Antibiotic resistance mechanism and diagnosis of common foodborne pathogens based on genotypic and phenotypic biomarkers
Bibliographic record
Abstract
The emergence of antibiotic-resistant bacteria due to the overuse or inappropriate use of antibiotics has become a significant public health concern. The agri-food chain, which serves as a vital link between the environment, food, and human, contributes to the large-scale dissemination of antibiotic resistance, posing a concern to both food safety and human health. Identification and evaluation of antibiotic resistance of foodborne bacteria is a crucial priority to avoid antibiotic abuse and ensure food safety. However, the conventional approach for detecting antibiotic resistance heavily relies on culture-based methods, which are laborious and time-consuming. Therefore, there is an urgent need to develop accurate and rapid tools for diagnosing antibiotic resistance in foodborne pathogens. This review aims to provide an overview of the mechanisms of antibiotic resistance at both phenotypic and genetic levels, with a focus on identifying potential biomarkers for diagnosing antibiotic resistance in foodborne pathogens. Furthermore, an overview of advances in the strategies based on the potential biomarkers (antibiotic resistance genes, antibiotic resistance-associated mutations, antibiotic resistance phenotypes) for antibiotic resistance analysis of foodborne pathogens is systematically exhibited. This work aims to provide guidance for the advancement of efficient and accurate diagnostic techniques for antibiotic resistance analysis in the food industry.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".