Differential effect of mesenchymal stem cell therapy on cardiac glucose utilization in the infarcted heart following chow and high fat feeding
Bibliographic record
Abstract
OBJECTIVE To determine whether mesenchymal stem cell therapy minimizes abnormal cardiac glucose utilization following a myocardial infarction (MI) in the presence of dietary‐induced insulin resistance. METHODS C57BL6 mice were high fat‐fed (HFF) for eight weeks. Mice were separated into three groups: HFF‐SHAM, HFF‐MI+PBS in which mice were given a MI followed by intramyocardial injection of saline and the HFF‐MI+ MSC group receiving MSCs. HFF groups were age‐matched with chow‐fed mice. RESULTS Five weeks post‐MI, cardiac glucose utilization was evaluated in the conscious, unrestrained mouse by performing a hyperinsulinemic‐euglycemic (insulin) clamp coupled with [14C]‐2‐deoxyglucose administration. In chow‐fed mice, MSCs preserved glucose uptake (μg/mg/min) in the remote left ventricle (RLV) (7.36 vs. 5.16 vs 6.00; CHOWSHAM vs CHOW‐MI+PBS vs CHOW‐MI+MSC). In HFF mice, MSCs increased insulin‐stimulated glucose uptake in the periinfarct region (3.85 vs 3.84 vs 5.24; HFF‐SHAM vs HFF‐MI+ MSC vs HFF‐MI+MSC). RLV glucose uptake in the HFF‐MI+ MSC group was higher than that of the HFF‐MI+PBS mice (4.80 vs 3.38). Immunoblots indicate MSCs preserve insulin signaling (p‐Akt/Akt) in chow‐fed mice. In contrast, this ratio was similar in HFF groups. CONCLUSIONS MSC tberapy promotes insulin sensitivity post‐MI differently in response to a high fat diet. Funded by CIHR and Killam Trusts.
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) | 0.001 | 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".