A conceptual framework for predicting and addressing the consequences of disease‐related microvascular dysfunction
Bibliographic record
Abstract
OBJECTIVE: A growing body of evidence indicates that impaired microvascular perfusion plays a pathological role in a number of diseases. This manuscript aims to better define which aspects of microvascular perfusion are important, what mass transport processes (eg, insulin action, tissue oxygenation) may be impacted, and what therapies might reverse these pathologies. METHODS: We derive a theory of microvascular perfusion and solute flux drawing from established relationships in mass transport and anatomy. We then apply this theory to predict relationships between microvascular perfusion parameters and microvascular solute flux. RESULTS: For convection-limited exchange processes (eg, pulmonary oxygen uptake), our model predicts that bulk blood flow is of primary importance. For diffusion-limited exchange processes (eg, insulin action), our model predicts that perfused capillary density is of primary importance. For convection/diffusion co-limited exchange processes (eg, tissue oxygenation), our model predicts that various microvascular perfusion parameters interact in a complex, context-specific manner. We further show that our model can predict established mass transport defects in disease (eg, insulin resistance in diabetes). CONCLUSIONS: The contributions of microvascular perfusion parameters to tissue-level solute flux can be described using a minimal mathematical model. Our results hold promise for informing therapeutic interventions targeting microvascular perfusion.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".