Blood Flow Calculations in 3D Microvascular Networks Reconstructed from Multi‐Photon Microscopy
Bibliographic record
Abstract
Laser scanning multi‐photon microscopy (MPM) makes it possible to obtain detailed, three‐dimensional images of tissue microvascular geometry. When combined with mathematical models of network blood flow and mass transport, this data will be extremely useful in estimating local oxygen delivery capabilities and correlating them with direct measures of tissue oxygenation. The objectives of this study were to reconstruct 3D microvascular networks imaged with MPM, and perform theoretical calculations of blood flow in the reconstructed networks. An intravital 3D angiogram of a complete microvascular unit in the mouse hindlimb, including feeding arterioles, capillary segments and collecting venules, was used as the basis for this study. Algorithms for skeletonizing the microvascular network and determining network connectivity and vessel diameters were implemented. The reconstructed network and a previously developed blood flow model were used to calculate steady‐state hemodynamics in the network. In the future, these results will be refined using experimental measures of 3D network hemodynamics. Financial Support: HSFC, MSFHR, CIHR/HSFC IMPACT Fellowships (RMB).
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".