Silver nanoparticles present high intracellular and extracellular killing against Staphylococcus aureus
Bibliographic record
Abstract
OBJECTIVES: Bone and joint infections caused by Staphylococcus aureus are becoming increasingly difficult to treat due to rising antibiotic resistance, resilient biofilms and intracellular survival of S. aureus. It has been challenging to identify and develop antimicrobial agents that can be used to kill extracellular and intracellular bacteria while having limited toxicity towards host cells. In addressing this challenge, this study investigates the antimicrobial efficacy and toxicity of silver nanoparticles (AgNPs). METHODS: Intracellular bacteria were generated using a co-culture model of human osteoblast cells and S. aureus. Extracellular and intracellular S. aureus were treated with AgNPs, antibiotics and their combinations, and numbers of colonies were quantified. Toxicity of AgNPs against human osteoblast cells was determined by quantifying the number of viable cells after treatment. RESULTS: AgNPs demonstrated excellent antimicrobial activity against extracellular S. aureus with a 100% killing efficacy at concentrations as low as 56 μM, along with a high intracellular killing efficacy of 76% at 371 μM. AgNPs were non-toxic or slightly toxic towards human osteoblasts at the concentrations studied (up to 927 μM). Moreover, smaller-sized (40 nm) AgNPs were more efficacious in killing bacteria compared with their larger-sized (100 nm) counterparts and synergistic antimicrobial effects against extracellular bacteria were observed when AgNPs were combined with gentamicin. CONCLUSIONS: AgNPs and their combination with antibiotics have demonstrated high extracellular and intracellular bacterial killing and presented unique aspects for potential clinical applications, especially for chronic and recurrent infections where intracellular bacteria may be the cause.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".