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Record W2344455108 · doi:10.1007/978-1-59745-281-6_1

Two-Dimensional Electrophoresis: An Overview

2009· article· en· W2344455108 on OpenAlexaff
Richard W. Smith

Bibliographic record

VenueMethods in molecular biology · 2009
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIsoelectric pointProteomeChemistryIsoelectric focusingElectrophoresisTwo-dimensional gel electrophoresisGel electrophoresisChromatographyMolecular massFractionationGel electrophoresis of nucleic acidsMass spectrometryGel electrophoresis of proteinsImmobilized pH gradientProteomicsBiochemistryPolyacrylamide gel electrophoresisEnzyme

Abstract

fetched live from OpenAlex

Two-dimensional gel electrophoresis (2DE) separates proteins by molecular charge and molecular size. Proteins are first solubilised in a denaturing buffer containing a neutral chaotrope, a zwitterionic or neutral detergent, and a reducing agent. First-dimension isoelectric keywords, focusing, then subjects proteins to a high voltage within a pH gradient. The amphoteric nature of proteins means each migrates to the pH where the net molecular charge is zero. After equilibration, to ensure complete protein unfolding, the second dimension separates by molecular size. Each protein is therefore resolved at a unique isoelectric point/molecular size coordinate. After visualisation by staining proteome changes are revealed by gel image analysis, and protein spots of interest excised and identified by mass spectrometry sequence analysis combined with database comparison. Variations to this procedure include staining or radio-labelling prior to electrophoresis. Although 2DE does have limitations, the most significant being the resolution of membrane and/or hydrophobic proteins, the potential solutions offered by pre-fractionation or adjustments to the electrophoresis regimen mean this technique is likely to remain central to proteomic research.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.245
Threshold uncertainty score0.505

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 head0.032
GPT teacher head0.441
Teacher spread0.409 · 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
GenreMethods

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

Citations16
Published2009
Admission routes1
Has abstractyes

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