Simultaneous imaging of blood flow dynamics and vascular remodelling during development
Bibliographic record
Abstract
Normal vascular development requires blood flow. Time-lapse imaging techniques have revolutionised our understanding of developmental biology, but measuring changes in blood flow dynamics has met with limited success. Ultrasound biomicroscopy and optical coherence tomography can concurrently image vascular structure and blood flow velocity, but these techniques lack the resolution to accurately calculate fluid forces such as shear stress. This is important because hemodynamic forces are biologically active and induce changes in the expression of genes important for vascular development. Regional variations in shear stress, rather than the overall level, control processes such as vessel enlargement and regression during vascular remodelling. We present a technique to concurrently visualise vascular remodelling and blood flow dynamics. We use an avian embryonic model and inject an endothelial-specific dye and fluorescent microspheres. The motion of the microspheres is captured with a high-speed camera and the velocity of the blood flow in and out of the region of interest is quantified by micro-particle image velocitymetry (µPIV). The vessel geometry and flow are used to numerically solve the flow physics with computational fluid dynamics (CFD). Using this technique, we can analyse changes in shear stress, pressure drops and blood flow velocities over a period of 10 to 16 h. We apply this to study the relationship between shear stress and chronic changes in vessel diameter during embryonic development, both in normal development and after TGFβ stimulation. This technique allows us to study the interaction of biomolecular and biomechanical signals during vascular remodelling using an in vivo developmental model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".