The Application of a Safe Neutralization Assay for Ebola Virus Using Lentivirus-Based Pseudotyped Virus
Bibliographic record
Abstract
Ebolavirus (EBOV) is responsible for several EBOV disease (EVD) outbreaks in Africa, with a fatality rate of up to 90%. During 2014-2016, An epidemic of EVD spread throughout Sierra Leone, Guinea and Liberia, and killed over 11,000 people. EBOV began to circulate again in the Democratic Republic of Congo in 2018. Due to the need for a BSL-4 facility to manipulate this virus, the development and improvement of specific therapeutics has been hindered. As a result, it is imperative to perform reliable research on EBOV under lowered BSL restrictions. In this study, we developed a safe neutralization assay based on pseudotyped EBOV, which incorporates the glycoprotein of the 2014 EBOV epidemic strain into a lentivirus vector. Our results demonstrated that the tropism of pseudotyped EBOV was similar to that of authentic EBOV, but with only one infection cycle. And neutralizing activity of both authentic EBOV and pseudotyped EBOV were compared in neutralization assay using three different samples of antibody-based reagents against EBOV, similar results were obtained. In addition, an indirect ELISA was performed to show the relationship between IgG and neutralizing antibody against EBOV detected by our pseudotyped EBOV-based neutralization assay. As expected, the neutralizing antibody titers varied with the IgG titers detected by indirect ELISA, and a correlation between the results of the two assays was identified. By comparison with two different assays, the reliability of the results detected by the pseudotyped EBOV-based neutralization assay was confirmed. Collectively, in the absence of BSL-4 restrictions, pseudotyped EBOV production and neutralizing activity evaluation can be performed safely and in a manner that is neither labor- nor time-consuming, providing a simple and safe method for EBOV-neutralizing antibody detection and the assessment of immunogenicity of EBOV vaccines. All these remarkable advantages of the newly established assay highlight its potential to further application in assessment of immunogenicity of EBOV vaccine candidates.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.010 |
| 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".