MétaCan
Menu
Back to cohort

Quantifying low-frequency revertants in oral poliovirus vaccine using next generation sequencing

2017· article· en· W2609371110 on OpenAlexaff
Eric Sarcey, Aurélie Serres, Fabrice Tindy, Audrey Chareyre, Siemon H. S. Ng, Marine Nicolas, Emmanuelle Vetter, Thierry Bonnevay, Éric Abachin, Laurent Mallet

Bibliographic record

VenueJournal of Virological Methods · 2017
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsSanofi (Canada)
FundersSanofi Pasteur
KeywordsBiologyVirologyPoliovirusDNA sequencingComputational biologyGeneticsVirusDNA

Abstract

fetched live from OpenAlex

Spontaneous reversion to neurovirulence of live attenuated oral poliovirus vaccine (OPV) serotype 3 (chiefly involving the n.472U>C mutation), must be monitored during production to ensure vaccine safety and consistency. Mutant analysis by polymerase chain reaction and restriction enzyme cleavage (MAPREC) has long been endorsed by the World Health Organization as the preferred in vitro test for this purpose; however, it requires radiolabeling, which is no longer supported by many laboratories. We evaluated the performance and suitability of next generation sequencing (NGS) as an alternative to MAPREC. The linearity of NGS was demonstrated at revertant concentrations equivalent to the study range of 0.25%-1.5%. NGS repeatability and intermediate precision were comparable across all tested samples, and NGS was highly reproducible, irrespective of sequencing platform or analysis software used. NGS was performed on OPV serotype 3 working seed lots and monovalent bulks (n=21) that were previously tested using MAPREC, and which covered the representative range of vaccine production. Percentages of 472-C revertants identified by NGS and MAPREC were comparable and highly correlated (r≥0.80), with a Pearson correlation coefficient of 0.95585 (p<0.0001). NGS demonstrated statistically equivalent performance to that of MAPREC for quantifying low-frequency OPV serotype 3 revertants, and offers a valid alternative to MAPREC.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.576
GPT teacher head0.546
Teacher spread0.030 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Virological MethodsSame topicViral Infections and Immunology ResearchFrench-language works237,207