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Record W4401052290 · doi:10.1016/j.heliyon.2024.e35325

Development and characterization of monoclonal antibodies recognizing nucleocapsid protein of multiple SARS-CoV-2 variants

2024· article· en· W4401052290 on OpenAlexafffund
Hongyu Qiu, Xin-Yong Yuan, Kimberly Holloway, Heidi Wood, Teresa Cabral, Chris K. Grant, Peter McQueen, Garrett Westmacott, Daniel R. Beniac, Lisa Lin, Michael Carpenter, Darwyn Kobasa, Tom Gräfenhan, I. Wayne Cheney

Bibliographic record

VenueHeliyon · 2024
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsMonoclonal antibodySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakAntibodyCoronavirusSars virusBiologyComputational biologyMedicineImmunologyPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Rapid antigen test (RAT) is widely used for SARS-CoV-2 infection diagnostics. However, test sensitivity has decreased recently due to the emergence of the Omicron variant and its sublineages. Here we developed a panel of SARS-CoV-2 nucleocapsid protein (NP) specific mouse monoclonal antibodies (mAbs) and assessed their sensitivity and specificity to important SARS-CoV-2 variants. We identified seven mAbs that exhibited strong reactivity to SARS-CoV-2 variants and recombinant NP (rNP) by Western immunoblot or ELISA. Their specificity to SARS-CoV-2 was confirmed by negative or low reactivity to rNPs from SARS-CoV-1, MERS, and common human coronaviruses (HCoV-HKU1, HCoV-CO43, HCoV-NL63, and HCoV-229E). These seven mAbs were further tested by immunoplaque assay against selected variants of concern (VOCs), including two Omicron sublineages, and five mAbs (F461G13, F461G7, F459G7, F457G3, and F461G6), showed strong reactions, warranting further suitability testing for the development of diagnostic assay.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.062
GPT teacher head0.331
Teacher spread0.269 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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