Flow dynamics control the location of sprouting and direct elongation during developmental angiogenesis
Bibliographic record
Abstract
Angiogenesis is tightly controlled by a number of signalling pathways. Although our understanding of the molecular mechanisms involved in angiogenesis has rapidly increased, the role that biomechanical signals play in this process is understudied. We recently developed a technique to simultaneously analyse flow dynamics and vascular remodelling by time-lapse microscopy in the capillary plexus of avian embryos and used this to study the hemodynamic environment present during angiogenic sprouting. We found that sprouts always form from a vessel at lower pressure towards a vessel at higher pressure, and that sprouts form at the location of a shear stress minimum, but avoid locations where two blood streams merge even if this point is at a lower level of shear stress than the sprouting location. Using these parameters, we were able to successfully predict sprout location in quail embryos. We also found that the pressure difference between two vessels is permissive to elongation, and that sprouts will either change direction or regress if the pressure difference becomes negative. Furthermore, the sprout elongation rate is proportional to the pressure difference between the two vessels. Our results show that flow dynamics are predictive of the location of sprout formation in perfused vascular networks and that pressure differences across the interstitium can guide sprout elongation.
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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 | 0.000 |
| 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 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".