Detection of Zaire ebolavirus in swine: Assay development and optimization
Bibliographic record
Abstract
Ebolaviruses (family Filoviridae, order Mononegavirales) cause often fatal, haemorrhagic fever in primates including humans. Pigs have been identified as a species susceptible to Reston ebolavirus (RESTV) infection, with indicated transmission to humans in the Philippines; however, their role during Ebola outbreaks in Africa needs to be clarified. To perform surveillance studies, detection of ebolavirus requires a prerequisite validation of viral RNA and antibody detection methods in swine samples. These diagnostic tests also need to be suitable for deployment to low-level containment laboratories. In this study, we developed a set of tests for detection of antibodies against Zaire ebolavirus (EBOV) in swine. Recombinant EBOV nucleoprotein was produced using a baculovirus expression system for indirect ELISA development. Evaluation of this assay was performed using laboratory and field samples, achieving a diagnostic specificity of 99%. Importantly, the indirect ELISA was able to detect antibodies to EBOV at 7 dpi, 3 days earlier than virus neutralization tests (VNT). The format of the VNT in this work was modified to a microtitre plaque reduction neutralization assay (miPRNT) complemented with immunostaining to provide a more rapid and highly specific assay. Finally, a confirmatory immunoblot assay was generated to supplement the indirect ELISA results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".