Metallic nanoparticles induced antibiotic resistance genes attenuation of leachate culturable microbiota: The combined roles of growth inhibition, ion dissolution and oxidative stress
Bibliographic record
Abstract
The dissemination and propagation of antibiotic resistance genes (ARGs) is an emerging global health concern, and the potential effects of nanomaterials on ARGs fates have drawn much attention recently. In the current study, the effects of metallic nanoparticles on ARGs occurrence of leachate culturable microbiota were investigated by four typical metal and metal oxide nanoparticles (Cu, Zn, CuO, and ZnO). The ARGs diversity was remarkably decreased during the cultivation and enrichment of leachate microbiota, and their abundances decreased for 1.4-3.2 orders of magnitude. The presence of nanoparticles facilitated the ARGs attenuation, and the magnitude of effects depended on types of nanoparticles and ARGs. Metal oxide nanoparticles caused more remarkable effects than metal nanoparticles. Mechanism analysis indicated that bacterial growth was inhibited, and the dissolved metal ions from nanoparticles partially contributed to nanoparticles decreasing ARGs. Flow cytometry experiments further confirmed that nanoparticles could enter bacterial cells, and then induce excessive reactive oxygen species (ROS) generation and increase membrane permeability. Finally, the possible mechanisms were put forward, and the structural equation models (SEM) differentiated the contribution of different factors shaping ARGs. The dissolved metal ions and growth inhibition caused by nanoparticles decreased ARGs transfer frequencies via exerting excessive metal stress and lowering population density. On the other hand, nanoparticles were incorporated into the cells, and then induced the generation of ROS, which might facilitate ARGs horizontal transfer via increasing membrane permeability, or decrease ARGs via the damage of genomic and plasmid DNA. Therefore, nanoparticles could affect ARGs fates via several ways, and combined effects finally determined the ARGs variations.
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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) | 0.001 | 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 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".