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Record W1999436595 · doi:10.1021/ac901471w

Differentiation and Detection of PDGF Isomers and Their Receptors by Tunable Aptamer Capillary Electrophoresis

2009· article· en· W1999436595 on OpenAlexafffund
Hongquan Zhang, Xing‐Fang Li, X. Chris Le

Bibliographic record

VenueAnalytical Chemistry · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAptamerChemistryCapillary electrophoresisPlatelet-derived growth factor receptorReceptorChromatographyFluorescenceBiophysicsBiochemistryMolecular biologyGrowth factorBiology

Abstract

fetched live from OpenAlex

Tunable aptamer capillary electrophoresis (CE) techniques were developed to enable the separation and detection of platelet derived growth factor (PDGF) isomers and their receptors. Using an aptamer that formed a stable complex with the B chain but not with the A chain of PDGF, we were able to tweak the electrophoretic mobilities of the PDGF isomers for their separation. PDGF-AB bound to a single aptamer molecule was well resolved from PDGF-BB bound to two aptamer molecules. Simultaneous determination of 50 pM of two isomers was accomplished in a single analysis. Furthermore, PDGF-AB was used as a connector to bring receptor alpha and fluorescent aptamer into a single complex molecule. As a result, the formation of a (receptor alpha)-(PDGF-AB)-(fluorescent aptamer) ternary complex enabled the detection of the receptor alpha by tunable aptamer CE. A competitive assay was developed to determine receptor beta, making use of the competition between the receptor beta and fluorescent aptamer in binding to PDGF-BB. Detection limits were 0.5 nM for PDGF receptor alpha and 3 nM for receptor beta. Determination of PDGF isomers and their receptors in diluted serum samples showed no interference from the sample matrix.

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 score0.008
Threshold uncertainty score0.430

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 head0.003
GPT teacher head0.212
Teacher spread0.209 · 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

Citations51
Published2009
Admission routes2
Has abstractyes

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