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Record W2512862427 · doi:10.1093/infdis/jiw289

Inactivation of<i>Zaire ebolavirus</i>Variant Makona in Human Serum Samples Analyzed by Enzyme-Linked Immunosorbent Assay

2016· article· en· W2512862427 on OpenAlexaff
Todd Cutts, Allen Grolla, Shane Jones, Bradley W. M. Cook, Xiangguo Qiu, Steven Theriault

Bibliographic record

VenueThe Journal of Infectious Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaCanadian Science Centre for Human and Animal Health
Fundersnot available
KeywordsEbola virusOutbreakVirologyEbolavirusTiterAntibodyVirusBiologyInfectious disease (medical specialty)MicrobiologyImmunologyMedicineDisease

Abstract

fetched live from OpenAlex

Personnel deployed to remote areas during infectious disease outbreaks have limited access to mechanical and chemical inactivation resources. The inactivation of infectious agents present in diagnostic samples is critical to ensure the safety of personnel and the containment of the disease. We evaluated the efficacy of thermal inactivation (exposure to 56°C for 1 hour) and chemical inactivation with 0.5% Tween-20 against a high titer of Ebola virus (species Zaire ebolavirus) variant Makona in spiked human serum samples. No surviving virus was revealed by a 50% tissue culture infective dose assay after the combined treatment under laboratory conditions. In-field use of this inactivation protocol during the 2013-2016 West Africa Ebola outbreaks demonstrated readily detectable levels of immunoglobulin G and/or immunoglobulin M in human plasma samples after treatment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2016
Admission routes1
Has abstractyes

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