Carbon Dioxide Concentration Alters the Dynamics of Oxygen-Mediated Capillary Blood Flow Response s in Skeletal Muscle
Bibliographic record
Abstract
ABSTRACT Hypothesis We hypothesize that the dynamics of O 2 ‐mediated blood flow responses in skeletal muscle capillaries are altered under different tissue carbon dioxide concentrations ([CO 2 ]) due to the interaction of overlapping mechanisms. Methods Eight male Sprague Dawley rats (164–215 g) were anesthetized and instrumented for systemic monitoring. The extensor digitorum longus muscle was isolated and reflected over a microfluidic gas exchange chamber mounted in an inverted microscope stage. 4‐min intravital video recordings of capillary blood flow during O 2 challenges consisted of a 1‐min baseline at 7% O 2 concentration ([O 2 ]), followed by 3 min at 2% [O 2 ], under constant background [CO 2 ] at 2%, 5%, and 8%. Recordings were analyzed offline using custom MATLAB software. Time transients (τ) of capillary hemodynamic responses were determined using a least squared regression fit to single‐ and double‐exponential models. Results Fast component τ of the O 2 ‐mediated capillary red blood cell (RBC) velocity response was 2.1 s for 2% [CO 2 ], 3.8 s for 5% [CO 2 ], and 7.0 s for 8% [CO 2 ]. Third minute low [O 2 ] capillary RBC supply rate increases from baseline were greater for 8% [CO 2 ] (5% [CO 2 ]: 6.4 ± 8.7 cells/s vs. 8% [CO 2 ]: 8.5 ± 10.7 cells/s, p = 0.0007). Conclusion The fast component τ of O 2 ‐mediated capillary hemodynamic responses was found to be slower with increasing background tissue [CO 2 ], suggesting that multiple interacting mechanisms are involved to appropriately regulate O 2 delivery under different CO 2 conditions in partial support of the hypothesis.
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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".