Interaction between a Broad-spectrum Antibiotic and Silver Nanoparticles in a Human Gut Ecosystem
Bibliographic record
Abstract
The antimicrobial properties of engineered silver nanoparticles (AgNPs) have led to their wide use in diverse consumer products.Ampicillin too, acts as a broad-spectrum antibiotic and thus is prescribed for the treatment of many common infections, but with the problematic emergence of ampicillin-resistant bacteria.As a consequence, there has been some interest in the combination of these two distinct chemistries prompted by the clinical challenge of resistance.Prior to trials of combination therapy, however, it is important to understand the impact on human microbiomes.Here we investigated the effect of ampicillin and AgNPs, both individually and in a combined therapy on a human intestinal ecosystem known as a defined experimental community (DEC-60).The DEC-60 consortia was co-treated with a concentration of AgNPs (50 mg/L) known to have a minimal impact, and a broad range of the antibiotic up to the clinical dose (6 mg/L).The addition of AgNPs to sub-clinical doses of ampicillin (0.06 and 0.6 mg/L) had a significantly impact (p<0.001-0.05) on gas production (%CO 2 and %N 2 ) and changed the composition of fatty acid methyl ester signatures so that they were distinct from each individual antimicrobial, as well as un-treated control cultures.DNA sequencing, supported by multidimensional scaling analysis, confirmed the community shift and showed distinct phylogenetic distributions at different concentrations of ampicillin, depending on the presence of AgNPs.Together, these results suggest that the antibacterial efficacy of sub-clinical doses of ampicillin was increased by AgNPs, possibly due to the synergistic effect of damage to the bacterial cell walls.Not only does this analysis provide insight into AgNP toxicity, it offers some promise that combined antimicrobial therapies may have value in a clinical setting.
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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) | 0.001 | 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 | 0.001 |
| 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".