Antimicrobial activity of PMMA enriched with nano-clay loaded with metronidazole and chlorhexidine
Bibliographic record
Abstract
Poly(methyl methacrylate) (PMMA) materials are highly susceptible to microbial colonization, predisposing patients to oral infections. To address this concern, we loaded PMMA samples with montmorillonite clay (MMT), a crystalline nanoparticle, in combination with chlorhexidine (CHX) or metronidazole (MET) targeting improved antimicrobial action. PMMA samples were prepared with or without MMT loaded with either CHX or MET, establishing the following groups: control (acrylic resin without the addition of nanoparticles), MMT/CHX (acrylic resin with 5% by weight of MMT loaded with CHX), and MMT/MET (acrylic resin with 5% by weight of MMT loaded with MET). Mechanical properties such flexural strength, flexural modulus, and Knoop hardness were evaluated using a universal testing machine. Antimicrobial efficacy was assessed via agar diffusion tests against Enterococcus faecalis and Porphyromonas gingivalis. The addition of MMT loaded with CHX did not affect the flexural strength and flexural modulus of PMMA compared to the control group (p > 0.05). However, MMT/MET reduced all mechanical properties of PMMA (p < 0.05). Both loaded-PMMA materials demonstrated antibacterial activity against E. faecalis but not against P. gingivalis. In conclusion, the incorporation of MMT/CHX into acrylic resin appears to be the most promising approach to combat microbial colonization while preserving PMMA mechanical properties. Future research should focus on optimizing material characteristics to enhance antimicrobial properties, paving the way for clinical applicability.
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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".