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Record W2758103344 · doi:10.1002/bit.26461

Advancements in the design and scalable production of viral gene transfer vectors

2017· review· en· W2758103344 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiotechnology and Bioengineering · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsViral vectorBiopharmaceuticalVector (molecular biology)LentivirusVirologyGenetic enhancementSAFERTechnology transferGene transferComputational biologyBiotechnologyBiologyComputer scienceVirusGeneViral diseaseGenetics

Abstract

fetched live from OpenAlex

The last 10 years have seen a rapid expansion in the use of viral gene transfer vectors, with approved therapies and late stage clinical trials underway for the treatment of genetic disorders, and multiple forms of cancer, as well as prevention of infectious diseases through vaccination. With this increased interest and widespread adoption of viral vectors by clinicians and biopharmaceutical industries, there is an imperative to engineer safer and more efficacious vectors, and develop robust, scalable and cost-effective production platforms for industrialization. This review will focus on major innovations in viral vector design and production systems for three of the most widely used viral vectors: Adenovirus, Adeno-Associated Virus, and Lentivirus.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.849
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.049
GPT teacher head0.324
Teacher spread0.275 · 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