Characterization of integrons among Escherichia coli in a region at high incidence of ESBL-EC
Bibliographic record
Abstract
Objective : The aim of study was to investigate the distribution of the integrons in Escherichia coli (E. coli) isolates, and analyze the possible relationship between the antimicrobial resistance profiles and the integrons. Methods : The antimicrobial profiles of 376 E. coli strains were analysed by disk diffusion test. The integron genes and variable regions were detected by PCR. Some amplicons were sequenced to determine the gene cassettes style. Results : Of 376 isolates, 223 isolates (59.3%) were confirmed as ESBL-EC. Comparison to ESBL-negative E. coli, the high rates of resistance to the third and fourth generation of cephalosporins, penicillins and amikacin were found in ESBL-EC. Only class 1 was integron detected in the isolates, and the prevalence of it was 66.5%. It was commonly found in ESBL-EC (77.6%, 173/223), which was higher than that of ESBL-negative E. coli (50.3%, 77/153) (p<0.001). Six different genes cassettes were detected in this study and were classified into three groups: dfr17-aadA5, dfrA12-aadA2 and aacA4-CmlA1. Additionally, more than one gene array harboured in 13.9% isolates of ESBL-EC, while in 9.1% isolates of ESBL-negative E.coli. Conclusion : The high incidence of ESBL-EC with resistance to multiple antibiotics were detected in the isolates from Blood stream infection (BSI). More resistant gene cassettes in ESBL-EC may partially underlie the high resistance to amikacin, while no relation exists between the high incidence of ESBL-EC and classes 1~ 3 integrons in this region.
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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 | 0.001 |
| 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) | 0.001 | 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".