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Record W3043804952 · doi:10.1016/j.bsheal.2020.07.004

Seroprevalence of Wenzhou virus in China

2020· article· en· W3043804952 on OpenAlexfundno aff
Li Guo, Shasha Liu, Jingdong Song, Lianlian Han, Hu Zhang, Chao Wu, Conghui Wang, Hongli Zhou, Jianwei Wang

Bibliographic record

VenueBiosafety and Health · 2020
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
FundersChinese Academy of Medical SciencesNational Natural Science Foundation of ChinaNational Major Science and Technology Projects of ChinaCanadian Dairy Commission
KeywordsSeroprevalenceLymphocytic choriomeningitisChinaVirusVirologyBiologyAge groupsAntibodyMedicineVeterinary medicineSerologyImmunologyDemographyGeographyAntigen

Abstract

fetched live from OpenAlex

Wenzhou virus (WENV) was first identified in rodents and Asian house shrews in Wenzhou, Zhejiang Province, China. However, little is known about the prevalence of WENV infections in humans in China. To determine the threat that WENV may pose to humans, we determine the seroprevalence of WENV in healthy individuals in China in this study. Cross-reactivities of nucleoprotein (NP) were detected between Lymphocytic choriomeningitis virus (LCMV) and WENV using Western blot and ELISA assy. The prevalence of specific IgG antibodies against WENV NP was investigated in different age groups of 830 healthy individuals aged 0-70 years old in China using a competition ELISA assay. The results indicate that WENV and LCMV share cross-reactive epitopes between NPs. The total seroprevalence of WENV in healthy adults was 4.6%, with 3.6% (8/221) for individuals 15-44 years of age, 5.4% (17/317) for individuals 45-59 years of age, and 4.1% (4/98) for older adults over 60. The total seroprevalence of WENV in children under age 15 was 1.5%, with 2.9% (1/34) in children aged 2-5 years, and 2.2% in 5-14 years (2/91). The finding suggests that WENV or WENV-like virus may sporadically infect humans of China.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.386
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2020
Admission routes1
Has abstractyes

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