Enhancing Antimicrobial Efficacy and Synergistic Effects of Nano-Silica-Based Combinations With Doxycycline, Metronidazole, and Ciprofloxacin Against Enterococcus faecalis Biofilms
Bibliographic record
Abstract
Background: Enterococcus faecalis biofilm formation within root canals poses a challenging problem in endodontics, often leading to treatment failure. To combat this issue, nanotechnology offers a promising avenue for enhancing antimicrobial efficacy. This study explores the potential synergistic effects of combining nanoscale silica particles with conventional antibiotics, including doxycycline, metronidazole, and ciprofloxacin, against E. faecalis biofilms. The unique characteristics of silica nanoparticles, such as their increased reactivity and ability to be functionalized with other compounds, make them ideal candidates for augmenting antibiotic efficacy. This research investigates the antimicrobial properties of these silica-based combinations and their potential to eliminate or inhibit E. faecalis biofilms more effectively than conventional treatments. Methodology: The methods involved the preparation of nanostructured silica particles and their combination with doxycycline, Flagyl, and ciprofloxacin at subinhibitory concentrations. These combinations were then tested against E. faecalis biofilms using the agar well diffusion technique. Results: Preliminary results suggested that the synergistic interactions between silica nanoparticles and antibiotics can significantly enhance antimicrobial efficacy. The combined treatment exhibited superior inhibitory effects on E. faecalis compared to antibiotics or silica nanoparticles alone (P < 0.05). Conclusions: This study sheds light on the potential of nanoscale silica-based combinations to address the challenges posed by E. faecalis biofilms in endodontics. Understanding the mechanisms of synergy between nanoparticles and antibiotics can pave the way for the development of more effective and targeted strategies for root canal disinfection, ultimately improving the success rates of endodontic treatments.
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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".