Evaluation and Validation of Real-Time Reverse Transcription-PCR Assay Using the LightCycler System for Detection and Quantitation of Norovirus
Bibliographic record
Abstract
We developed an assay for the detection and quantitation of norovirus with the LightCycler SYBR Green-based real-time reverse transcription-PCR (real-time LC RT-PCR) and previously published primers in the capsid and the polymerase gene. One hundred thirty-two stool specimens from the Provincial Laboratory for Public Health (Microbiology), Alberta, Canada, and the Centers for Disease Control and Prevention, Atlanta, Ga., were used to validate the new assay. The samples were collected from patients involved in outbreaks of acute gastroenteritis or children who presented with sporadic gastroenteritis. The real-time LC RT-PCR assay detected norovirus strains from three genogroup I (G-I) clusters (G-I/1, G-I/2, and G-I/3) and 10 genogroup II (G-II) clusters (G-II/1, G-II/2, G-II/3, G-II/4, G-II/6, G-II/7, G-II/10, G-II/12, G-II/15, and G-II/16). There was 100% concordance with the results from 58 stool specimens which tested positive by conventional RT-PCR assays. By dilution analysis, the real-time LC RT-PCR was 10,000 times more sensitive than the conventional RT-PCR. The new assay increased the number of samples in which noroviruses were detected by 19%. The real-time LC RT-PCR had a wide dynamic range, detecting from 5 to 5 x 10(6) copies of RNA per reaction, resulting in a theoretical lower limit of detection of 25,000 copies of RNA per g of stool. No cross-reactions were found with specimens containing sapovirus, rotavirus, astrovirus, and adenovirus. Because of the high sensitivity and specificity of the assay with a relatively rapid and simple procedure, the real-time LC RT-PCR will be useful as a routine assay for the clinical diagnosis of norovirus infection.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".