In Ovo Monitoring of Smooth Muscle Fiber Development in the Chick Embryo: Diffusion Tensor Imaging with Histologic Correlation
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging is a noninvasive method of evaluating embryonic development. Magnetic resonance diffusion tensor imaging, which is based on the measuring the directional diffusivity of water molecules, is an established method of evaluating tissue structure. Prolonged imaging times have precluded the use of embryonic diffusion tensor imaging due to motion artifact. Using temperature-based motion suppression, we aimed to investigate whether diffusion tensor imaging can be used to monitor embryonic smooth muscle development in ovo, and to determine the correlation between histologically-derived muscle fiber fraction, day of incubation and diffusion tensor imaging fractional anisotropy values and length of tracked fibers. METHODOLOGY/PRINCIPAL FINDINGS: From a set of 82 normally developing fertile chicken eggs, 5 eggs were randomly chosen each day from incubation days 5 to 18 and cooled using a dual-cooling technique prior to and during magnetic resonance imaging at 3.0 Tesla. Smooth muscle fibers of the gizzard were tracked using region of interests placed over the gizzard. Following imaging, the egg was cracked and the embryo was fixated and sectioned, and a micrograph most closely corresponding to the acquired magnetic resonance image was made. Smooth muscle fiber fraction was determined using an automated computer algorithm. CONCLUSIONS/SIGNIFICANCE: We show that diffusion tensor images of smooth muscle within the embryonic gizzard can be acquired in ovo from incubation day 11 through hatching. Length of tracked fibers and day of incubation were found to have statistical significance (p<0.05) by multiple linear regression correlation with histologic specimens of sacrificed embryos from day 11 of incubation through hatching. The morphologic pattern of development in our histologic specimens corresponds to the development of embryonic gizzard as reported in the literature. These results suggest that diffusion tensor imaging can provide a noninvasive method of evaluating in ovo development of smooth muscle tissue.
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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 | 0.001 |
| 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 | 0.000 |
| 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".