Non-invasive imaging of zebrafish with spinal deformities using optical coherence tomography: a preliminary study
Bibliographic record
Abstract
A zebrafish model has recently been introduced to study various genetic mutations that could lead to spinal deformities such as scoliosis. However, current imaging techniques make it difficult to perform longitudinal studies of this condition in zebrafish, especially in the early stages of development. The goal of this project is to determine whether optical coherence tomography (OCT) is a viable non-invasive method to image zebrafish exhibiting spinal deformities. Images of both live and fixed malformed zebrafish (5 to 21 days postfertilization) as well as wild-type fish (5 to 29 days postfertilization) were acquired non-invasively using a commercial SD-OCT system, with a laser source centered at 930nm (λ=100nm), permitting axial and lateral resolutions of 7 and 8μm respectively. Using two-dimensional images and three-dimensional reconstructions, it was possible to identify the malformed notochord as well as deformities in other major organs at different stages of formation. Visualization of the notochord was facilitated with the development of a segmentation algorithm. OCT images were compared to HE histological sections and images obtained by calcein staining. Because of the possibility of performing longitudinal studies on a same fish and reducing image processing time as compared with staining techniques and histology, the use of OCT could facilitate phenotypic characterization in studying genetic factors leading to spinal deformities in zebrafish and could eventually contribute to the identification of the genetic causes of spinal deformities such as scoliosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".