MétaCan
Menu
Back to cohort
Record W2114959684 · doi:10.1096/fj.04-2769fje

Factors limiting autogene‐based cytoplasmic expression systems

2005· article· en· W2114959684 on OpenAlexaff
Jonathan D. Finn, Ian MacLachlan, Pieter R. Cullis

Bibliographic record

VenueThe FASEB Journal · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsInimex Pharmaceuticals (Canada)Innovative Targeting Solutions (Canada)University of British Columbia
Fundersnot available
KeywordsGene expressionTransfectionGeneBiologyMessenger RNAMolecular biologyRNase PTransgeneCytoplasmRegulation of gene expressionPlasmidGene deliveryRNACell biologyGenetics

Abstract

fetched live from OpenAlex

The relatively low levels of transfection that can be achieved by current gene delivery systems have limited the therapeutic utility of gene transfer. This is especially true for non-viral gene delivery systems, where the levels of gene expression achieved are usually well below the levels achieved by viral gene transfer systems. Previous work from our laboratory describes an enhanced dual promoter autogene-based cytoplasmic expression system that gives rise to levels of gene expression 20-fold higher than that of a CMV nuclear expression plasmid control. Here various strategies are described to increase the levels of autogene-based gene expression by changing variables such as the type of nuclear promoter, phage RNAP gene, and IRES element. Although insights into the function of various IRES elements were gained, none of these changes demonstrated a significant increase in gene expression. However, determination of the mRNA levels achieved using quantitative RNase protection assays and immunofluorescence experiments revealed that transgene mRNA levels were saturated at up to 10 times higher than all other mRNA in the transfected cell combined. It follows that mRNA production, as well as translation, are important factors limiting autogene-based cytoplasmic expression.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.040
GPT teacher head0.299
Teacher spread0.258 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicVirus-based gene therapy researchFrench-language works237,207