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Micellar electrokinetic chromatography separations of dynorphin peptide analogs

2000· article· en· W1981475504 on OpenAlexaff
Alexandra Fürtös‐Matei, Robert Day, Serge St‐Pierre, Louis G. St-Pierre, Karen C. Waldron

Bibliographic record

VenueElectrophoresis · 2000
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversité du Québec à MontréalUniversité de SherbrookeUniversité de Montréal
Fundersnot available
KeywordsChemistryDynorphinMicellar electrokinetic chromatographyChromatographyPeptideCapillary electrophoresisOpioid peptideOpioidReceptorBiochemistry

Abstract

fetched live from OpenAlex

Prodynorphin is a precursor that has multiple cleavage sites to release various dynorphin opioid peptides. The dynorphin analogs used in this study have 18 amino acid residues. A series of dynorphin-like peptides, differing by a single residue (alanine substitution) were assembled by Fmoc solid-phase procedures and purified by preparative high performance liquid chromatography (HPLC). Separation of the Ala-scan dynorphin analogs was investigated by micellar electrokinetic chromatography (MEKC) employing anionic, cationic and zwitterionic surfactants. The role of electrostatic and hydrophobic forces in analyte-surfactant interactions is discussed with respect to the observed elution patterns. Separation of all dynorphin analogs by MEKC using a zwitterionic surfactant shows this technique to be powerful for separating closely related peptide species. It also demonstrates the potential for using MEKC for the prescreening of peptide libraries to determine their biological activity toward specific receptors. Results from the separation of dynorphin analogs by free solution and ion-pairing capillary electrophoresis are also presented.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.180
Teacher spread0.177 · 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.

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

Citations19
Published2000
Admission routes1
Has abstractyes

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