3‐D localization of non‐radioactive strontium in osteoarthritic bone: Role in the dynamic labeling of bone pathological changes
Bibliographic record
Abstract
The study objective was to visualize regions of bone that undergo pathological mineralization and/or remodeling during pathogenesis of osteoarthritis, by employing non-radioactive strontium as a dynamic tracer of bone turnover. Post traumatic osteoarthritis was surgically induced in skeletally mature rats, followed by in vivo micro-CT imaging for 12 weeks to assess bone micro-structural changes. Rats either received strontium ranelate daily for the entire course of study or only last 10 days before euthanization. Distribution of strontium in bone was assessed in two and three dimensions, using electron probe micro-analysis (EPMA) and synchrotron dual energy K-edge subtraction micro-CT (SRμCT), respectively. Considerable early formation of osteophytes around the collateral ligament attachments and margins of articulating surfaces were observed, followed by subchondral sclerosis at the later stages. Accordingly, strontium was heavily incorporated by mineralizing osteophytes at 4, 8, and 12 weeks post-surgery, whereas subchondral bone only incorporated strontium between weeks 8-12.This study showed low dose stable strontium can effectively serve as a dynamic tracer of bone turnover to study pathological bone micro-structural changes, at resolution higher than nuclear medicine. Co-administration of strontium during therapeutic drug intervention may show enormous utility in assessing the efficacy of those compounds upon adaptive bone physiology.
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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 | 0.001 | 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".