Diet‐induced obesity alters skeletal muscle satellite cell functional capacity.
Bibliographic record
Abstract
Skeletal muscle satellite cells are integral for normal muscle growth. As skeletal muscle is the largest organ for blood glucose disposal, we were interested to ascertain whether a high fat diet (HFD) would negatively impact muscle satellite cells, and thus have a negative impact on skeletal muscle growth. Using isolated, intact single fibers, we found that the number of satellite cells in an 'activated state' was significantly enhanced (159 ± 21% of control) while their proliferative capacity was significantly impaired (55 ± 9 % of control). Preliminary analysis of cells isolated from HFD muscle demonstrate neither a significant impairment in migration capacity, using a Boyd's filter chamber, nor in the ability to fuse or express myosin heavy chain after 48 and 96 hours of exposure to differentiation media. These preliminary findings suggest that HFD induces a satellite cell state similar to that found in atrophic muscle, where there is an initial enhancement in activation and impairment in proliferative ability. Alternative techniques for assessing satellite cell activation and proliferation are ongoing to confirm these results. Importantly, the potential irreversible effects of diet‐induced obesity on satellite cell function in young muscle requires further investigation to fully define the implications on long‐term muscle growth and regeneration.
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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".