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Record W2922116967 · doi:10.3390/v11030259

Generation and Characterization of Anti-Filovirus Nucleoprotein Monoclonal Antibodies

2019· article· en· W2922116967 on OpenAlexafffund
Md Niaz Rahim, Min Wang, Tong Wang, Shihua He, Bryan D. Griffin, Darwyn Kobasa, Ruifu Yang, Zongmin Du, Xiangguo Qiu

Bibliographic record

VenueViruses · 2019
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsEbola virusVirologyNucleoproteinMonoclonal antibodyMarburg virusEpitopeEbolavirusBiologyFiloviridaeEpitope mappingViral replicationAntibodyVirusParamyxoviridaeViral diseaseImmunology

Abstract

fetched live from OpenAlex

Filoviruses cause lethal hemorrhagic fever in humans. The filovirus nucleoprotein (NP) is expressed in high abundance in infected cells and is essential for virus replication. To generate anti-filovirus monoclonal antibodies (mAbs) against the NP, mice were immunized with peptides known as B-cell epitopes corresponding to different filovirus NPs, and hybridomas were screened using FLAG-tagged filovirus NP constructs. Numerous mAbs were identified, isotyped, and characterized. The anti-NP mAbs demonstrated different ranges of binding affinities to various filovirus NPs. Most of the clones specifically detected both recombinant and wild-type NPs from different filoviruses, including Ebola (EBOV), Sudan (SUDV), Bundibugyo (BDBV), Marburg (MARV), Tai Forest (TAFV), and Reston (RESTV) viruses in western blot analysis. The mAbs were also able to detect native NPs within the cytoplasm of infected cells by immunofluorescence confocal microscopy. Thus, this panel of mAbs represents an important set of tools that may be potentially useful for diagnosing filovirus infection, characterizing virus replication, and detecting NP⁻host protein interactions.

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 score0.250
Threshold uncertainty score0.323

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 head0.059
GPT teacher head0.329
Teacher spread0.270 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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