MétaCan
Menu
Back to cohort
Record W1490397540 · doi:10.2144/01312st02

Fluorescence Produced by Transfection Reagents Can Be Confused with Green Fluorescent Proteins in Mammalian Cells

2001· article· en· W1490397540 on OpenAlexafffund
Baoqing Guo, Andrew Pearce, K.E.A. Traulsen, Anne C. Rintala, H. Lee

Bibliographic record

VenueBioTechniques · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsNortheast Cancer Centre
FundersMedical Research CouncilCanadian Institutes of Health Research
KeywordsGreen fluorescent proteinAequorea victoriaTransfectionFluorescenceFluorescence microscopeFusion proteinMolecular biologyBiologyFlow cytometryReporter geneChemistryGene expressionCell biologyRecombinant DNAGeneBiochemistry

Abstract

fetched live from OpenAlex

The Aequorea victoria green fluorescent protein (GFP) reporter system is a convenient way to monitor gene expression and other cellular functions in mammalian cells. To study gene expression, a GFP-fusion plasmid construct is often transfected into mammalian cells using a variety of methods including calcium phosphate- and liposome-based DNA transfer. Subsequently, the expression of GFP-fusion protein is monitored by fluorescence microscopy or flow cytometry. Here, we report that certain transfection reagents can produce fluorescence that can be detected in a wide range of wavelengths, which can be confused with GFP-fusion protein. The fluorescence false positives can be a problem, particularly when the GFP expression levels are low. To improve the GFP-based detection or screening methods, it is imperative to include an appropriate negative control and to detect GFP using a narrow-wavelength emission filter corresponding to the emission spectrum around the GFP peak.

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)0.003
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.003

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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

Citations20
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueBioTechniquesSame topicRNA Interference and Gene DeliveryFrench-language works237,207