Comparison of five rep-PCR genomic fingerprinting methods for differentiation ofâ fecal<i>Escherichia coli</i>from humans, poultry and wild birds
Bibliographic record
Abstract
The development of a methodology to identify the origin of fecal pollution is important both for assessing the degree of risk posed to public health and for developing strategies to mitigate the environmental loading of pathogens associated with waterborne disease transmission. Five rep-PCR genomic fingerprinting methods, such as rep-PCR, enterobacterial repetitive intergenic consensus (ERIC)-PCR, ERIC2-PCR, BOX-PCR and (GTG)(5)-PCR, were assessed for their potential in differentiation of 232 fecal Escherichia coli isolates obtained from humans, poultry (chicken, duck and turkey) and wild birds (Canada goose and gull). Based on the results of cluster analysis and discriminant function analysis, (GTG)(5)-PCR was found to be the most suitable method for molecular typing of fecal E. coli, followed by BOX-PCR, REP-PCR, ERIC-PCR and ERIC2-PCR. A discriminant function analysis of (GTG)(5)-PCR fingerprints showed that 94.1%, 79.8%, 80.5%, 74.4%, 86.7% and 88.6% of turkey, chicken, duck, Canada goose, gull and human E. coli isolates were classified into the correct host group, respectively. Subsequently, (GTG)(5)-PCR was tested for its ability to track the origin of 113 environmental E. coli isolated from natural pond water. In conclusion, the (GTG)(5)-PCR genomic fingerprinting method can be considered as a promising genotypic tool for epidemiological surveillance of fecal pollution in aquatic environments.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".