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Record W2101883797 · doi:10.1002/bit.25122

Improved isoelectric focusing chromatography on strong anion exchange media via a new model that custom designs mobile phases using simple buffers

2013· article· en· W2101883797 on OpenAlexafffund
Derek Yau Chung Choy, A. Louise Creagh, Charles A. Haynes

Bibliographic record

VenueBiotechnology and Bioengineering · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChromatofocusingElutionChemistryChromatographyIsoelectric pointIsoelectric focusingIon exchangeImmobilized pH gradientIon chromatographyMass spectrometryIonAnalytical Chemistry (journal)Organic chemistry

Abstract

fetched live from OpenAlex

Isoelectric chromatofocusing (ICF), a mode of chromatography by which proteins are separated based on changes in their charge state with pH, is widely used at analytical scales and finding increasing interest in biologics manufacturing due to its exceptional resolving power. Here, a method is described for using simple monoprotic and diprotic buffers to create stable mobile phases for sample loading on a strong anion exchange column and for achieving an elution pH gradient of desired shape covering any pH range from pH 10.0 to 3. The buffers used are selected to satisfy cost constraints, and to permit facile detection of eluted biologics by UV spectroscopy and mass spectrometry. The method exploits a new model described here that combines multiple-chemical and adsorption-equilibria theory to enable in silico tailoring of elution pH profiles using mixtures of these simple buffers. It is shown to provide a versatile platform for optimizing and conducting ICF of protein mixtures on strong anion exchange media.

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 score0.286
Threshold uncertainty score0.873

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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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

Citations4
Published2013
Admission routes2
Has abstractyes

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