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Record W4224988147 · doi:10.3390/separations9050106

Separation Abilities of Capillary Electrophoresis Coupled with Ion Mobility Mass Spectrometry for the Discrete Detection of Sequence Isomeric Peptides

2022· article· en· W4224988147 on OpenAlexaff
Yury E. Glazyrin, Gleb G. Mironov, Anna S. Kichkailo, Maxim V. Berezovski

Bibliographic record

VenueSeparations · 2022
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Ottawa
FundersMinistry of Science and Higher Education of the Russian Federation
KeywordsCapillary electrophoresisChemistryMass spectrometryChromatographyCapillary electrophoresis–mass spectrometryIsoleucineTandem mass spectrometryPeptideValinePeptide mass fingerprintingAnalytical Chemistry (journal)Protein mass spectrometryPeptide sequenceAlanineAmino acidProteomicsLeucineElectrospray ionizationBiochemistry

Abstract

fetched live from OpenAlex

The separation and discrete detection of isomeric sequence peptides with similar properties are important tasks for analytical science. Three different peptide isomers of 12 amino-acid residues long, containing direct and reverse regions of the alanine-valine-proline-isoleucine (AVPI) motif, were partially separated and discretely detected from their mixture using two approaches. Capillary electrophoresis enabled the separation and optical detection of the peptide sequence isomers close to the baseline. The ability to separate these sequence isomers from the mixture and discretely identify them from mass spectra has also been demonstrated by ion-mobility tandem mass spectrometry. Moreover, for the first time, capillary electrophoresis and ion-mobility mass spectrometry connected online have shown their ability for a discrete detection of the multidirectional sequence isomers.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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
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

Citations5
Published2022
Admission routes1
Has abstractyes

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