Duplex PCR Methods for the Molecular Detection of <i>Escherichia fergusonii</i> Isolates from Broiler Chickens
Bibliographic record
Abstract
Escherichia fergusonii is an emerging pathogen that has been isolated from a wide range of infections in animals and humans. Primers targeting specific genes, including yliE (encoding a conserved hypothetical protein of the cellulose synthase and regulator of cellulose synthase island), EFER_1569 (encoding a hypothetical protein, putative transcriptional activator for multiple antibiotic resistance), and EFER_3126 (encoding a putative triphosphoribosyl-dephospho-coenzyme A [CoA]), were designed for the detection of E. fergusonii by conventional and real-time PCR methods. Primers were screened by in silico PCR against 489 bacterial genomic sequences and by both PCR methods on 55 reference and field strains. Both methods were specific and sensitive for E. fergusonii, showing amplification only for this bacterium. Conventional PCR required a minimum bacterial concentration of approximately 10(2) CFU/ml, while real-time PCR required a minimum of 0.3 pg of DNA for consistent detection. Standard curves showed an efficiency of 98.5%, with an R(2) value of 0.99 for the real-time PCR assay. Cecal and cloacal contents from 580 chickens were sampled from broiler farms located in the Fraser Valley (British Columbia, Canada). Presumptive E. fergusonii isolates were recovered by enrichment and plating on differential and selective media. Of 301 total presumptive isolates, 140 (46.5%) were identified as E. fergusonii by biochemical profiling with the API 20E system and 268 (89.0%) using PCR methods. E. fergusonii detection directly from cecal and cloacal samples without preenrichment was achieved with both PCR methods. Hence, the PCR methods developed in this work significantly improve the detection of E. fergusonii.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".