Whole Transcriptome Sequencing Analysis of the Synergistic Antimicrobial Effect of Metal Oxide Nanoparticles and Ajoene on Campylobacter jejuni
Bibliographic record
Abstract
Two metal oxide (i.e., Al2O3 and TiO2) nanoparticles and ajoene, a garlic-derived organosulfur compound, were identified to be effective antimicrobials against Campylobacter jejuni, a leading cause of human gastrointestinal diseases worldwide. A significant synergistic antimicrobial effect was observed using ajoene and Al2O3/TiO2 nanoparticles in a combined manner to cause at least 8 Log10 CFU/mL reduction of C. jejuni cells. Whole transcriptome sequencing (RNA-seq) and confocal micro-Raman spectroscopic analyses revealed the antimicrobial mechanism and identified the roles of ajoene and metal oxide nanoparticles in the synergistic treatment. Ajoene and metal oxide nanoparticles mediated a two-phase antimicrobial mechanism. Ajoene served as the inducing factor at the first phase that caused injury of cell membranes and increased the susceptibility of C. jejuni to stress. Metal oxide nanoparticles served as the active factor at the second phase that targeted sensitive cells and physically disrupted cell structure. This synergistic antimicrobial treatment demonstrates a potential to reduce the prevalence of C. jejuni and other pathogens on food contact surfaces and in the food chain.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".