Comparison of different protocols for demineralization of cortical bone
Bibliographic record
Abstract
Abstract Bone is a biological composite material consisting of two main components: collagen and mineral. Collagen is the most abundant protein in vertebrates, which makes it of high clinical and scientific interest. In this paper, we compare the composition and structure of cortical bone demineralized using several protocols: ethylene-diamine-tetraacetic acid (EDTA), formic acid (CH2O2), hydrochloric acid (HCl), and HCl/EDTA mixture. The efficiencies of these four agents were investigated by assessing the remaining mineral quantities and collagen integrity with various experimental techniques. Raman spectroscopy results show that the bone demineralized by the CH2O2agent has highest collagen quality parameter. The HCl/EDTA mixture removes the most mineral, but it affects the collagen secondary structure as amide II bands are shifted as observed by Fourier transform infrared spectroscopy. Thermogravimetric analysis reveals that HCl and EDTA are most effective in removing the mineral with bulk measurements. In summary, we conclude that HCl best demineralizes bone, leaving the well-preserved collagen structure in the shortest time. These findings guide on the best demineralization protocol to obtain high-quality collagen from bone for clinical and scientific applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".