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Record W2610312550 · doi:10.1080/09540105.2017.1320359

Visual flow-through column biomimetic immunoassay using molecularly imprinted polymer as artificial antibody for rapid detection of clenbuterol in water sample

2017· article· en· W2610312550 on OpenAlexafffund
Yiwei Tang, Yuchen Zhang, Hong Zhang, Xiuying Liu, Xue Gao, Changxin Lv, Tao Ma, Xiaonan Lu, Jianrong Li

Bibliographic record

VenueFood and Agricultural Immunology · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPharmacological Effects and Assays
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsChromatographyDetection limitMolecularly imprinted polymerImmunoassayChemistryHorseradish peroxidaseClenbuterolColumn chromatographySelectivityAntibodyOrganic chemistry

Abstract

fetched live from OpenAlex

A novel visual flow-through column biomimetic immunoassay using horseradish peroxidase-labeled clenbuterol (HRP-CLB) as a tracer and molecularly imprinted polymers (MIPs) with high specificity as the artificial antibody has been developed for rapid detection of CLB. Different intensities of blue color products on test layer of the column could be observed after reaction. This optimized assay provided a preliminary qualitative result in the concentration range of 0 to 1000 μg/L by judging color density of the test layer without any equipment, and the detection could be finished within 15 min. This assay was evaluated using CLB spiked water samples and results were comparable to high performance liquid chromatography method. The visual detection limit of 5 μg/L in water sample was obtained. This developed flow-through column biomimetic immunoassay was demonstrated as a suitable tool for rapid, sensitive, low-cost, and qualitative determination of CLB residues on site.

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 score0.051
Threshold uncertainty score0.412

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 studies0.0010.000
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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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