Molecular characterization of emerging recombinant African swine fever virus of genotype I and II in Vietnam, 2023
Bibliographic record
Abstract
African swine fever virus (ASFV) recombinant strains pose new challenges for diagnosis and control. This study characterizes genotype I and II recombinant ASFV strains identified in northern Vietnam in 2023 through whole-genome sequencing and comparative genomic analysis. Seven ASFV-positive samples from six provinces were analyzed, with recombinant strains detected in Bac Giang, Phu Tho, and Vinh Phuc provinces. Isolates showed hemadsorption positivity despite having genotype I B646L, indicating their recombinant nature. Genome-wide analysis revealed 19 recombination breakpoints consistent with Chinese recombinant strains. Vietnamese isolates shared 99.86-99.98% nucleotide identity with Chinese recombinants, forming a distinct monophyletic group. Comparative analysis identified 50 SNPs and INDELs, with 39 variations found across Vietnamese strains, distinguishing them from Chinese isolates. Unique genetic markers in C962R, I329L, and MGF 505-11L genes distinguished Vietnamese recombinants from Chinese counterparts, while mutations in C122R and NP1450L differentiated all recombinants from parental genotypes. The central variable region (CVR) of the B602L gene showed diversity among Vietnamese isolates, while the I73R-I329L intergenic regions were recognized as in the IGR2 group. This study enhances understanding of recombinant ASFV evolution through homologous recombination and identifies new genetic markers for improved detection and characterization. The observed genetic diversity highlights challenges for existing diagnostic methods and vaccine development, emphasizing the need for continued surveillance and research into the functional implications of these genetic variations on ASFV pathogenicity and transmissibility.
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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".