Electrophysiological engineering of heart-derived cells with calcium-dependent potassium channels improves cell therapy efficacy for cardioprotection
Bibliographic record
Abstract
Abstract We have shown that calcium-activated potassium (KCa)-channels regulate fundamental progenitor-cell functions, including proliferation, but their contribution to cell-therapy effectiveness is unknown. Here, we test the participation of KCa-channels in human heart explant-derived cell (EDC) physiology and therapeutic potential. TRAM34-sensitive KCa3.1-channels, encoded by theKCNN4gene, are exclusively expressed in therapeutically bioactive EDC subfractions and maintain a strongly polarized resting potential; whereas therapeutically inert EDCs lack KCa3.1 channels and exhibit depolarized resting potentials. Somatic gene transfer ofKCNN4results in membrane hyperpolarization and increases intracellular [Ca2+], which boosts cell-proliferation and the production of pro-healing cytokines/nanoparticles. Intramyocardial injection of EDCs afterKCNN4-gene overexpression markedly increases the salutary effects of EDCs on cardiac function, viable myocardium and peri-infarct neovascularization in a well-established murine model of ischemic cardiomyopathy. Thus, electrophysiological engineering provides a potentially valuable strategy to improve the therapeutic value of progenitor cells for cardioprotection and possibly other indications.
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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".