Engineering bone repair with K21-Infused brushite cement: In-vitro and in-vivo insights
Bibliographic record
Abstract
BACKGROUND: Calcium phosphate biomaterials are widely used for bone regeneration, yet antibacterial and mechanical limitations persist. Brushite cements offer biodegradability and osteoconductivity but lack antimicrobial effects. Quaternary ammonium compounds (QACs), such as K21, provide potent antibacterial activity and tissue healing potential. This study developed and evaluated a novel K21-doped brushite cement with enhanced antibacterial and anti-inflammatory properties. METHODS: Brushite granules were synthesized from β-tricalcium phosphate and monocalcium phosphate monohydrate, followed by K21 drug loading (0.5 % and 1 % w/v). Material characterization used X-ray diffraction, Raman spectroscopy, and porosity analysis. In vitro tests included K21 release, antimicrobial efficacy against dual-species biofilms (P. aeruginosa, S. aureus), cytocompatibility with human gingival fibroblasts, and anti-inflammatory activity via protein denaturation assays. In vivo, mandibular critical-size defects were created in 24 rabbits (n = 8/group) and treated with control, 0.5 %, or 1 % K21-doped brushite cement. Bone regeneration and inflammatory response were assessed histologically at six weeks. RESULTS: K21 incorporation showed pH-sensitive release with higher drug content at increased concentrations. Both 0.5 % and 1 % K21 cements significantly reduced microbial viability, with 1 % showing stronger bacteriostatic effects. All groups exhibited good cytocompatibility. In vivo, 0.5 % K21-doped cement achieved the greatest new bone formation (∼95 %) and reduced inflammation. In contrast, 1 % formulations induced increased inflammation despite antibacterial efficacy. CONCLUSIONS: K21-doped brushite cements provide antibacterial, anti-inflammatory, and bone regenerative benefits. The 0.5 % formulation demonstrated optimal balance of safety and efficacy, making it a promising candidate for clinical use in craniofacial applications. CLINICAL SIGNIFICANCE: Bone graft failures are often linked to infection and inflammation. Incorporating K21 into brushite cement offers a dual-action material that not only supports bone regeneration but also reduces microbial risk and inflammatory complications, potentially improving outcomes in dental and maxillofacial defect repair.
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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 | 0.001 | 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".