Biofilm Formation and Antimicrobial Susceptibility of E. coli Associated With Colibacillosis Outbreaks in Broiler Chickens From Saskatchewan
Bibliographic record
Abstract
The global poultry industry has grown to the extent that the number of chickens now well exceeds the number of humans on Earth. Escherichia coli infections in poultry cause significant morbidity and economic losses for producers each year. We obtained 94 E. coli isolates from 12 colibacillosis outbreaks on Saskatchewan farms and screened them for antimicrobial resistance and biofilm formation. Fifty-six isolates were from broilers with confirmed colibacillosis, and 38 isolates were from healthy broilers in the same flocks (cecal E. coli). Resistance to penicillins, tetracyclines, and aminoglycosides was common in isolates from all 12 outbreaks, while cephalosporin resistance varied by outbreak. Most E. coli were able to form biofilms in at least one of three growth media (1/2 TSB, M63, and BHI broth). There was an overall trend that disease-causing E. coli had more antibiotic resistance and were more likely to form biofilms in nutrient-rich media (BHI) as compared to cecal strains. However, on an individual strain basis, there was no correlation between antimicrobial resistance and biofilm formation. The 21 strongest biofilm forming strains consisted of both disease-causing and cecal isolates that were either drug resistant or susceptible. Draft whole genome sequencing indicated that many known antimicrobial resistance genes were present on plasmids, with disease-causing E. coli having more plasmids on average than their cecal counterparts. We tested four common disinfectants for their ability to kill 12 of the best biofilm forming strains. All disinfectants killed single cells effectively, but biofilm cells were more resistant, although the difference was less pronounced for the disinfectants that have multiple modes of action. Our results indicate that there is significant diversity and complexity in E. coli poultry isolates, with different lifestyle pressures affecting disease-causing and cecal isolates.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".