Shifted vascular optimization: the emergence of a new arteriolar behaviour with chronic metabolic disease
Bibliographic record
Abstract
NEW FINDINGS: What is the topic of this review? Altered perfusion distribution at skeletal muscle arteriolar bifurcations and how this is modified by development of chronic metabolic disease. What advances does it highlight? The outcome created is a distribution of erythrocytes in the distal microcirculation that is characterized by increased spatial heterogeneity and reduced flexibility such that mass transport/exchange within the network is impaired, with limited ability to respond to imposed challenges. This advances our understanding of how altered vascular structure and function with metabolic disease impairs perfusion to skeletal muscle at a level of resolution that would not be identified through bulk flow responses. ABSTRACT: This review is based on the presentation 'Shifted vascular optimization: the emergence of a new arteriolar behaviour with chronic metabolic disease', given at the Symposium 'Understanding Complex Behaviours in the Microcirculation: from Blood Flow to Oxygenation' during the Annual Meeting of the Physiological Society at the Aberdeen Exhibition and Conference Centre in Aberdeen, UK in July 2019. The past years of dedicated investigation on linkages between vascular (dys)function under conditions of elevated cardiovascular disease risk and tissue/organ performance have produced results and insights that frequently suffer from limited correlation and causation. Reaching out from this challenge, it was proposed that this may reflect a 'level of resolution' argument and that altered haemodynamic behaviour in vascular networks could be a stronger predictor of functional outcomes than higher resolution measures. Using this approach, we have determined that an attractor that describes the spatial and temporal shift in perfusion distribution at successive arteriolar bifurcations within the skeletal muscle is a strong predictor of functional outcomes within animals and provides novel insight into fundamental mechanistic contributors to altered patterns of intra-muscular perfusion. This article focuses on the applicability and utility of the attractor in models of cardiovascular and metabolic disease risk of increasing severity. We will also discuss the utility of the attractor in terms of understanding the effectiveness of aggressive interventions for reversing established vasculopathy and perfusion impairments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".