Cell fusion contributes to the rescue of apoptotic cardiomyocytes by bone marrow cells
Bibliographic record
Abstract
Cardiomyocyte apoptosis is an important contributor to the progressive cardiac dysfunction that culminates in congestive heart failure. Bone marrow cells (BMCs) restore cardiac function following ischaemia, and transplanted BMCs have been reported to fuse with cells of diverse tissues. We previously demonstrated that the myogenic conversion of bone marrow stromal cells increased nearly twofold when the cells were co-cultured with apoptotic (TNF-α treated) cardiomyocytes. We therefore hypothesized that cell fusion may be a major mechanism by which BMCs rescue cardiomyocytes from apoptosis. We induced cellular apoptosis in neonatal rat cardiomyocytes by treatment with hydrogen peroxide (H(2)O(2)). The TUNEL assay demonstrated an increase in apoptosis from 4.5 ± 1.3% in non-treated cells to 19.0 ± 4.4% (P < 0.05) in treated cells. We subsequently co-cultured the apoptotic cardiomyocytes with BMCs and assessed cell fusion using flow cytometry. Fusion was rare in the non-treated control cardiomyocytes (0.3%), whereas H(2)O(2) treatment led to significantly higher fusion rates than the control group (P < 0.05), with the highest rate of 7.9 ± 0.3% occurring at 25 μM H(2)O(2). We found an inverse correlation between cell fusion and completion of cardiomyocyte apoptosis (R(2) = 0.9863). An in vivo mouse model provided evidence of cell fusion in the infarcted myocardium following the injection of BMCs. The percentage of cells undergoing fusion was significantly higher in mice injected with BMCs following infarction (8.8 ± 1.3%) compared to mice that did not undergo infarction (4.6 ± 0.6%, P < 0.05). Enhancing cell fusion may be one method to preserve cardiomyocytes following myocardial infarction, and this new approach may provide a novel target for cardiac regenerative therapies.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".