Delineating the origins of the multidrug-resistant pathogens in ornamental fish farms by multilocus sequence typing and identification of a novel multidrug-resistant plasmid
Bibliographic record
Abstract
To evaluate the overuse of antibiotics and to identify the origin of pathogens in the ornamental fish industry, we conducted a field investigation of three representative fish farms in Liaoning province, China. Drug-resistant pathogens in the fishponds and groundwater were isolated and subtyped by multilocus sequence typing (MLST). In total, 33 pathogenic strains, including Aeromonas veronii and five other pathogens, were isolated from diseased fish and from groundwater. MLST revealed that A. veronii obtained from diseased fish in three fish farms can be subtyped into four sequence types, which were also identified in the corresponding groundwater. All of the isolates obtained from diseased fish showed resistance to at least four antibiotics. Notably, Citrobacter freundii JY-17 exhibited resistance to the majority of the antibiotics and was a carrier of a megaplasmid with 15 drug resistance genes. PCR assays targeting β-lactam, kanamycin, macrolide, phenicol, sulfonamide, and trimethoprim resistance genes in the pathogens from the diseased fish and groundwater were also conducted. The results revealed strong correlations between antibiotic treatment and increased antimicrobial resistance in fish pathogens. The results suggested that groundwater is the origin of the pathogens in ornamental fish. Antibiotic treatment of ornamental fish promoted the emergence of resistant pathogens.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".