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Record W3003236464 · doi:10.1111/jam.14596

Radiosensitivity increase in FCV-F9 virus using combined treatments with natural antimicrobials and γ-irradiation

2020· article· en· W3003236464 on OpenAlexafffund
Alexandra Gobeil, Shiv Shankar, Monique Lacroix

Bibliographic record

VenueJournal of Applied Microbiology · 2020
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaInternational Atomic Energy Agency
KeywordsRadiosensitivityAntimicrobialMicrobiologyIrradiationBiologyVirusVirology

Abstract

fetched live from OpenAlex

Abstract Aims The objective was to evaluate the possible synergistic effect of cranberry juice (CJ) and commercial citrus extract (BS) against FCV-F9 viral titre in vitro in combination with γ-irradiation and to determinate the D10 values and radiosensitivity increase. Methods and Results Virus samples were treated with a formulation containing a mixture of BS or CJ. Results showed a D10 of 0·05, 0·42% and 1·34 kGy for the virus treated with the BS, the CJ and the irradiation alone respectively. Concentrations needed to reduce 6 log TCID50 ml−1 of viral titre were BS-0·3%, CJ-2·52% and 8·04 kGy. Irradiation combined with BS-0·01% and CJ-0·1% against FCV-F9 virus showed D10 values of 0·74 and 0·72 kGy, respectively, resulting in a viral radiosensitization of 1·28 and 1·50 for respective treatments. Conclusion The higher viral radiosensitization observed after combining γ-irradiation with BS-0·01% and CJ-0·1% indicates that CJ and BS could be used as antiviral agents alone or in combination with γ-irradiation to prevent NoV outbreaks. Significance and Impact of the Study Cranberry juice and BS could be used in hurdle approaches in combined treatment with γ-irradiation to assure food safety without a detrimental effect on nutritional value and maintain low processing cost.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.269
Teacher spread0.250 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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