The emerging tick-borne Yezo virus - current knowledge, challenges, and perspectives
Bibliographic record
Abstract
Ticks are external parasites that can carry and transmit a diverse range of pathogens, including bacteria, parasites, and viruses. In recent years, factors such as climate change, deforestation, and human activities have contributed to the expansion of tick populations along with the pathogens they harbour. These pathogens can cause serious disease in both humans and animals, such as Lyme disease and Crimean-Congo hemorrhagic fever (CCHF). In 2021, a novel tick-borne orthonairovirus, Yezo virus (YEZV) was reported in Hokkaido, Japan, in two patients presenting with acute febrile illness characterized by thrombocytopenia and leukopenia. Subsequent cases of YEZV have been reported in China, and it has been isolated in ticks collected from migratory birds in Japan, indicating its potential to spread further. As this virus emerges, critical gaps remain in understanding the mechanisms of YEZV pathogenesis, transmission, and prevention. This review aims to provide a comprehensive understanding of YEZV since its discovery, with a focus on its biology, pathogenesis, reported patient cases, treatments, and prevention strategies. By examining the current literature, this review seeks to identify key directions for future research and public health initiatives to enhance control measures for this emerging disease. Ultimately, adopting cross-sectoral One Health approaches will improve early warning, prevention, and preparedness for a potential global health emergency caused by YEZV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".