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Record W2559730744 · doi:10.1038/srep38293

The Disinfection Characteristics of Ebola Virus Outbreak Variants

2016· article· en· W2559730744 on OpenAlexafffund
Bradley W. M. Cook, Todd Cutts, Aidan M. Nikiforuk, Anders Leung, Darwyn Kobasa, Steven Theriault

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersCanadian Institutes of Health Research
KeywordsEbola virusOutbreakVirologyInfectivitySodium hypochloriteEbolavirusVirusInfectious doseBiologyNorovirusMicrobiologyChemistry

Abstract

fetched live from OpenAlex

The recent Ebola virus outbreak in West Africa has forced experts to re-evaluate their understanding of how to best disinfect areas contaminated with infectious bodily fluids. Recent research has found that Ebola virus remains viable in blood for 7-10 days making appropriate disinfection crucial to infection control. We sought to determine if the three most important outbreak variants of Zaire ebolavirus (Mayinga, Kikwit and Makona) exhibit separate phenotypes when challenged with a range of sodium hypochlorite (NaOCl) concentrations or 70% ethanol (EtOH) at average West African temperature. The time dependent killing of Ebola virus was evaluated by measuring infectious virus and viral RNA (vRNA), to determine if RNA detection is a viable method for decontamination measurement in areas without high containment laboratory access. Makona was less susceptible to weaker concentrations of NaOCl (0.05 and 0.1%) than Mayinga and Kikwit. At the recommended concentration of NaOCl (≥0.5%) all of the variants were inert after 5 minutes of contact time. Similarly, all variants were inactivated by 70% EtOH after 2.5 minutes, only Makona was detected at 1 minute. In multiple instances, high amounts of vRNA was detected in the absence of infectious virus, suggesting that it does not serve as an accurate measure of remaining infectivity after cleansing.

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 score0.487
Threshold uncertainty score0.311

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 head0.019
GPT teacher head0.300
Teacher spread0.281 · 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

Citations38
Published2016
Admission routes2
Has abstractyes

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