Detection of Hepatitis A Virus by the Nucleic Acid Sequence-Based Amplification Technique and Comparison with Reverse Transcription-PCR
Bibliographic record
Abstract
A nucleic acid sequence-based amplification (NASBA) technique for the detection of hepatitis A virus (HAV) in foods was developed and compared to the traditional reverse transcription (RT)-PCR technique. Oligonucleotide primers targeting the VP1 and VP2 genes encoding the major HAV capsid proteins were used for the amplification of viral RNA in an isothermal process resulting in the accumulation of RNA amplicons. Amplicons were detected by hybridization with a digoxigenin-labeled oligonucleotide probe in a dot blot assay format. Using the NASBA, as little as 0.4 ng of target RNA/ml was detected per comparison to 4 ng/ml for RT-PCR. When crude HAV viral lysate was used, a detection limit of 2 PFU (4 x 10(2) PFU/ml) was obtained with NASBA, compared to 50 PFU (1 x 10(4) PFU/ml) obtained with RT-PCR. No interference was encountered in the amplification of HAV RNA in the presence of excess nontarget RNA or DNA. The NASBA system successfully detected HAV recovered from experimentally inoculated samples of waste water, lettuce, and blueberries. Compared to RT-PCR and other amplification techniques, the NASBA system offers several advantages in terms of sensitivity, rapidity, and simplicity. This technique should be readily adaptable for detection of other RNA viruses in both foods and clinical 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".