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Record W2127223388 · doi:10.1086/520593

Development of an Immunofiltration‐Based Antigen‐Detection Assay for Rapid Diagnosis of Ebola Virus Infection

2007· article· en· W2127223388 on OpenAlexafffund
Andreas Lucht, Pierre Formenty, Heinz Feldmann, Marion G. Götz, Eric M. Leroy, Pierre Bataboukila, Allen Grolla, Friederike Feldmann, Tatiana Wittmann, Patricia T. Campbell, Catherine Atsangandoko, Paul Boumandoki, E.-J. Finke, Peter Miethe, Stephan Becker, Roland Grunow

Bibliographic record

VenueThe Journal of Infectious Diseases · 2007
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Institute for Health and Care ResearchPublic Health Agency of Canada
KeywordsEbola virusVirologyEbolavirusOutbreakViral hemorrhagic feverAntigenVirusImmunoassayMonoclonal antibodyAntibodyBiologyMedicineImmunology

Abstract

fetched live from OpenAlex

Ebola virus (EBOV) has caused outbreaks of severe viral hemorrhagic fever in regions of Central Africa where medical facilities are ill equipped and diagnostic capabilities are limited. To obtain a reliable test that can be implemented easily under these conditions, monoclonal antibodies to the EBOV matrix protein (VP40), which previously had been found to work in a conventional enzyme-linked immunosorbent assay, were used to develop an immunofiltration assay for the detection of EBOV antigen in chemically inactivated clinical specimens. The assay was evaluated by use of defined virus stocks and specimens from experimentally infected animals. Its field application was tested during an outbreak of Ebola hemorrhagic fever in 2003. Although the original goal was to develop an assay that would detect all EBOV species, only the Zaire and Sudan species were detected in practice. The assay represents a first-generation rapid field test for the detection of EBOV antigen that can be performed in 30 min without electrical power or expensive or sensitive equipment.

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 score0.195
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.026
GPT teacher head0.334
Teacher spread0.308 · 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

Citations42
Published2007
Admission routes2
Has abstractyes

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