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Record W2022714659 · doi:10.1089/fpd.2010.0681

Heat Inactivation of Hepatitis A Virus and a Norovirus Surrogate in Soft-Shell Clams <i>(Mya arenaria)</i>

2010· article· en· W2022714659 on OpenAlexafffund
Halimatou Sow, M. Desbiens, Rocío Morales-Rayas, Solange Ngazoa, Julie Jean

Bibliographic record

VenueFoodborne Pathogens and Disease · 2010
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsMinistère de l'Agriculture, des Pêcheries et de l'AlimentationUniversité Laval
FundersHealth Canada
KeywordsNorovirusMurine norovirusTiterHepatitis a virusVirus quantificationMicrobiologyBiologyVirologyShellfishContaminationVirusInfectious doseFood scienceChemistryFish <Actinopterygii>Aquatic animalFisheryEcology

Abstract

fetched live from OpenAlex

The effectiveness of different thermal treatments for inactivating two viruses in clams was evaluated. Soft-shell clam digestive glands experimentally contaminated with hepatitis A virus (HAV) or murine norovirus (MNV) were heated for 90, 180, or 300 seconds at 85°C or 90°C in glass vials or plastic bags with 200 g of soft-shell clam meat. Inactivation was measured by plaque assay and real-time reverse-transcription (RT)-polymerase chain reaction assay. Measured inactivation was similar using both assays. The 90°C for 90 seconds treatment reduced MNV-1 titer by 3.33 log cycles and HAV by 2.66 log cycles. At 90°C for 180 seconds, both MNV-1 and HAV were completely inactivated (titer reduced by 5.47 log cycles) in glass vials. In the presence of clam meat as well, HAV inactivation was complete at 90°C for 180 seconds. In general, HAV was more resistant to heat treatment than MNV-1, suggesting that it would require a more severe treatment than human norovirus for inactivation in soft-shell clams. The results of the present study should contribute to the development of strategies for controlling the spread of enteric viral illness via shellfish.

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)0.001
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 score0.253
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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

Citations56
Published2010
Admission routes2
Has abstractyes

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