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Record W2528380537 · doi:10.1038/srep34350

Electrochemical aptasensor for lung cancer-related protein detection in crude blood plasma samples

2016· article· en· W2528380537 on OpenAlexaff
Galina S. Zamay, Tatiana N. Zamay, Vasilii A. Kolovskii, Alexandr V. Shabanov, Yury E. Glazyrin, Dmitry V. Veprintsev, Alexey V. Krat, Sergey S. Zamay, Olga S. Kolovskaya, Ana Gargaun, Alexey E. Sokolov, Andrey A. Modestov, Ivan Artyukhov, Н. В. Чесноков, М. М. Петрова, Maxim V. Berezovski, Anna S. Zamay

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAptamerDetection limitFerrocyanideFerricyanideChemistryElectrochemistryElectrochemical gas sensorChromatographyLung cancerElectrodeBiochemistryMolecular biologyPathologyMedicine

Abstract

fetched live from OpenAlex

The development of an aptamer-based electrochemical sensor for lung cancer detection is presented in this work. A highly specific DNA-aptamer, LC-18, selected to postoperative lung cancer tissues was immobilized onto a gold microelectrode and electrochemical measurements were performed in a solution containing the redox marker ferrocyanide/ferricyanide. The aptamer protein targets were harvested from blood plasma of lung cancer patients by using streptavidin paramagnetic beads and square wave voltammetry of the samples was performed at various concentrations. In order to enhance the sensitivity of the aptasensor, silica-coated iron oxide magnetic beads grafted with hydrophobic C8 and C4 alkyl groups were used in a sandwich detection approach. Addition of hydrophobic beads increased the detection limit by 100 times. The detection limit of the LC-18 aptasensor was enhanced by the beads to 0.023 ng/mL. The formation of the aptamer - protein - bead sandwich on the electrode surface was visualized by electron microcopy. As a result, the electrochemical aptasensor was able to detect cancer-related targets in crude blood plasma of lung cancer patients.

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 score0.006
Threshold uncertainty score0.406

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 head0.007
GPT teacher head0.263
Teacher spread0.255 · 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

Citations64
Published2016
Admission routes1
Has abstractyes

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