In vivo microCT-based time-lapse morphometry reveals anatomical site-specific differences in bone (re)modeling serving as baseline parameters to detect early pathological events
Bibliographic record
Abstract
The bone structure is very dynamic and continuously adapts its geometry to external stimuli by modeling and remodeling the mineralized tissue. In vivo microCT-based time-lapse morphometry is a powerful tool to study the temporal and spatial dynamics of bone (re)modeling. Here an advancement in the methodology to detect and quantify site-specific differences in bone (re)modeling of 12-week-old BALB/c nude mice is presented. We describe our method of quantifying new bone surface interface readouts and how these are influenced by bone curvature. This method is then used to compare bone surface (re)modeling in mice across different anatomical regions to demonstrate variations in the rate of change and spatial gradients thereof. Significant differences in bone (re)modeling baseline parameters between the metaphyseal and epiphyseal, as well as cortical and trabecular bone of the distal femur and proximal tibia are shown. These results are validated using conventional static in vivo microCT analysis. Finally, the insights from these new baseline values of physiological bone (re)modeling were used to evaluate pathological bone (re)modeling in a pilot breast cancer bone metastasis model. The method shows the potential to be suitable to detect early pathological events and track their spatio-temporal development in both cortical and trabecular bone. This advancement in (re)modeling surface analysis and defined baseline parameters according to distinct anatomical regions will be valuable to others investigating various disease models with site-distinct local alterations in bone (re)modeling.
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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".