Comprehensive Hemodynamic Analysis of Arteriolar Networks in the Rat Gluteus Maximus Muscle
Bibliographic record
Abstract
Introduction Theoretical blood flow models enable comprehensive analysis of blood flow, pressure, resistance, and hematocrit distribution within microvascular networks. However, the validity of data produced by such models is dependent on the detail of its experimentally derived inputs. Currently, many theoretical models of skeletal muscle hemodynamics are limited to incorporating experimental inputs from small network segments or trees, or from non‐locomotive tissues such as the mesentery. Thus, the objective of this study was to utilize recently collected experimental (geometric and topological) data from complete arteriolar networks of locomotive skeletal muscle as inputs for our computational blood flow model. Using outputs from theoretical simulations, we evaluated inter‐network hemodynamic variability in an effort to describe the level of functional homology among skeletal muscle arteriolar networks. Methods The rat GM provides the ideal experimental model to study locomotive skeletal muscle. Its planar geometry and uniform thinness enable access (within a single focal plane) to its entire microcirculation for microscopic imaging and perturbation. Using intravital videomicroscopy, the GM (n=4) was scanned under baseline conditions and photomontages were compiled (~400 images per network). Photomontages were registered to a MATLAB x–y coordinate system and scaled digital networks were generated. Approximately 1500 discrete nodes were used to mathematically model each network, with an average length of approximately 100 μm between each node, resulting in 179 to 239 vessel segments per network. A previously established two‐phase blood flow model (MATLAB) was used to simulate and assess the hemodynamic properties of each arteriolar network including heterogeneity within each network and variability between networks. Results Blood flow rate in each vessel segment decayed exponentially with increasing (1A to 9A) arteriolar order for each network (Exponential Decay fit: R 2 0.71 to 0.88). Additionally, the coefficients of variation (a measure of blood flow distribution) at each order ranged from 30.24% to 86.79%. Log‐log plots between blood flow and arteriolar diameter showed a strong correlation for each network (Linear Regression, R 2 0.65 to 0.86) with similar slopes of regression lines (3.08 to 3.81). Conclusion Simulation results show strong homology in blood flow properties between all the networks considered. Other hemodynamic properties such as RBC flow and hematocrit, as well as overall resistance, are now being analyzed with respect to both arteriolar order and diameter. This analysis allows detailed comparison of the hemodynamic properties of the different arteriolar networks studied, including the extent to which observed topological and geometric homologies result in similar hemodynamic outcomes. Support or Funding Information NSERC
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".