Pseudotyping of VSV with Ebola virus glycoprotein is superior to HIV-1 for the assessment of neutralising antibodies
Bibliographic record
Abstract
Ebola virus (EBOV) is an enveloped, single-stranded RNA virus that can cause Ebola virus disease (EVD). It is thought that EVD survivors are protected against subsequent infection with EBOV and that neutralising antibodies to the viral surface glycoprotein (GP) are potential correlates of protection. Serological studies are vital to assess neutralising antibodies targeted to EBOV GP; however, handling of EBOV is limited to containment level 4 laboratories. Pseudotyped viruses can be used as alternatives to live viruses, which require high levels of bio-containment, in serological and viral entry assays. However, neutralisation capacity can differ among pseudotyped virus platforms. We evaluated the suitability of EBOV GP pseudotyped human immunodeficiency virus type 1 (HIV-1) and vesicular stomatitis virus (VSV) to measure the neutralising ability of plasma from EVD survivors, when compared to results from a live EBOV neutralisation assay. The sensitivity, specificity and correlation with live EBOV neutralisation were greater for the VSV-based pseudotyped virus system, which is particularly important when evaluating EBOV vaccine responses and immuno-therapeutics. Therefore, the EBOV GP pseudotyped VSV neutralisation assay reported here could be used to provide a better understanding of the putative correlates of protection against EBOV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".