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Record W4294074093 · doi:10.4315/jfp-22-164

Examining the Effect of Organic Acids on Inactivation of Hepatitis E Virus

2022· article· en· W4294074093 on OpenAlexafffundabout
Madison McLeod, Genevieve Belford, Jennifer Harlow, Neda Nasheri

Bibliographic record

VenueJournal of Food Protection · 2022
Typearticle
Languageen
FieldMedicine
TopicHepatitis Viruses Studies and Epidemiology
Canadian institutionsUniversity of OttawaHealth Canada
FundersCanadian Institute for Advanced Research
KeywordsInfectivityCitric acidAcetic acidHepatitis E virusFood scienceChemistryVirologyBiologyVirusGenotypeBiochemistry

Abstract

fetched live from OpenAlex

Infection with hepatitis E virus genotype 3 (HEV-3) is an emerging cause of illness in developed countries. In North America and Europe, HEV-3 has been increasingly detected in swine, and exposure to pigs and pork products is considered the primary source of infection. We have previously demonstrated the prevalence of the HEV-3 genome in commercial pork products in Canada. In this study, we investigated the application of citric acid and acetic acid to inactivate HEV-3 on food and on food contact surfaces. For this purpose, plastic, stainless steel, and pork pâté surfaces were inoculated with HEV-3 and were treated with acetic acid or citric acid at 1, 3, or 5%. The infectivity of posttreatment viral particles was determined by cell culture. A greater than 2-log reduction in viral infectivity was observed on plastic and stainless steel treated with the organic acids, but the treatment was less effective on HEV infectivity on pork pâté (average reductions of 0.47 log citric acid and 0.63 log acetic acid). Therefore, we conclude that citric acid and acetic acid have potential application to control HEV-3 on food contact surfaces but are not suitable for food. Citric acid reduces HEV-3c infectivity by a maximum of 2.5 log on stainless steel and plastic. Acetic acid reduces HEV-3c infectivity by a maximum of 2.4 log on stainless steel and plastic. Citric acid and acetic acid reduce HEV-3c infectivity by a maximum of 0.7 log on pork pâté.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.047
GPT teacher head0.283
Teacher spread0.236 · 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 designBench or experimental
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

Citations10
Published2022
Admission routes3
Has abstractyes

Explore more

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