3D imaging, registration, and analysis of the early mouse embryonic vasculature
Bibliographic record
Abstract
BACKGROUND: Cardiovascular development requires the input of a large number of molecular signaling molecules, and undergoes tightly regulated, three-dimensional developmental patterning. Conventional developmental biology techniques have successfully identified many of the signaling cascades and molecular cues necessary for proper cardiovascular development, which has furnished us with a wealth of biochemical, molecular, and biologically functional information on how tightly linked cardiac and vascular development are. Still missing, however, is a genuine appreciation of the three-dimensional (3D) nature of these important developmental steps. RESULTS: Optical projection tomography (OPT) is a 3D imaging technique that allows for high-resolution imaging of early mouse embryos and their developing cardiovascular systems when a PECAM-1 antibody stain is used to highlight the vascular branching. Reported here is a method in which several 3D images of mouse embryo vasculatures can be registered, thus allowing for analysis of within-strain variance between genetically identical mouse pups. Post-registration, small differences in somitogenesis and ventricular trabeculation patterning can be visualized in mouse pups that differ by as little as a few hours of gestational time. Additionally, similarity metrics (cross-correlation values) can be calculated to quantify similarities and differences. Two different mouse strains are analyzed (C57Bl/6 and CD-1), and similar results are recognized in each strain. CONCLUSIONS: Visualizing the cardiovascular system in such a precise 3D manner allows for more accuracy in describing the steps that take place during cardiovascular development. This novel method will be applicable to many developmental biology questions in other organ systems and other species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".