Magnetic bead hybridization to detect enterotoxigenic<i>Escherichia coli</i>strains associated with cattle in environmental water sources
Bibliographic record
Abstract
A magnetic capture hybridization - polymerase chain reaction (MCH-PCR) method was used to increase the detection sensitivity of the enterotoxin gene LTIIa, used as a biomarker for waste in environmental samples. The samples were collected from cow lagoons of different farms and from environmental waters. Total DNA was extracted from colonies grown on mTEC medium or directly from environmental samples. The cow-specific Escherichia coli LTIIa gene was used as a DNA marker. A LTIIa-specific oligonucleotide probe was designed to capture the LTIIa marker during the MCH, followed by PCR. Varying levels of humic acid were added to the DNA extracts to evaluate the sensitivity and effectiveness of MCH-PCR. The minimal detection limit of MCH-PCR for the LTIIa gene was 2.5 ag/muL DNA. In the presence of humic acid, MCH-PCR was able to increase the detection sensitivity 10 000-fold over that of conventional PCR. The MCH-PCR could also detect one cell with the LTIIa DNA marker in a 1-L seeded environmental water sample. Results in this study indicate that MCH-PCR is more sensitive than nested PCR in testing environmental samples.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".