Real-time molecular beacon NASBA for rapid and sensitive detection of norovirus GII in clinical samples
Bibliographic record
Abstract
To improve the sensitivity and efficiency of the real-time nucleic acid sequence based amplification (NASBA) assay targeting the open reading frame 1-2 (ORF1-ORF2) junction of the norovirus (NoV) genome, a selection of clinical samples were analyzed. The assay results were compared with those of TaqMan and conventional reverse transcription PCR (RT-PCR) and a commercial enzyme-linked immunoassay (ELISA) for the specific detection of GII NoV in 96 fecal samples. Based on end-point dilution, the two real-time assays had similar sensitivities (0.01 particle detectable units), two log10 cycles greater than that of conventional RT-PCR. GII NoV was detected in 88.54% of the samples by real-time NASBA, in 86.46% by TaqMan RT-PCR, in 81.25% by conventional RT-PCR, and in 65.7% by ELISA. The two real-time assays were in agreement for 88.5% of the samples. These results demonstrate that real-time NASBA with a molecular beacon probe is highly sensitive, accurate, and specific for NoV detection in clinical samples. Applying this technique to samples with complex matrix and low viral loads, such as food and environmental samples, could be useful for the detection of NoVs and will improve the prevention of NoV outbreaks.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".