High strength resorbable brushite bone cement with controlled drug releasing capabilities
Bibliographic record
Abstract
Brushite cements differ from apatite-forming compositions by consuming a lot of water in their setting reaction whereas apatite-forming cements consume little or no water at all. Only such cement systems that consume water during setting can theoretically produce near-zero porosity ceramics. This study aimed to produce such a brushite ceramic and investigated whether near elimination of porosity would prevent a burst release profile of incorporated antibiotics that is common to prior calcium phosphate cement delivery matrices. Through adjustment of the powder technological properties of the powder reactants, that is particle size and particle size distribution, and by adjusting citric acid concentration of the liquid phase to 800 mM, a relative porosity of as low as 11% of the brushite cement matrix could be achieved (a 60% reduction compared to previous studies), resulting in a wet unprecompacted compressive strength of 52 MPa (representing a more than 100% increase to previously reported results) with a workable setting time of 4.5 min of the cement paste. Up to 2 wt.% of vancomycin and ciprofloxacin could be incorporated into the cement system without loss of wet compressive strength. It was found that drug release rates could be controlled by the adjustable relative porosity of the cement system and burst release could be minimized and an almost linear release achieved, but the solubility of the antibiotic (vancomycin > ciprofloxacin) appeared also to be a crucial factor.
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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 | 0.001 |
| 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".