with those of the control HL-1 cells, the secretion of CCL2 and the number of migrating macrophages in pacing HL-1 cells was significantly increased, which could be reversed by TRAM-34. Further in vitro experiments showed that K Ca 3.1 regulated CCL2 secretion through the p65/STAT3 signalling pathway. Conclusions Inhibiting myocardial K Ca 3.1 reduced the migration of EAT macrophages to adjacent atrial muscles caused by rapid atrial pacing, thereby decreasing vulnerability to AF. The mechanism by which K Ca 3.1 regulates CCL2 may be related to the p65/STAT3 signalling pathway.
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) | 0.002 | 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".