Three-dimensional labeling of newly formed bone using synchrotron radiation barium K-edge subtraction imaging
Bibliographic record
Abstract
Bone is a dynamic tissue which exhibits complex patterns of growth as well as continuous internal turnover (i.e. remodeling). Tracking such changes can be challenging and thus a high resolution imaging-based tracer would provide a powerful new perspective on bone tissue dynamics. This is, particularly so if such a tracer can be detected in 3D. Previously, strontium has been demonstrated to be an effective tracer which can be detected by synchrotron-based dual energy K-edge subtraction (KES) imaging in either 2D or 3D. The use of strontium is, however, limited to very small sample thicknesses due to its low K-edge energy (16.105 keV) and thus is not suitable for in vivo application. Here we establish proof-of-principle for the use of barium as an alternative tracer with a higher K-edge energy (37.441 keV), albeit for ex vivo imaging at the moment, which enables application in larger specimens and has the potential to be developed for in vivo imaging of preclinical animal models. New bone formation within growing rats in 2D and 3D was demonstrated at the Biomedical Imaging and Therapy bending magnet (BMIT-BM) beamline of the Canadian Light Source synchrotron. Comparative x-ray fluorescence imaging confirmed those patterns of uptake detected by KES. This initial work provides a platform for the further development of this tracer and its exploration of applications for in vivo development.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".