Assessing the efficacy of different bead-based assays in capturing hepatitis E virus
Bibliographic record
Abstract
Hepatitis E virus (HEV) generally causes acute liver infection in humans and its transmission could be waterborne, foodborne, bloodborne, or zoonotic. To date, there is no standard method for the detection of HEV from food and environmental samples. Herein, we explored the possibility of using magnetic beads for the capture and detection of HEV. For this purpose, we employed Dynabeads M-270 Epoxy magnetic beads, coated with different monoclonal antibodies (mAbs) against HEV capsid protein, and the Nanotrap Microbiome A Particle magnetic beads, which are coated with chemical affinity baits, to capture HEV-3 particles in suspension. Viral RNA was extracted by heat-shock or QIAamp viral RNA kit and subjected to quantification using digital-droplet RT-PCR (ddRT-PCR). We demonstrated that the mAb-coupled Dynabeads and the Nanotrap particles, both were able to successfully capture HEV-3. The latter, however had lower limit of detection (<140gc compared with <1400 gc) and significantly higher extraction efficiency in comparison to the mAb-coupled Dynabeads (41.1% vs 8.8%). We have also observed that viral RNA extraction by heat-shock is less efficient compared to using highly denaturing reagents in QIAmp viral RNA extraction kit. As such, magnetic beads have the potential to be used to capture HEV virions for research and surveillance purposes.
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| 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".