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Record W2075673875 · doi:10.1086/520586

Rapid Assembly of Sensitive Antigen‐Capture Assays for Marburg Virus, Using In Vitro Selection of Llama Single‐Domain Antibodies, at Biosafety Level 4

2007· article· en· W2075673875 on OpenAlexfundno aff
Laura J. Sherwood, Lisa E. Osborn, Ricardo Carrion, Jean L. Patterson, Andrew Hayhurst

Bibliographic record

VenueThe Journal of Infectious Diseases · 2007
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Center for Research ResourcesPublic Health AgencyNational Institutes of HealthPublic Health Agency of CanadaJoint Program Executive Office for Chemical, Biological, Radiological and Nuclear DefenseSan Antonio Area Foundation
KeywordsEbola virusVirologyMarburg virusAntigenBiologyImmunoassayAntibodyVirusNucleoproteinBiosafetySingle-domain antibodyImmunology

Abstract

fetched live from OpenAlex

There is a pressing need for rapid and reliable approaches to the delivery of sensitive yet rugged diagnostic assays specific for emerging viruses, to hasten containment of outbreaks when and wherever they occur. Within 3 weeks, we delivered an antigen-capture assay for Marburg virus (MARV) that was based on llama single-domain antibodies (sdAbs) selected at biosafety level 4. Four unique sdAbs were capable of independently detecting MARV variants Musoke, Ravn, and Angola without cross-reactivity with the 4 Ebola virus species. The unoptimized assays could be performed in <30 min and, at best, provided a visual read of 10-100 pfu in a 100-microL sample when a colorimetric substrate was used and 0.1-1 pfu when a chemiluminescent substrate was used. All the sdAbs were specific for nucleoprotein, with an assay sensitivity that was reliant on detergent-mediated exposure of polyvalent antigen. Our strategy highlights the potential of direct antibody selection on filoviruses as a guide for effective and fast diagnostic development.

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 score0.196
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.040
GPT teacher head0.324
Teacher spread0.283 · 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

Citations66
Published2007
Admission routes1
Has abstractyes

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