Autochthonous Transmission of West Nile Virus by a New Vector in Iran, Vector-Host Interaction Modeling and Virulence Gene Determinants
Bibliographic record
Abstract
Using molecular techniques and bioinformatics tools, we studied the vector-host interactions and the molecular epidemiology of West Nile virus (WNV) in western Iran. Mosquitoes were collected during 2017 and 2018. DNA typing assays were used to study vector-host interactions. Mosquitoes were screened by RT-PCR for the genomes of five virus families. WNV-positive samples were fully sequenced and evolutionary tree and molecular architecture were constructed by Geneious software and SWISS-MODEL workspace, respectively. A total of 5028 mosquito specimens were collected and identified. The most prevalent species was Culex (Cx.) pipiens complex (57.3%). Analysis of the blood-feeding preferences of blood-fed mosquitoes revealed six mammalian and one bird species as hosts. One mosquito pool containing non-blood-fed Cx. theileri and one blood-fed Culex pipiens pipiens (Cpp.) biotype pipiens were positive for WNV. A phylogram indicated that the obtained WNV sequences belonged to lineage 2, subclade 2 g. Several amino acid substitutions suspected as virulence markers were observed in the Iranian WNV strains. The three-dimensional structural homology model of the E-protein identified hot spot domains known to facilitate virus invasion and neurotropism. The recent detection of WNV lineage 2 in mosquitoes from several regions of Iran in consecutive years suggests that the virus is established in the country.
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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 | 0.000 | 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) | 0.000 | 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".