Antimicrobial resistance patterns and biofilms resurgence ability of Escherichia coli associated with commercial layer chicken farms in Sri Lanka
Bibliographic record
Abstract
This study aims to understand antimicrobial resistance (AMR) associated with planktonic and biofilm-forming bacteria in Sri Lankan chicken industry. Planktonic or biofilm-forming Escherichia coli were isolated from different sources in layer chicken farms and their AMR profiles were determined. Minimum inhibitory concentrations (MICs) of selected antibiotics were quantified against planktonic E. coli isolated from water. Minimum biofilm inhibitory/eradication concentrations (MBIC/MBEC) of tetracycline were determined for biofilm E. coli . Faecal E. coli demonstrated highest resistance to tetracycline (64 % of isolates). Biofilm-derived planktonic E. coli showed greater resistance to antibiotics than planktonic E. coli (75 % vs 62.5 %) . The MBIC and MBEC of tetracycline in E. coli biofilm phenotype were significantly greater than MIC of planktonic phenotype of the same isolate (P < 0.05). This study highlights the importance of eliminating biofilms in chicken industry as biofilm-forming bacteria isolated from drinkers demonstrated a significantly greater AMR and can act as a source of AMR dissemination. • Drug resistant E. coli existed in chicken faeces, feeders, drinkers, and water. • Chicken faecal E. coli isolates were highly resistant to tetracycline • Chicken faecal E. coli isolates were susceptible to imipenem and gentamycin. • Biofilm E. coli exhibited greater antibiotic resistance than planktonic bacteria. • Poultry farm environment may act as reservoirs of antibiotic resistant microbes.
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 | 0.001 |
| 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 | 0.001 | 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".