Rapid detection of human rotavirus using colorimetric nucleic acid sequence-based amplification (NASBA)âenzyme-linked immunosorbent assay in sewage treatment effluent
Bibliographic record
Abstract
A colorimetric nucleic acid sequence-based amplification-enzyme-linked immunosorbent assay (NASBA-ELISA) was developed for rapid detection and identification of human rotavirus. Oligonucleotide primers targeting gene 9 encoding a serotype-specific antigen VP7 were selected and used for the amplification of viral RNA by the isothermal NASBA process, resulting in the accumulation of biotinylated RNA amplicons. Amplicons were hybridized with a specific amino-linked oligonucleotide probe covalently immobilized on microtiter plates. The DNA-RNA hybrids were colorimetrically detected by the addition of streptavidin-peroxidase conjugate and tetramethylbenzidine substrate. Using the NASBA-ELISA system, as little as 0.2 PFU (4 x 10(1) PFU ml(-1)) and 15 PFU (3 x 10(3) PFU ml(-1)) of rotavirus were detected within 6 h in spiked MQ water and sewage treatment effluent respectively. No interference was encountered in the amplification and detection of rotavirus in the presence of non-target RNA or DNA. Moreover, the presence of non-target bacteria and virus does not generate any non-specific signal, confirming the specificity of the developed NASBA-ELISA system and its effectiveness in specifically detecting rotavirus. The NASBA-ELISA 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".