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Record W2766426664 · doi:10.1021/acs.analchem.7b02036

Nanoelectronic Platform for Ultrasensitive Detection of Protein Biomarkers in Serum using DNA Amplification

2017· article· en· W2766426664 on OpenAlexfundno aff
Luye Mu, Ilia A. Droujinine, Jieun Lee, Mathias Wipf, Paschall Davis, C.J. Adams, Jennifer Hannant, Mark A. Reed

Bibliographic record

VenueAnalytical Chemistry · 2017
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of KoreaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsBiosensorChemistryNanotechnologyImmunoassayAnalyteField-effect transistorTransistorMaterials scienceChromatographyAntibodyBiochemistryPhysics

Abstract

fetched live from OpenAlex

Silicon nanowire field effect transistors (NWFETs) are low noise, low power, ultrasensitive biosensors that are highly amenable to integration. However, using NWFETs to achieve direct protein detection in physiological buffers such as blood serum remains difficult due to Debye screening, nonspecific binding, and stringent functionalization requirements. In this work, we performed an indirect sandwich immunoassay in serum combined with exponential DNA amplification and pH measurement by ultrasensitive NWFET sensors. Measurements of model cytokine interleukin-2 concentrations from <20 fM to >200 pM were demonstrated, surpassing the conventional NWFET urease-based readout. Our approach paves way for future development of universal, highly sensitive, miniaturized, and integrated nanoelectronic devices that can be applied to a wide variety of analytes.

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 score0.100
Threshold uncertainty score0.345

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 head0.020
GPT teacher head0.258
Teacher spread0.238 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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