MétaCan
Menu
Back to cohort
Record W2100806839 · doi:10.1186/1471-2199-6-11

Investigation of factors responsible for cell line cytoplasmic expression differences

2005· article· en· W2100806839 on OpenAlexafffund
Jonathan D. Finn, Tabitha Wong, Pieter R. Cullis

Bibliographic record

VenueBMC Molecular Biology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsInimex Pharmaceuticals (Canada)University of British Columbia
FundersCanadian Institutes of Health Research
KeywordsCytoplasmBiologyGene expressionCell cultureNucleaseCell biologyGeneCellRegulation of gene expressionMolecular biologyCell typeGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Previous work has described a novel cytoplasmic expression system that results in a 20-fold increase in the levels of gene expression over a standard CMV-based nuclear expression system, as compared with a 2-3 fold increase seen with previous similar systems. While this increase was seen with BHK and Neuro-2a cells, further studies revealed that some cell lines, such as COS-7, demonstrated relatively poor levels of cytoplasmic expression. The objective of this study was to determine what factors were responsible for the different expression levels between BHK (a high expressing cell line) and COS-7 (a low expressing cell line). RESULTS: The main findings of this work are that the individual elements of the cytoplasmic expression system (such as the T7 RNAP gene and Internal Ribosome Entry Sequence) are functioning similarly in both cell types. Both cell types were found to have the same amount of cytosolic nuclease activity, and that the cells appeared to have differences in the intra-cellular processing of DNA-cationic lipid complexes. CONCLUSION: After exploring many factors, it was found that differences in the intra-cellular processing of the DNA-cationic lipid complex was the most probable factor responsible for the difference in cytoplasmic gene 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.533

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 head0.029
GPT teacher head0.279
Teacher spread0.250 · 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

Citations2
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueBMC Molecular BiologySame topicRNA Interference and Gene DeliveryFrench-language works237,207