The utility and application of real-time PCR and FISH in the detection of single-copy gene targets in<i>Escherichia coli</i>O157:H7 and<i>Salmonella</i>Typhimurium
Bibliographic record
Abstract
The ultimate specificity in molecular-based assays for pathogen detection relies on the design of the primers and probes. Their ability to hybridize to DNA sequences found only in pathogens can be realized by designing primers and probes that are complementary to pathogen-specific virulence genes. This study evaluates the detection and enumeration strengths of real-time PCR (qPCR) and fluorescent in situ hybridization (FISH) for selected waterborne pathogens and their ultimate applicability within a monitoring framework. Detection limits calculated in the qPCR assay were 150 tir (intimin protein receptor) gene copies for Escherichia coli O157:H7 and 2 x 103 invA (inner membrane invasive protein) gene copies for Salmonella enterica serovar Typhimurium. Detection limits were, however, at least 100-fold less sensitive in wastewater extracts, partly because of the inhibitory effect of the wastewater itself. Fluorescent signals from hybridized whole target cells were below the detection limit of the FISH assay. While this research demonstrates the potential detection strength of qPCR, it highlights the need for strong dependable primer and probe sets among PCR and FISH methodologies as well as the need for further signal amplification with DNA-targeted FISH for single-copy gene targets within 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".