[Sequencing and analysis of N gene of street rabies virus isolated from different hosts in Zhejiang province].
Bibliographic record
Abstract
OBJECTIVE: To elucidate the characteristics of genetic variability and its relationship with prevalence, through sequencing and analysis of N gene among street rabies virus isolated from different hosts (homo sapiens, ferret badger, dog) in Zhejiang province. METHODS: Samples were screened and confirmed by direct fluorescence assay and reverse transcript PCR. Sequences were analyzed using bio-information software. RESULTS: Eighteen street rabies virus strains were identified, including 2 from homo sapiens, 5 from ferret badger, and 11 from dog. Similarities of N gene and N protein were calculated to be 89.7%-100.0% and 98.4%-100.0% respectively. Mutations occurred in N gene were almost non-sense mutations. In addition,Data from phylogenetic analysis showed that all these strains could be classified into traditional genotype 1. CONCLUSION: The prevalence of rabies viruses among different hosts in Zhejiang province had certain regional properties. Rabies viruses isolated from the same kind of host or from the same/adjacent county/counties had the closest relationship. However, the characteristics of rabies virus prevalent in homo sapiens were somewhat complicated. In summary, the transmission of street rabies virus in Zhejiang province was from dogs to ferret badgers and homo sapiens, and the virus could circulate and cross-regional transmit among dogs and ferret badgers.
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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".