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Record W2745086030 · doi:10.1002/jmv.24909

A safe and reliable neutralization assay based on pseudovirus to measure neutralizing antibody titer against poliovirus

2017· article· en· W2745086030 on OpenAlexfundno aff
Shaohua Liu, Dongmei Song, Han Bai, Weiwei Lu, Xinxian Dai, Chunsheng Hao, Zhongyang Zhang, Huijie Guo, Yue Zhang, Xiuling Li

Bibliographic record

VenueJournal of Medical Virology · 2017
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsPoliovirusVirologyNeutralizationTiterNeutralizing antibodyPoliomyelitisPoliomyelitis eradicationAntibodyPolio vaccineVirusBiologyImmunology

Abstract

fetched live from OpenAlex

With the promotion of inactivated poliomyelitis vaccine (IPV) and live attenuated oral poliomyelitis vaccine (OPV), the global reported cases of poliomyelitis have reduced sharply from 0.35 million in 1988 to 74 in 2015. The Polio Eradication & Endgame Strategic Plan published by WHO in 2013 included the strategy of implementation of poliovirus safe handling and containment measures to minimize the risks of facility-associated reintroduction of virus into the polio-free community to prevent the re-import of poliovirus. Toward this strategy, we produced replication-incompetent pseudovirus of poliovirus type 1, 2, 3 attenuated strains by constructing poliovirus capsid expression vectors and poliovirus replicon then transfecting HEK293T cells and developed a pseudovirus-based neutralization assay (pNA) to determine neutralizing antibody titer which is more secure, time-saving and reliable than conventional neutralization assay (cNA). By using anti-poliovirus rat serum, we demonstrated excellent correlation between neutralizing antibody titers measured by cNA and pNA. It was concluded that pNA can be a potential alternative to replace cNA as a safe and time-saving system for titer determination after live poliovirus's safekeeping.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.001
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 head0.042
GPT teacher head0.389
Teacher spread0.346 · 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 designObservational
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

Citations13
Published2017
Admission routes1
Has abstractyes

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