High Prevalence of Vancomycin Non-Susceptible and Multi-Drug Resistant Enterococci in Farmed Animals and Fresh Retail Meats in Bangladesh
Bibliographic record
Abstract
Abstract The emergence of antimicrobial resistant Enterococcus spp., a leading cause of untreatable nosocomial infection, in food animals and dissemination to humans is a public health concern. The study was conducted to determine the prevalence and antimicrobial resistance, and virulence characteristics of Enterococcus faecalis and Enterococcus faecium in food animals and meats in Bangladesh. Enterococcus spp., were confirmed using sodA gene specific PCR, and antimicrobial resistance and virulence properties were characterized by PCR. Enterococcus spp. were recovered from 57% of the collected samples (n=201/352). Farm samples yielded significantly higher (p≤0.05) prevalence (62%) compared to retail meat samples (41%) and E. faecalis (52%) was most frequently isolated species. High proportions of isolates exhibited resistance to tetracycline (74%), erythromycin (65%) and ciprofloxacin (34%). Fifty-one isolates were vancomycin non-susceptible enterococci (VNSE), of which forty-seven were MDR and 20 were linezolid resistant, a last line drug for VNSE. Virulence factors such as gelatinase (gelE), aggregation factor (asa1) and sex pheromone (cpd) were detected along with vancomycin resistance gene (vanA, vanB and vanC2/C3) in VNSE isolates. The high prevalence of MDR enterococci in food animals and retail meats may lead to infection in consumers with concomitant reduced therapeutic options available for treatment.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".