Evaluation of intramyocellular lipid breakdown during exercise by biochemical assay, NMR spectroscopy, and Oil Red O staining
Bibliographic record
Abstract
The study compared the net decline of intramyocellular lipids (IMCL) during exercise (n = 18) measured by biochemical assay (BIO) and Oil Red O (ORO) staining on biopsy samples from vastus lateralis muscle and by (1)H-MR spectroscopy (MRS) sampled in an 11 x 11 x 18-mm(3) voxel in the same muscle. IMCL was measured before and after a 2-h cycling bout ( approximately 75% V(.)(O(2) peak)). ORO and MRS measurements showed substantial IMCL use during exercise of 31 +/- 12 and 47 +/- 6% of preexercise IMCL content. In contrast, use of BIO for IMCL determination did not reveal an exercise-induced breakdown of IMCL (2 +/- 9%, P = 0.29) in young healthy males. Correlations between different measures of exercise-induced IMCL degradation were low. Coefficients were 0.48 for MRS vs. ORO (P = 0.07) and were even lower for BIO vs. MRS (r = 0.38, P = 0.13) or ORO (r = 0.08, P = 0.78). This study demonstrates that different methods to measure IMCL in human muscles can result in different conclusions with regard to exercise-induced IMCL changes. MRS has the advantage that it is noninvasive, however, not fiber type specific and hampered by an at least 30-min delay in measurements after exercise completion and may overestimate IMCL use. BIO is the only quantitative method but is subject to variation when biopsies have different fiber type composition. However, BIO yields lower IMCL breakdown compared with ORO and MRS. ORO has the major advantage that it is fiber type specific, and it therefore provides information that is not available with the other methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".