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Record W2067477861 · doi:10.1117/12.628791

Synthesis and characterization of peptide nanostructures designed for sensing applications

2005· article· en· W2067477861 on OpenAlexafffund
Normand Voyer, Mathieu Arseneault, François Otis

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Laval
KeywordsAvidinPeptideFluorophoreBiotinylationVesicleBiotinMembraneChemistryBiosensorAnalyteFluorescenceNanostructureBiophysicsCombinatorial chemistryNanotechnologyChromatographyMaterials scienceBiochemistryBiology

Abstract

fetched live from OpenAlex

We report the design and the synthesis of membrane-active peptide nanostructures, as well as their use as signal transducer in a fluorimetric assay for biologically relevant analytes. Addition of hydrophobic 21-residue peptides bearing six crown ether side chains to a solution of small unilamellar vesicles loaded with a pH-sensitive fluorophore induces a rapid fluorescence increase associated with Na+/H+ transport across the bilayer membrane. To demonstrate the usefulness of these peptide nanostructures in the development of simple, rapid, and sensitive detection assays for a wide range of analytes, peptide nanostructures bearing a biotin at the N-terminal position were prepared. Addition of avidin to the assay employing these modified peptides resulted in a significant change in the time-dependent fluorescence profile. Control experiments with a non-binding proteins and saturated avidin showed that the observed changes are indeed due to specific binding of avidin to biotin modified nanostructures.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.229
Teacher spread0.223 · 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
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

Citations1
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE→Same topicAdvanced biosensing and bioanalysis techniques→French-language works237,207→