Quantitation of Hemodynamic Function during Developmental Vascular Regression in the Mouse Eye
Bibliographic record
Abstract
PURPOSE: Ultrasound biomicroscopy (UBM) utilizes frequencies higher than conventional diagnostic ultrasound and can noninvasively provide anatomic and functional information about mouse ocular structures in vivo at high resolution. Vascular development can also be assessed with high-frequency Doppler imaging, which permits detection and characterization of ocular blood flow not detectable at lower, conventional Doppler frequencies. METHODS: The eyes of CD-1 mice were examined daily from the day of birth to postnatal day (P)16. Hyaloid vascular system anatomy was imaged with UBM and microcomputed tomography (microCT). Blood flow velocity was also measured with Doppler UBM imaging in the hyaloid artery, vasa hyaloidea propria, tunica vasculosa lentis, and retina. RESULTS: In the mouse, the hyaloid vasculature degenerated from a well-defined structure at birth by progressive loss of branches. Hyaloid regression coincided with a progressive decrease in blood velocity detected in the hyaloid vascular structures, which is thought to be one of the major triggering factors of the regression in these vessels. At P13, no further blood flow was detected in the CD-1 mouse hyaloid vasculature. An inverse relationship was also shown between peak blood velocity in the lens and retina. CONCLUSIONS: UBM imaging provides a valuable means of rapidly and noninvasively characterizing ocular development in vivo. MicroCT scans have also provided intralumenal images of hyaloid vascular structure. This is the first study of vascular structure and function during the dynamic process of hyaloid vascular regression during mouse neonatal eye development and the first three-dimensional images of the complex hyaloid vascular structure.
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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 | 0.001 | 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 | 0.001 |
| 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".