Detection of the Escherichia coli pathogenic gene<i>eae</i>with three real-time polymerase chain reaction methods
Bibliographic record
Abstract
Several real-time polymerase chain reaction (PCR) methods are currently available to rapidly detect the presence of a specific DNA sequence. When used for detection of pathogenic organisms, the turnaround time for PCR-based methods is much lower than for traditional culture techniques. This study compared the sensitivity of three real-time PCR methods when detecting the Escherichia coli pathogenic gene eae to determine which method is most effective in identifying very low levels of the organism. The three methods were used to detect the eae gene over a range of DNA concentrations. The differences in sensitivity were statistically significant (p<0.05), and SYBR Green I PCR was found to have the lowest detection limit of the three; LUX primers had the highest detection limit. Therefore, using a defined DNA concentration for detecting the eae gene, SYBR Green I is the best alternative.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".