Genotypic and Phenotypic Characterization of Antimicrobial-Resistant Escherichia coli from Farm-Raised Diarrheic Sika Deer in Northeastern China
Bibliographic record
Abstract
In China, overuse and/or abuse of antimicrobials are common in stockbreeding, which possess high risks of antimicrobial-resistant contaminations. The serogroups, major virulence genes, and antimicrobial resistant patterns of the antimicrobial-resistant Escherichia coli (E. coli) were investigated in the feces of diarrheic farm-raised sika deer from 50 farms in three Northeastern provinces of China. A total of 220 E. coli isolates were obtained and characterized. Twenty-eight O serogroups were identified from the obtained E. coli isolates with O2, O26, O128, O142 and O154 being dominant. Nearly all the isolates were resistant to at least four of the tested antimicrobials. More than 90% of the E. coli isolates carried at least one of the tested virulence genes. About 85% of the E. coli isolates carried one or more antimicrobial-resistant genes responsible for resistant phenotypes of sulfonamides, streptomycin/spectionomycin or tetracycline. The antimicrobial resistant level and pathogenic group occurrences of the obtained E. coli isolates were higher than that of livestock and wild animals reported in some developed countries. Thus, the fecal-carrying antimicrobial-resistant E. coli from the farm-raised sika deer is potentially a significant contamination source for freshwater systems and food chain, and may pose great health risks for human and animals in Northeastern China.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".