Mesenchymal Stem Cell Transplantation Mitigates Electrophysiological Remodeling in a Rat Model of Myocardial Infarction
Bibliographic record
Abstract
INTRODUCTION: Transplantation of mesenchymal stem cells (MSCs) has shown therapeutic potential for cardiovascular diseases, but the electrophysiological implications are not understood. The purpose of this study was to evaluate the impact of MSC transplantation on adverse electrophysiological remodeling in the heart following myocardial infarction (MI). METHODS AND RESULTS: Three weeks after coronary ligation to induce MI in rats, MSCs or culture medium were directly injected into each infarct. One to two weeks later, hearts were excised, Langendorff-perfused, and optically mapped using the potentiometric fluorescent dye Di-4-ANEPPS. Quantitative real-time PCR was also performed to assess gene expression. Optical mapping showed that post-MI reduction in conduction velocity (from 0.70 ± 0.04 m/s in 12 normal controls to 0.47 ± 0.02 m/s in 11 infarcted hearts, P < 0.05) was attenuated with MSC transplantation (0.65 ± 0.04 m/s, n = 18, P < 0.05). Electrophysiological changes correlated with higher vascular density and better-preserved ventricular geometry in MSC-transplanted hearts. A number of ion channel genes showed changes in RNA expression following infarction. In particular, the expression of Kir2.1, which mediates the inward rectifier potassium current, I(K1), was reduced in infarcted tissues (n = 7) to 13.8 ± 3.7% of normal controls, and this post-MI reduction was attenuated with MSC transplantation (44.4 ± 11.2%, n = 7, P < 0.05). CONCLUSION: In addition to promoting angiogenesis and limiting adverse structural remodeling in infarcted hearts, MSC transplantation also alters ion channel expression and mitigates electrophysiological remodeling. Further understanding of the electrophysiological impact of MSC transplantation to the heart may lead to the development of cell-based therapies for post-MI arrhythmias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".