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Record W4401827835 · doi:10.1093/jambio/lxae221

Evaluation of inhibitory activity of essential oils and natural extracts on foodborne viruses

2024· article· en· W4401827835 on OpenAlexafffundabout
M. Amri, Éric Jubinville, Valérie Goulet-Beaulieu, Ismaı̈l Fliss, Julie Jean

Bibliographic record

VenueJournal of Applied Microbiology · 2024
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Agriculture, des Pêcheries et de l'Alimentation
KeywordsMurine norovirusNorovirusTiterFood scienceBiologyHepatitis a virusAntimicrobialVirus quantificationEssential oilVirusMicrobiologyVirology

Abstract

fetched live from OpenAlex

AIMS: Enteric viruses are recognized as a major concern in health care and in the food sector in Canada. Novel clean-label strategies for controlling enteric viruses are sought in the food industry. In this study, we examined the antiviral potential of plant extracts and essential oils on murine norovirus 1 (MNV-1), hepatitis A virus (HAV), and herpes simplex virus 1 (HSV-1). METHODS AND RESULTS: Inactivation of the viruses by grape seed, blueberry, green tea, and cranberry extracts and by rosemary and thyme essential oils was measured using plaque formation assay. Concentrations ranging from 50 to 200 000 ppm with a contact time of 90 min were tested. Grape seed extract at 10 000 ppm was the most effective (P < 0.05) at reducing MNV-1 and HAV infectious titers, respectively, by 2.85 ± 0.44 log10 and 1.94 ± 0.17 log10. HSV-1 titer was reduced by 3.81 ± 0.40 log10 at 1000 ppm grape seed extract. CONCLUSIONS: Among the plant products tested, grape seed extract was found the most effective at reducing the infectious titers of MNV-1, HAV, and HSV.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.036
GPT teacher head0.352
Teacher spread0.316 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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