Development of in vitro and in vivo rabies virus neutralization assays based on a high-titer pseudovirus system
Bibliographic record
Abstract
Pseudoviruses are useful virological tools because of their safety and versatility; however the low titer of these viruses substantially limits their wider applications. We developed a highly efficient pseudovirus production system capable of yielding 100 times more rabies pseudovirus than the traditional method. Employing the high-titer pseudoviruses, we have developed robust in vitro and in vivo neutralization assays for the evaluation of rabies vaccine, which traditionally relies on live-virus based assays. Compared with current rapid fluorescent focus inhibition test (RFFIT), our in vitro pseudovirus-based neutralization assay (PBNA) is much less labor-intensive while demonstrating better reproducibility. Moreover, the in vivo PBNA assay was also found to be superior to the live virus based assay. Following intravenous administration, the pseudovirus effectively infected the mice, with dynamic viral distributions being sequentially observed in spleen, liver and brain. Furthermore, data from in vivo PBNA showed great agreement with those generated from the live virus model but with the experimental time significantly reduced from 2 weeks to 3 days. Taken together, the effective pseudovirus production system facilitated the development of novel PBNA assays which could replace live virus-based traditional assays due to its safety, rapidity, reproducibility and high throughput capacity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".