The <i>Campylobacter fetus</i> S layer provides resistance to photoactivated zinc oxide nanoparticles
Bibliographic record
Abstract
The antimicrobial activity of metal-based compounds, including metal oxides, has resulted in numerous agricultural, industrial, and medical applications. Zinc oxide nanoparticles are toxic to Gram-positive and Gram-negative bacteria as well as to some fungi. In this study we assess the sensitivity of Campylobacter fetus, a Gram-negative bacterial pathogen of humans and animals, to ZnO nanoparticles and determine whether the S layer protects C. fetus from the antibacterial action of these nanoparticles. Broth and agar dilution assays revealed that ZnO nanoparticles at 100 μg/mL were bacteriocidal for C. fetus. Resazurin reduction assays confirmed the absence of metabolic activity, indicating that C. fetus cells had not entered into a viable but nonculturable state. Photoactivation of ZnO nanoparticles greatly enhanced their antibacterial activity, as evidenced by minimum bacteriocidal concentration (MBC) values decreasing to 16–62.5 μg/mL as a function of strain. MBC assays completed in the presence and absence of catalase revealed that H2O2, a product of ZnO nanoparticle photoactivation, contributed to C. fetus but not to C. jejuni cell death. S-layer-expressing C. fetus strains were more resistant to H2O2-mediated cell killing than were isogenic S-layer-deficient strains. These data indicate that C. fetus is sensitive to the antibacterial activity of ZnO nanoparticles and that the C. fetus S layer imparts protection against photoactivated nanoparticles.
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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".