Detection of Hepatitis A Virus and Rotavirus Using Nucleic Acid Sequence-Based Amplification
Bibliographic record
Abstract
Viral food-borne illnesses have become very common in humans worldwide. Three viruses—noroviruses, rotavirus, and hepatitis A virus (HAV)—are implicated frequently in food-borne illness and have been ranked among the top 10 causes of food-borne disease over the past 10 years. The most common food vehicles for the transmission of enteric viruses to humans are shellfish, fruits, and vegetables. Foods may be contaminated by water tainted with untreated sewage or by contact with infected human food handlers. Virus concentrations in foods are usually low, as they are not able to multiply in situ . Therefore, the ability to detect traces of viruses in foods is essential in the development of tools for the investigation and possible prevention of viral disease outbreaks. Molecular approaches based on the amplification of viral RNA have been proposed for the specific and ultrasensitive detection of enteric viruses in foods. Nucleic acid sequence-based amplification (NASBA) is one of these molecular techniques showing great promise in viral detection. In this chapter, we describe two applications using NASBA techniques for the detection of hepatitis A virus and rotavirus.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".