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Record W2054190466 · doi:10.1039/c3an36881e

Ultra-rapid colorimetric assay for protease detection using magnetic nanoparticle-based biosensors

2013· article· en· W2054190466 on OpenAlexaff
Ghadeer A. R. Y. Suaifan, Chiheb Esseghaier, Andy Ng, Mohammed Zourob

Bibliographic record

VenueThe Analyst · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsInstitut National de la Recherche Scientifique
FundersUniversity of Jordan
KeywordsBiosensorDetection limitProteaseChemistryNaked eyeMagnetic nanoparticlesNanotechnologyChromatographyNanoparticleMaterials scienceBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Sensitive protease detection methods often require time-consuming techniques and expensive instrumentation. To overcome this limitation, a novel, simple, sensitive and selective colorimetric detection approach was developed. This biosensing configuration was validated by the use of prostate specific antigen (PSA) protease as a model target. In this method, proteolytically active PSA capable of cleaving PSA substrates caused the release of black-colored magnetic carrier complexes, exposing the gold color sensor surface visible to the naked eye. The assay showed excellent sensitivity as well as specificity, capable of discriminating between different types of protease targets. The biosensor was able to quantitatively detect different PSA concentrations with a detection limit as low as 10 ng mL(-1). The sensor offers the possibility of developing a wash-less and cost-effective point-of-care device due to the simplicity of the probe immobilization process and the elimination of labeling and reporter molecules during the biosensing step.

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 score0.016
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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

Citations56
Published2013
Admission routes1
Has abstractyes

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