MétaCan
Menu
Back to cohort
Record W2739174932 · doi:10.1039/c7an00624a

Detection of a cancer biomarker protein on modified cellulose paper by fluorescence using aptamer-linked quantum dots

2017· article· en· W2739174932 on OpenAlexafffund
Pradip Das, Ulrich J. Krull

Bibliographic record

VenueThe Analyst · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsCanadian Celiac AssociationUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAptamerBiomarkerFluorescenceQuantum dotChemistrySubstrate (aquarium)SelectivityBioassayNanotechnologyChromatographyCombinatorial chemistryMaterials scienceMolecular biologyBiochemistryBiologyGenetics

Abstract

fetched live from OpenAlex

The development of point-of-care bioassays for sensitive screening of protein-based cancer biomarkers would improve the opportunity for early stage diagnosis. A strategy for a fluorescence resonance energy transfer (FRET)-based bioassay has been investigated that makes use of modified cellulose paper for the detection of an epithelial cell adhesion molecule (EpCAM), which is a transmembrane glycoprotein that is overexpressed in several tumors of epithelial origin. The paper matrix was a substrate for immobilized aptamer-linked quantum dots (QDs-Apt) and Cy3 labeled complementary DNA (cDNA), which served as a donor and an acceptor, respectively. Competitive binding of EpCAM displaced the cDNA, resulting in the reduction of FRET. The paper-based bioassay was able to detect EpCAM in buffer solution as well as in 10% bovine serum solution using a reaction time of no more than 60 minutes. The dynamic range was 1-100 nM in buffer with a precision better than 4%, and the limit of detection was 250 pM in buffer and 600 pM in 10% serum.

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 score0.425

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 head0.026
GPT teacher head0.310
Teacher spread0.284 · 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

Citations44
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueThe AnalystSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207