The incorporation of fluoride and strontium in hydroxyapatite affects the composition, structure, and mechanical properties of human cortical bone
Bibliographic record
Abstract
Strontium ranelate and fluoride salts are therapeutic options to reduce fracture risk in osteoporosis. Incorporation of these elements in the physiological hydroxyapatite matrix of bone is accompanied by changes in bone remodeling, composition, and structure. However, a direct comparison of the effectiveness of strontium and fluoride treatment in human cortical bone with a focus on the resulting mechanical properties remains to be established. Study groups are composed of undecalcified specimens from healthy controls, treatment-naïve osteoporosis cases, and strontium ranelate or fluoride-treated osteoporosis cases. Concentrations of both elements were determined using instrumental neutron activation analysis (INAA). Backscattered electron imaging was carried out to investigate the calcium content and the cortical microstructure. In comparison to osteoporotic patients, fluoride and strontium-treated patients have a lower cortical porosity indicating an improvement in bone microstructure. Mechanical properties were assessed via reference point indentation as a measure of bone's resistance to deformation. The strontium-incorporation led to significantly lower total indentation distance values compared to osteoporotic cases; controls have the highest resistance to indentation. In conclusion, osteoporosis treatment with strontium and fluoride showed positive effects on the microstructure and the mechanical characteristics of bone in comparison to treatment-naïve osteoporotic bone. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 433-442, 2017.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".