High fat feeding enhances the ability of mesenchymal stem cell therapy to modulate mitochondria in the infarcted heart
Bibliographic record
Abstract
OBJECTIVE Evaluate the effect of mesenchymal stem cell (MSC) therapy on cardiac mitochondria following a myocardial infarction (MI). METHODS C57BL6 mice were high fat‐fed (HFF) for eight weeks and separated into three groups: HFF‐SHAM, HFF‐MI+PBS mice given a MI followed by intramyocardial injection of saline and HFF‐MI+MSC mice receiving MSCs. HFF groups were age‐matched with chow‐fed mice. RESULTS Mitochondrial function was evaluated via high‐resolution respirometry in permeabilized, peri‐infarct fibers 5 weeks post‐MI. In chow‐fed mice, maximal oxygen flux (155.30 vs 122.63 vs 135.61 pmol/s/mgww/CS; SHAM vs MI+PBS vs MI+MSC; n=9–10) and oxidative phosphorylation (OXPHOS) efficiency (RCR = 3.17 vs 2.34 vs 2.84) was preserved by MSC therapy. Also, MSC treatment prevented a decline in mitochondrial content as indicated by citrate synthase activity (1.28 vs 0.90 vs 1.34 mmol/min/mg pro). In HFF mice, the MSC treatment maintained mitochondrial content (1.21 vs 0.99 vs 1.24) and OXPHOS efficiency (RCR = 2.18 vs 1.88 vs 2.23). Also, HFF‐MI+MSC hearts exhibited an exaggerated decline in futile oxygen flux (61.39 vs 50.69 vs 38.85; n=8–9). Immunoblots show the HFF‐MI+MSC hearts have lower mitochondrial complex I and II, UCP3 and TFAM protein levels (n=6). CONCLUSIONS MSC therapy minimizes mitochondrial dysfunction post‐MI and HFF enhances the therapeutic potential of MSCs. Funded by CIHR and Killam Trusts.
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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 | 0.001 |
| 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".