Simple Zinc Metallic Particle Doping Transforms Ceramic Bone Cement Therapeutic Performance
Bibliographic record
Abstract
Abstract Ceramic bone cements are widely utilized for bone defect repair, but their therapeutic effects remain unsatisfying due to their slow degradation, limited bioactivity, and lack of antibacterial properties. This study demonstrates a simple yet effective strategy to transform their performance by doping zinc (Zn), in the form of metal particles, into the bone cement matrix. Zn particle doping endows the cement with hierarchical porosity, sustained Zn 2+ release, and reactive oxygen species generation. The Zn particle‐doped bone cement exhibits potent antibacterial activity against methicillin‐resistant Staphylococcus aureus , with mechanistic insights revealed through comparative in vitro and in vivo studies. In a critical‐sized bone defect model, Zn‐doped cement demonstrates superior bioresorption, tissue infiltration, and osteogenic capacity compared to pure cement. Among the tested formulations, cements containing 5–10 wt.% Zn particles achieved the most favorable balance of antibacterial efficacy, degradation, and bone regeneration, thereby representing the most promising candidates for clinical translation. In addition, the pivotal role of the SMAD3 signaling pathway in Zn 2+ ‐mediated cell migration and osteogenesis is identified. This study not only delivers a clinically promising ceramic bone cement but also pioneers a versatile and scalable strategy for transforming bone cement properties through Zn biodegradable metal particle doping.
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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".