Regulation of Skeletal Muscle Resistance Arteriolar Tone: Temporal Variability in Vascular Responses
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: A full understanding of the integration of the mechanisms of vascular tone regulation requires an interrogation of the temporal behavior of arterioles across vasoactive challenges. Building on previous work, the purpose of the present study was to start to interrogate the temporal nature of arteriolar tone regulation with physiological stimuli. METHODS: We determined the response rate of ex vivo proximal and in situ distal resistance arterioles when challenged by one-, two-, and three-parameter combinations of five major physiological stimuli (norepinephrine, intravascular pressure, oxygen, adenosine [metabolism], and intralumenal flow). Predictive machine learning models determined which factors were most influential in controlling the rate of arteriolar responses. RESULTS: Results indicate that vascular response rate is dependent on the intensity of the stimulus used and can be severely hindered by altered environments, caused by application of secondary or tertiary stimuli. Advanced analytics suggest that adrenergic influences were dominant in predicting proximal arteriolar response rate compared to metabolic influences in distal arterioles. CONCLUSION: These data suggest that the vascular response rate to physiologic stimuli can be strongly influenced by the local environment. Translating how these effects impact vascular networks is imperative for understanding how the microcirculation appropriately perfuses tissue across conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it