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Record W1598329243 · doi:10.2174/1874357901509010001

Exploration of New Sites in Adenovirus Hexon for Foreign Peptides Insertion

2015· article· en· W1598329243 on OpenAlexafffund
Satyender Hansra, Sujit Pujhari, Alexander N. Zakhartchouk

Bibliographic record

VenueThe Open Virology Journal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - SaskatchewanCanadian Swine Health Board
KeywordsCapsidVirologyHypervariable regionEpitopeBiologyVirusPorcine reproductive and respiratory syndrome virusAntigenSerotypePeptide vaccineViral vectorPeptideGeneGenetics

Abstract

fetched live from OpenAlex

Adenoviral vectors are now being explored as vaccine carriers to prevent infectious diseases in humans and animals. There are two strategies aimed at the expression of a vaccine antigen by adenoviral vectors. The first includes an insertion of the foreign gene expression cassette into the E1 region. The second strategy is antigen incorporation into the viral capsid proteins. To extend this methodology, we have searched for new sites at the human adenovirus serotype 5 major capsid protein hexon for a vaccine antigen insertion. To this end, we utilized sites in the hexon hypervariable region (HVR) 7, 8 and 9 to display a 15-mer peptide containing the main neutralizing epitope of porcine reproductive and respiratory syndrome virus. However, we could not rescue the viruses with the insertions of the peptide into HVR 8 and 9, consistent with the viruses being unable to tolerate insertions at these sites. In contrast, the virus with the insertion of the peptide in HVR 7 was viable - growing well in cell culture and the inserted peptide was exposed on the virion surface.

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 communication0.0010.000
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 head0.146
GPT teacher head0.380
Teacher spread0.234 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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