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Record W4384557607 · doi:10.3390/v15071558

The Re-Emergence of Hepatitis E Virus in Europe and Vaccine Development

2023· review· en· W4384557607 on OpenAlexaff
Gergana Zahmanova, Katerina Takova, Valeria Tonova, Tsvetoslav Koynarski, Laura L. Lukov, Ivan Minkov, Maria Pishmisheva, Stanislav Kotsev, Ilia Tsachev, Magdalena Baymakova, Anton Andonov

Bibliographic record

VenueViruses · 2023
Typereview
Languageen
FieldMedicine
TopicHepatitis Viruses Studies and Epidemiology
Canadian institutionsUniversity of Manitoba
FundersEuropean Regional Development FundL'Oreal USA
KeywordsHepatitis E virusTransmission (telecommunications)VirologyHepatitis EMedicineVirusViral hepatitisVaccinationEnvironmental healthImmunologyBiologyGenotype

Abstract

fetched live from OpenAlex

Hepatitis E virus (HEV) is one of the leading causes of acute viral hepatitis. Transmission of HEV mainly occurs via the fecal-oral route (ingesting contaminated water or food) or by contact with infected animals and their raw meat products. Some animals, such as pigs, wild boars, sheep, goats, rabbits, camels, rats, etc., are natural reservoirs of HEV, which places people in close contact with them at increased risk of HEV disease. Although hepatitis E is a self-limiting infection, it could also lead to severe illness, particularly among pregnant women, or chronic infection in immunocompromised people. A growing number of studies point out that HEV can be classified as a re-emerging virus in developed countries. Preventative efforts are needed to reduce the incidence of acute and chronic hepatitis E in non-endemic and endemic countries. There is a recombinant HEV vaccine, but it is approved for use and commercially available only in China and Pakistan. However, further studies are needed to demonstrate the necessity of applying a preventive vaccine and to create conditions for reducing the spread of HEV. This review emphasizes the hepatitis E virus and its importance for public health in Europe, the methods of virus transmission and treatment, and summarizes the latest studies on HEV vaccine development.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.192
GPT teacher head0.414
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations33
Published2023
Admission routes1
Has abstractyes

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