Technique: Imaging Earliest Tooth Development in 3D Using a Silver‐Based Tissue Contrast Agent
Bibliographic record
Abstract
Looking in microscopic detail at the 3D organization of initiating teeth within the embryonic jaw has long-proved technologically challenging because of the radio-translucency of these tiny un-mineralized oral tissues. Yet 3D image data showing changes in the physical relationships among developing tooth and jaw tissues are vital to understand the coordinated morphogenesis of vertebrate teeth and jaws as an animal grows and as species evolve. Here, we present a new synchrotron-based scanning solution to image odontogenesis in 3D and in histological detail using a silver-based contrast agent. We stained fixed, intact wild-type mice aged embryonic (E) day 10 to birth with 1% Protargol-S at 37°C for 12-32 hr. Specimens were scanned at 4-10 µm pixel size at 28 keV, just above the silver K-edge, using micro-computed tomography (µCT) at the Canadian Light Source synchrotron. Synchrotron µCT scans of silver-stained embryos showed even the earliest visible stages of tooth initiation, as well as many other tissue types and structures, in histological detail. Silver stain penetration was optimal for imaging structures in intact embryos E15 and younger. This silver stain method offers a powerful yet straightforward approach to visualize at high-resolution and in 3D the earliest stages of odontogenesis in situ, and demonstrates the important of studying the tooth organ in all three planes of view.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".