Detection of Enterovirus D68 infection Among Malaysian population using a Preliminary luciferase-based Seroneutralization test / Nur Elena Mat Nayan
Bibliographic record
Abstract
Enterovirus D68 (EV-D68) is a respiratory tract pathogen which causes a range of mild to severe respiratory symptoms and in rare cases, neurological symptoms such as acute flaccid myelitis. In 2014, major outbreaks of EV-D68 occurred in several countries such as the United States and Canada, demonstrating that EV-D68 is an emerging pathogen. Therefore, studying the prevalence of EV-D68 infection in the Malaysian population is important to predict future outbreaks. In this study, a recombinant EV-D68 virus expressing a NanoLuc luciferase reporter gene (EV-D68_Nluc) was constructed using restriction enzyme-based cloning. This reporter-expressing infectious clone was used to develop a preliminary luciferase-based seroneutralization test which yields results 3 days faster than the conventional test. Subsequently, the luciferase-based test was used to assess the seroprevalence of EV-D68 neutralising antibodies (NAbs) in Malaysia using serum samples from three age groups, children (1 to 12 years old), adults (13 to 49 years old) and elderly (50 years old and above) collected in 2013, 2014 and 2015. We hypothesised that EV-D68 NAb seroprevalence would increase after 2014 in all age groups, but found a significant increase only in adults. Furthermore, we also hypothesized that EV-D68 NAb seroprevalence would increase with age. This was supported by our finding that children had significantly lower seroprevalence rates compared to adults and elderly. In addition, within the children group, seroprevalence rates increased with age: 32%, 59% and 77% in 1-3, 4-6 and 7-12 years old children, respectively. Hence, children should be prioritised in future vaccination programmes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".