A raised metabolic rate slows pulmonary O<sub>2</sub>uptake kinetics on transition to moderate-intensity exercise in humans independently of work rate
Bibliographic record
Abstract
During exercise below the lactate threshold (LT), the rate of adjustment (τ) of pulmonary VO(2) uptake (τ) is slowed when initiated from a raised work rate. Whether this is consequent to the intrinsic properties of newly recruited muscle fibres, slowed circulatory dynamics or the effects of a raised metabolism is not clear. We aimed to determine the influence of these factors on τV(O(2)) using combined in vivo and in silico approaches. Fifteen healthy men performed repeated 6 min bouts on a cycle ergometer with work rates residing between 20 W and 90% LT, consisting of the following: (1) two step increments in work rate (S1 and S2), one followed immediately by the other, equally bisecting 20 W to 90% LT; (2) two 20 W to 90% LT bouts separated by 30 s at 20 W to raise muscle oxygenation and pretransition metabolism (R1 and R2); and (3) two 20 W to 90% LT bouts separated by 12 min at 20 W allowing full recovery (F1 and F2). Pulmonary O(2) uptake was measured breath by breath by mass spectrometry and turbinometry, and quadriceps oxygenation using near-infrared spectroscopy. The influence of circulatory dynamics on the coupling of muscle and τV(O(2)) lung was assessed by computer simulations. The τV(O(2)) in R2 (32 ± 9 s) was not different (P > 0.05) from S2 (30 ± 10 s), but both were greater (P < 0.05) than S1 (20 ± 10 s) and the F control bouts (26 ± 10 s). The slowed V(O(2)) kinetics in R2 occurred despite muscle oxygenation being raised throughout, and could not be explained by slowed circulatory dynamics (τV(O(2)) predicted by simulations: S1 = R2 < S2). These data therefore suggest that the dynamics of muscle O(2) consumption are slowed when exercise is initiated from a less favourable energetic state.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".