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Record W2005472972 · doi:10.1039/c5an00475f

Single-step bioassays in serum and whole blood with a smartphone, quantum dots and paper-in-PDMS chips

2015· article· en· W2005472972 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Analyst · 2015
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMichael Smith Health Research BCCanada Foundation for Innovation
KeywordsQuantum dotAlexa FluorFörster resonance energy transferAnalyteMaterials scienceWhole bloodMultiplexBiosensorOptoelectronicsNanotechnologyMultiplexingFluorescenceChemistryOpticsBioinformaticsChromatographyComputer scienceTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

The development of nanoparticle-based bioassays is an active and promising area of research, where point-of-care (POC) diagnostics are one of many prospective applications. Unfortunately, the majority of nanoparticle-based assays that have been developed to date have failed to address two important considerations for POC applications: use of instrumentation amenable to POC settings, and measurement of analytes in biological sample matrices such as serum and whole blood. To address these considerations, we present design criteria and demonstrate proof-of-concept for a semiconductor quantum dot (QD)-based assay format that utilizes smartphone readout for the single-step, Förster resonance energy transfer (FRET)-based detection of hydrolase activity in serum and whole blood, using thrombin as a model analyte. Important design criteria for assay development included (i) the size and emission wavelength of the QDs, which had to balance brightness for smartphone imaging, optical transmission through blood samples, and FRET efficiency for signaling; (ii) the wavelength of a light-emitting diode (LED) excitation source, which had to balance transmission through blood and the efficiency of excitation of QDs; and (iii) the use of an array of paper-in-polydimethylsiloxane (PDMS)-on-glass sample chips to reproducibly limit the optical path length through blood to ca. 250 μm and permit multiplexing. Ultimately, CdSe/CdS/ZnS QDs with peak emission at 630 nm were conjugated with Alexa Fluor 647-labeled peptide substrates for thrombin and immobilized on paper test strips inside the sample cells. This FRET system was sensitive to thrombin activity, where the recovery of QD emission with hydrolytic loss of FRET permitted kinetic assays in buffer, serum and whole blood. Quantitative results were obtained in less than 30 min with a limit of detection 18 NIH units mL(-1) of activity in 12 μL of whole blood. Proof-of-concept for a competitive binding assay was also demonstrated with the same platform. Overall, this work demonstrates that the integration of QDs with smartphones and other consumer electronics can potentiate bioassays that are highly amenable to future point-of-care diagnostic applications.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.291

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 head0.014
GPT teacher head0.182
Teacher spread0.168 · 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