Population- and Variant-Based Genome Analyses of Viruses from Vaccine-Derived Rabies Cases Demonstrate Product Specific Clusters and Unique Patterns
Bibliographic record
Abstract
Rabies in wildlife has been successfully controlled in parts of Europe and North America using oral rabies vaccination, i.e., the distribution of baits containing live-attenuated virus strains. Occasionally, these vaccines caused vaccine virus-induced rabies cases. To elucidate the mechanisms of genetic selection and the effect of viral populations on these rabies cases, a next generation sequencing approach as well as comprehensive data analyses of the genetic diversity of Street Alabama Dufferin (SAD) and ERA vaccine virus strains and vaccine-induced rabies cases from Canada and several European countries were conducted. As a result, twelve newly generated sets of sequencing data from Canada and Poland were added to a pool of previously investigated samples. While the population-based analysis showed a segregation of viruses of ERA vaccine-induced rabies cases from those of SAD Bern original (SAD Bernorig)-derived rabies cases, the in-depth variant analysis revealed three distinct combinations of selected variants for the ERA vaccine-induced cases, suggesting the presence of multiple replication-competent haplotypes in the investigated ERA-BHK21 vaccine. Our findings demonstrate the potential of a deep sequencing approach in combination with comprehensive analyses on the consensus, population, and variant level.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".