Effects of Genes and Environment on the Fetoplacental Arterial Microcirculation in Mice Revealed by Micro‐Computed Tomography Imaging
Bibliographic record
Abstract
The fetoplacental arterial tree is critical for efficient distribution of arterial blood to capillaries throughout the placental exchange region; yet, little is known about the factors and mechanisms that control its development. Advances in micro-CT imaging and analysis, and available mutant mouse strains, are facilitating rapid progress. Indeed, micro-CT studies show that genetic differences between the CD1 and C57Bl/6 mouse strains, and between Gcm1 heterozygotes and wild-type littermates alter the developmental trajectory of the fetoplacental arterial tree as do environmental factors including maternal exposure to toxins in cigarette smoke and malarial infection. Relative to other vascular beds, the fetoplacental arterial tree is particularly tractable because veins can more easily be excluded when infusing the contrast agent and because of the placenta's small size, which means that the whole organ can be imaged (maintaining connectivity) and that the tree is simpler (fewer branching generations). Despite these differences, measured parameters were found to be similar to arterial trees in other adult rodent organs. Thus, micro-CT analysis provides a means for advancing of our understanding of the mechanisms controlling development of the fetoplacental arterial tree. Results will likely have relevance to other arterial vasculatures as well.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".