Effects of heavy-intensity priming exercise on pulmonary oxygen uptake kinetics and muscle oxygenation in heart failure with preserved ejection fraction
Bibliographic record
Abstract
Exercise intolerance is a hallmark feature in heart failure with preserved ejection fraction (HFpEF). Prior heavy exercise (“priming exercise”) speeds pulmonary oxygen uptake (V̇o2p) kinetics in older adults through increased muscle oxygen delivery and/or alterations in mitochondrial metabolic activity. We tested the hypothesis that priming exercise would speed V̇o2pon-kinetics in patients with HFpEF because of acute improvements in muscle oxygen delivery. Seven patients with HFpEF performed three bouts of two exercise transitions: MOD1, rest to 4-min moderate-intensity cycling and MOD2, MOD1 preceded by heavy-intensity cycling. V̇o2p, heart rate (HR), total peripheral resistance (TPR), and vastus lateralis tissue oxygenation index (TOI; near-infrared spectroscopy) were measured, interpolated, time-aligned, and averaged. V̇o2pand HR were monoexponentially curve-fitted. TPR and TOI levels were analyzed as repeated measures between pretransition baseline, minimum value, and steady state. Significance was P < 0.05. Time constant (τ; tau) V̇o2p(MOD1 49 ± 16 s) was significantly faster after priming (41 ± 14 s; P = 0.002), and the effective HR τ was slower following priming (41 ± 27 vs. 51 ± 32 s; P = 0.025). TPR in both conditions decreased from baseline to minimum TPR ( P < 0.001), increased from minimum to steady state ( P = 0.041) but remained below baseline throughout ( P = 0.001). Priming increased baseline ( P = 0.003) and minimum TOI ( P = 0.002) and decreased the TOI muscle deoxygenation overshoot ( P = 0.041). Priming may speed the slow V̇o2pon-kinetics in HFpEF and increase muscle oxygen delivery (TOI) at the onset of and throughout exercise. Microvascular muscle oxygen delivery may limit exercise tolerance in HFpEF.
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 | 0.001 |
| 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".