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Record W1974465538 · doi:10.1021/bc034130j

Efficient and Expedient Two-Step Pyranose-Retaining Fluorescein Conjugation of Complex Reducing Oligosaccharides:  Galectin Oligosaccharide Specificity Studies in a Fluorescence Polarization Assay

2003· article· en· W1974465538 on OpenAlexaff
Christopher T. Öberg, Susanne Carlsson, Éric Fillion, Hakon Leffler, Ulf J. Nilsson

Bibliographic record

VenueBioconjugate Chemistry · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChemistryGalectinPyranoseFluorescence anisotropyFluorescenceOligosaccharideConjugateAcylationFluoresceinFluorescent labellingBiochemistryChromatographyCombinatorial chemistryBiophysics

Abstract

fetched live from OpenAlex

Fluorescence labeling of naturally occurring saccharides provides a tool for studying lectins. A practical and efficient two-step protocol for fluorescence labeling of reducing sugars without disrupting their pyranose structure has been developed, consisting of generation of the amino sugar using NH(4)HCO(3)(s)/NH(3)(aq, concentrated) followed by BOP-mediated acylation with derivatives of 5- or 6-carboxyfluorescein. The acylated conjugates were subsequently run against galectins-1, -3, and -8, beta-galactoside recognizing lectins of current interest, in a fluorescence polarization binding assay. Upon analyzing a collection of isomerically pure 5- and 6-carboxyfluorescein derivatives with different tether lengths, we found that conjugates based on 5-carboxyfluorescein gave significantly better results than the ones based on 6-carboxyfluorescein and that galectins-1 and -8 favored conjugates with different tether lengths than did galectin-3. The results show that fluorescence labeling can be chemically tuned to find optimal probes for individual galectins but also probes interacting well with many galectins.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.039
GPT teacher head0.294
Teacher spread0.254 · 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

Citations25
Published2003
Admission routes1
Has abstractyes

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