Identification of shear stress as a potential vasoconduction signal across microvascular networks
Bibliographic record
Abstract
The objective of this study was to computationally estimate the effects of vessel specific vasoconstriction on immediate shear stress changes across microvascular networks. Shear stress due to microvascular blood flow is an established initiator of ion-mediated signaling along microvessels which regulates control of microcirculatory blood flow. Yet, beyond initiating local vasomotion in a vessel, shear stress as a vasoconduction signal itself and characteristics of hydrodynamic propagation via blood flow are not well understood. In the current work, we use images of mesenteric microvascular networks from adult rat tissues and a network segmental blood flow model to simulate various vessel constriction scenarios and estimate subsequent shear stress changes and distances these changes spread from the site of constriction. Scenarios involving both arteriolar constriction and capillary constriction are considered, in addition to a microvascular network from muscle tissue. The findings generally reveal heterogenous and physiologically relevant shear stress changes across the networks for all cases, with magnitudes spanning a wide range and can exceed 30 dyne/cm 2 . Further, physiological relevant wall shear changes were predicted at distances several mm from the stimulus site. Spatial patterns of shear stress change relative to network topology and capillary density are also identified. Altogether, the results invigorate consideration and discussion about shear stress as a potential player in vasoconduction responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".