Novel Human Polyomaviruses Discovered From 2007 to the Present: An Update of Current Knowledge
Bibliographic record
Abstract
Human polyomaviruses (HPyVs) are a diverse group of viruses that typically establish asymptomatic persistent infections in healthy individuals. However, they can lead to severe diseases in immunocompromised patients. The past 15 years have witnessed significant advancements in understanding HPyVs, leading to the discovery of several novel and highly divergent strains. This surge in knowledge raises critical questions about their evolution, tropism, and potential contributions to various diseases. Although HPyVs are generally benign, certain strains can lead to significant health issues under immunocompromised conditions. Since 2007, several novel PyVs have been isolated from humans: Karolinska Institute Polyomavirus (KIPyV), Washington University Polyomavirus (WUPyV), Merkel cell Polyomavirus (MCPyV), HPyV6, HPyV7, Trichodisplasia spinulosa polyomavirus (TSPyV), HPyV9, HPyV10, Saint Louis polyomavirus (STLPyV), HPyV12, New Jersey Polyomavirus (NJPyV), Lyon IARC polyomavirus (LIPyV), HPyV16 and Quebec polyomavirus (QPyV). This review summarises the available data regarding the biology, tissue tropism, epidemiology, and associated diseases of novel HPyVs discovered from 2007 to the present. While some HPyVs are well-characterised with clear associations to specific diseases, others remain enigmatic, warranting additional investigation into their biology and clinical implications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".