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Record W2803286407 · doi:10.1080/02648725.2018.1471643

Vaccinia-based vaccines to biothreat and emerging viruses

2018· review· en· W2803286407 on OpenAlexaff
Les P. Nagata, Chad R. Irwin, Weigang Hu, David H. Evans

Bibliographic record

VenueBiotechnology and Genetic Engineering Reviews · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of AlbertaDefence Research and Development Canada
Fundersnot available
KeywordsVacciniaSmallpoxVirologySmallpox vaccineVaccinationOncolytic virusModified vaccinia AnkaraViral vectorAttenuated vaccineBiologyMedicineVirusImmunologyVirulence

Abstract

fetched live from OpenAlex

The past few years have seen a rash of emerging viral diseases, including the Ebola crisis in West Africa, the pandemic spread of chikungunya, and the recent explosion of Zika in South America. Vaccination is the most reliable and cost-effective method of control of infectious diseases, however, there is often a long delay in production and approval in getting new vaccines to market. Vaccinia was the first vaccine developed for the successful eradication of smallpox and has properties that make it attractive as a universal vaccine vector. Vaccinia can cause severe complications, particularly in immune suppressed recipients that would limit its utility, but nonreplicating and attenuated strains have been developed. Modified vaccinia Ankara is nonreplicating in human cells and can be safely given to immune suppressed individuals. Vaccinia has recently been modified for use as an oncolytic treatment for cancer therapy. These new vaccinia vectors are replicating; but have been attenuated and could prove useful as a universal vaccine carrier as many of these are in clinical trials for cancer therapy. This article reviews the development of a universal vaccinia vaccine platform for emerging diseases or biothreat agents, based on nonreplicating or live attenuated vaccinia viruses.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.336
Teacher spread0.304 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2018
Admission routes1
Has abstractyes

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