Scoping Review: Integration of the Major Mechanisms Underlying the Regulation of Arteriolar Tone
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide. Arteriolar tone regulation plays a critical role in maintaining appropriate organ blood flow and perfusion distribution, which is vital for both vascular and overall health. SUMMARY: This scoping review aimed to explore the interplay between five major regulators of arteriolar tone: metabolism (adenosine), adrenergic control (norepinephrine), myogenic activation (intravascular pressure), perivascular oxygen tension, and intraluminal flow rates. Specifically, the aim was to address how arteriolar reactivity changes in the presence of other vasoactive stimuli and by what mechanisms. The review focused on animal studies that investigated the impact of combining two or more of these stimuli on arteriolar diameter. Overall, 848 articles were identified through MEDLINE and EMBASE database searches, and 38 studies were included in the final review. KEY MESSAGES: The results indicate that arteriolar reactivity is influenced by multiple factors, including competitive processes, structural limitations, and indirect interactions among stimuli. Additionally, the review identified a lack of research involving female animal models and limited insight into the interaction of molecular signaling pathways, which represent gaps in the literature.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".