Embryonic and Neonatal Phenotyping of Genetically Engineered Mice
Bibliographic record
Abstract
Considerable progress has been made in adapting existing and developing new technologies to enable increasingly detailed phenotypic information to be obtained in embryonic and newborn mice. Sophisticated methods for imaging mouse embryos and newborns are available and include ultrasound and magnetic resonance imaging (MRI) for in vivo imaging, and MRI, vascular corrosion casts, micro-computed tomography, and optical projection tomography (OPT) for postmortem imaging. In addition, Doppler and M-mode ultrasound are useful noninvasive tools to monitor cardiac and vascular hemodynamics in vivo in embryos and newborns. The developmental stage of the animals being phenotyped is an important consideration when selecting the appropriate technique for anesthesia or euthanasia and for labeling animals in longitudinal studies. Study design also needs to control for possible differences between inter- and intralitter variability, and for possible long-term developmental effects caused by anesthesia and/or procedures. Noninvasive or minimally invasive intravenous or intracardiac injections or blood sampling, and arterial pressure and electrocardiography (ECG) measurements are feasible in newborns. Whereas microinjection techniques are available for embryos as young as 6.5 days of gestation, further advances are required to enable minimally invasive fluid or tissue samples, or blood pressure or ECG measurements, to be obtained from mouse embryos in utero. The growing repertoire of techniques available for phenotyping mouse embryos and newborns promises to accelerate knowledge gained from studies using genetically engineered mice to understand molecular regulation of morphogenesis and the etiology of congenital diseases.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".