Epidemiological surveillance and phylogenetic diversity of Orthohantavirus hantanense using high-fidelity nanopore sequencing, Republic of Korea
Bibliographic record
Abstract
BACKGROUND: Orthohantavirus hantanense (HTNV) poses a substantial global public health threat due to its role in causing hemorrhagic fever with renal syndrome (HFRS). HTNV outbreaks are particularly prevalent in the Gyeonggi and Gangwon Provinces of the Republic of Korea (ROK). This study aimed to evaluate the application of advanced nanopore sequencing and bioinformatics to generate complete genome sequences of HTNV, with the objective of accurately identifying infection sources and analyzing their genetic diversity. METHODOLOGY/PRINCIPAL FINDINGS: In 2022 and 2023, we collected 579 small mammals from 11 distinct locations across Gyeonggi and Gangwon Provinces, as well as in Gwangju Metropolitan City, ROK. Among these, 498 Apodemus agrarius specimens were subjected to an epidemiological survey to investigate HTNV infections. The serological and molecular positivity of HTNV were found to be 65/498 (13.1%) and 17/65 (26.2%), respectively. Furthermore, 15 whole-genome sequences of HTNV were obtained from rodents in Gyeonggi and Gangwon Provinces. We developed a novel amplicon-based nanopore sequencing approach to acquire high-fidelity and precise genomic sequences of HTNV. Genome exchange analysis revealed three reassortant candidates, including heterogeneous L segments, from Paju-si and Yeoncheon-gun in Gyeonggi Province. CONCLUSION/SIGNIFICANCE: Our findings enhance the resolution of the spatiotemporal genomic surveillance of HTNV by consistently providing new viral sequences and epidemiological data from HFRS-endemic regions in the ROK. This report signifies a notable advancement in nanopore sequencing techniques and bioinformatics, offering a robust platform for genome-based diagnostics and sophisticated phylogenetic analyses of orthohantaviruses, which are essential for public health strategies aimed at controlling HFRS.
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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 | 0.000 |
| 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".