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Recovery of a Neurovirulent Human Coronavirus OC43 from an Infectious cDNA Clone

2006· article· en· W2046082083 on OpenAlexaff
Julien R. St-Jean, Marc Desforges, Fernando Almazán, Hélène Jacomy, Luis Enjuanes, Pierre J. Talbot

Bibliographic record

VenueJournal of Virology · 2006
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBiologyVirologyComplementary DNABacterial artificial chromosomeRecombinant DNAVirusCoronavirusNidoviralesclone (Java method)Recombinant virusGenomeReverse geneticsMolecular biologyGeneGeneticsCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

This study describes the assembly of a full-length cDNA clone of human coronavirus (HCoV)-OC43 in a bacterial artificial chromosome (BAC). The BAC containing the full-length infectious cDNA (pBAC-OC43(FL)) was assembled using a two-part strategy. The first step consisted in the introduction of each end of the viral genome into the BAC with accessory sequences allowing proper transcription. The second step consisted in the insertion of the whole HCoV-OC43 cDNA genome into the BAC. To produce recombinant viral particles, pBAC-OC43(FL) was transfected into BHK-21 cells. Recombinant virus displayed the same phenotypic properties as the wild-type virus, including infectious virus titers produced in cell culture and neurovirulence in mice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.038
GPT teacher head0.367
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), 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

Citations96
Published2006
Admission routes1
Has abstractyes

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