Modeling the hemodynamic response in capillaries to an altered tissue oxygen environment
Bibliographic record
Abstract
Regulation of the convective O 2 supply to capillary beds within organs is accomplished by coordinated changes in arteriolar diameter. To test the hypothesis that red blood cells (RBC) are a key component of this regulation system, in vivo experiments have been performed using a gas exchange chamber with computer‐controlled gas flowmeters to alter the O 2 environment at the surface of a rat skeletal muscle. The microvascular responses to sine oscillations in chamber O 2 levels were determined by measuring RBC velocity ( v ), hematocrit ( Hc ), and RBC supply rate ( SR ) in capillaries near the muscle surface. In approximately half the capillaries examined, the regulatory response resulted in changes in both v and Hc that were correlated to oscillations in chamber O 2 ; however, in other capillaries only one of these parameters was regulated. To explain the observed variability, we have developed a model describing the relative magnitude of blood flow and RBC distributions at arteriolar and capillary bifurcations deeper in the muscle that determine blood flow to the surface capillaries. This model utilizes empirical relations describing RBC distribution at diverging bifurcations (phase separation) and yields predictions of capillary velocity and hematocrit based on the baseline arteriolar geometry and the changes in vessel diameter resulting from the local regulation of O 2 supply.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".