Passive and Active <i>In Vitro</i> Resorption of Calcium and Magnesium Phosphate Cements by Osteoclastic Cells
Bibliographic record
Abstract
Biocements are clinically applied materials for bone replacement in non-load-bearing defects. Depending on their final composition, cements can be either resorbed or remain stable at the implantation site. Degradation can occur by two different mechanisms, by simple dissolution (passive) or after osteoclastic bone remodeling (active). This study investigated both the passive and active in vitro resorption behavior of brushite (CaHPO₄ · 2H₂O), monetite (CaHPO₄), calcium-deficient hydroxyapatite (CDHA; Ca₉(PO₄)₅HPO₄OH), and struvite (MgNH₄PO₄ · 6H₂O) cements. Passive resorption was measured by incubating the cement samples in a cell culture medium, whereas active resorption was determined during the surface culture of multinuclear osteoclastic cells derived from RAW 264.7 macrophages. Osteoclast formation was confirmed by showing tartrate resistant acid phosphatase (TRAP) activity on CDHA, brushite, and monetite surfaces, as well as by measuring calcitonin receptor (CT-R) expression as an osteoclast-specific protein by Western blot analysis for struvite ceramics. An absence of passive degradation and only marginally active degradation of <0.01% were found for CDHA matrices. For the secondary calcium phosphates brushite and monetite, active degradation was predominant with a cumulative Ca²+ release of 2.02 (1.20) μmol during 13 days, whereas passive degradation released only 0.788 (0.04) μmol calcium ions into the medium. The struvite cement was the most degradable with a passive (active) release of 9.26 (2.92) Mg²+ ions and a total weight loss of 4.7% over 13 days of the study.
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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 | 0.000 |
| 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".