Changes in whole tissue heme concentration dissociates muscle deoxygenation from muscle oxygen extraction during passive head-up tilt
Bibliographic record
Abstract
Skeletal muscle deoxygenated hemoglobin and myoglobin concentration ([HHb]), assessed by near-infrared spectroscopy (NIRS), is commonly used as a surrogate of regional O2 extraction (reflecting the O2 delivery-to-consumption ratio, Q̇/V̇o2). However, [HHb] change (Δ[HHb]) is also influenced by capillary-venous heme concentration, and/or small blood vessel volume (reflected in total heme; [THb]). We tested the hypotheses that Δ[HHb] is associated with O2 extraction, and insensitive to [THb], over a wide range of Q̇/V̇o2 elicited by passive head-up tilt (HUT; 10-min, 15° increments, between -10° and 75°). Steady-state common femoral artery blood flow (FBF) was measured by echo-Doppler, and time-resolved NIRS measured [HHb] and [THb] of vastus lateralis (VL) and gastrocnemius (GS) in 13 men. EMG confirmed muscles were inactive. During HUT in VL [HHb] increased linearly (57 ± 10 to 101 ± 16 μM; P < 0.05 above 15°) and was associated (r(2) ∼ 0.80) with the reduction in FBF (618 ± 75 ml/min at 0° to 268 ± 52 ml/min at 75°; P < 0.05 above 30°) and the increase in [THb] (228 ± 30 vs. 252 ± 32 μM; P < 0.05 above 15°). GS response was qualitatively similar to VL. However, there was wide variation within and among individuals, such that the overall limits of agreement between Δ[HHb] and ΔFBF ranged from -35 to +19% across both muscles. Neither knowledge of tissue O2 saturation nor vascular compliance could appropriately account for the Δ[HHb]-ΔFBF dissociation. Thus, under passive tilt, [HHb] is influenced by Q̇/V̇o2, as well as microvascular hematocrit and/or tissue blood vessel volume, complicating its use as a noninvasive surrogate for muscle microvascular O2 extraction.
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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".