High Throughput Screening of Antimicrobial Resistance Genes in Gram-Negative Seafood Bacteria
Bibliographic record
Abstract
From a global view of antimicrobial resistance over different sectors, seafood and the marine environment are often considered as potential reservoirs of antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs); however, there are few studies and sparse results on this sector. This study aims to provide new data and insights regarding the content of resistance markers in various seafood samples and sources, and therefore the potential exposure to humans in a global One Health approach. An innovative high throughput qPCR screening was developed and validated in order to simultaneously investigate the presence of 41 ARGs and 33 MGEs including plasmid replicons, integrons, and insertion sequences in Gram-negative bacteria. Analysis of 268 seafood isolates from the bacterial microflora of cod (n = 24), shellfish (n = 66), flat fishes (n = 53), shrimp (n = 10), and horse mackerel (n = 115) show the occurrence of sul-1, ant(3″)-Ia, aph(3′)-Ia, strA, strB, dfrA1, qnrA, and blaCTX-M-9 genes in Pseudomonas spp., Providencia spp., Klebsiella spp., Proteus spp., and Shewanella spp. isolates and the presence of MGEs in all bacterial species investigated. We found that the occurrence of MGE may be associated with the seafood type and the environmental, farming, and harvest conditions. Moreover, even if MGE were detected in half of the seafood isolates investigated, association with ARG was only identified for twelve isolates. The results corroborate the hypothesis that the incidence of antimicrobial-resistant bacteria (ARB) and ARG decreases with increasing distance from potential sources of fecal contamination. This unique and original high throughput micro-array designed for the screening of ARG and MGE in Gram-negative bacteria could be easily implementable for monitoring antimicrobial resistance gene markers in diverse contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".