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Record W2911702732 · doi:10.3390/s19030631

Long-Range Surface Plasmon-Polariton Waveguide Biosensors for Human Cardiac Troponin I Detection

2019· article· en· W2911702732 on OpenAlexaff
Oleksiy Krupin, Pierre Berini

Bibliographic record

VenueSensors · 2019
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBiosensorTroponin IAnalyteMaterials scienceSurface plasmon polaritonDetection limitRefractive indexSurface plasmon resonanceSurface plasmonOptoelectronicsBiomedical engineeringNanotechnologyPlasmonAnalytical Chemistry (journal)ChemistryMyocardial infarctionChromatographyMedicineCardiologyNanoparticle

Abstract

fetched live from OpenAlex

Straight long-range surface plasmon-polariton (LRSPP) waveguides as biosensors for label-free detection are discussed. The sensors consist of 5-μm-wide 35-nm-thick gold stripes embedded in a low-index optical-grade fluoropolymer (CYTOPTM) with fluidic channels etched to the Au surface of the stripes. This work demonstrates the application of the LRSPP biosensors for the detection of human cardiac troponin I (cTnI) protein. cTnI is a biological marker for acute myocardial infarction (AMI), often referred to as a heart attack, which can be diagnosed by elevated levels of cTnI in patient blood. Direct and sandwich assays were developed and demonstrated over the concentration range from 1 to 1000 ng/mL, yielding detection limits of 430 pg/mL for the direct assay and 28 pg/mL for the sandwich assay (1 standard deviation), the latter being physiologically relevant to the early detection or onset of AMI. In addition, a novel approach for data analysis is proposed, where the analyte response is normalized to the response of the antibody layer.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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