Dynamics of intramuscular<sup>31</sup>P-MRS P<sub>i</sub>peak splitting and the slow components of PCr and O<sub>2</sub>uptake during exercise
Bibliographic record
Abstract
The dynamics of pulmonary O(2) uptake (Vo(2)) during the on-transient of high-intensity exercise depart from monoexponentiality as a result of a "slow component" whose mechanisms remain conjectural. Progressive recruitment of glycolytic muscle fibers, with slow O(2) utilization kinetics and low efficiency, has, however, been suggested as a mechanism. The demonstration of high- and low-pH components of the exercising skeletal muscle (31)P magnetic resonance (MR) spectrum [inorganic phosphate (P(i)) peak] at high work rates (thought to be reflective of differences between oxidative and glycolytic muscle fibers) is also consistent with this conjecture. We therefore investigated the dynamics of Vo(2) (using a turbine and mass spectrometry) and intramuscular ATP, phosphocreatine (PCr), and P(i) concentrations and pH, estimated from the (31)P MR spectrum. Eleven healthy men performed prone square-wave high-intensity knee extensor exercise in the bore of a whole body MR spectrometer. A Vo(2) slow component of magnitude 15.9 +/- 6.9% of the phase II amplitude was accompanied by a similar response (11.9 +/- 7.1%) in PCr concentration. Only five subjects demonstrated a discernable splitting of the P(i) peak, however, which began from between 35 and 235 s after exercise onset and continued until cessation. As such, the dynamics of the pH distribution in intramuscular compartments did not consistently reflect the temporal features of the Vo(2) slow component, suggesting that P(i) splitting does not uniquely reflect the activity of oxidative or glycolytic muscle fibers per se.
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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 | 0.001 |
| 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".