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Record W2556365973 · doi:10.1109/tnb.2016.2628403

Rapid Detection of Protein Kinase on Capacitive Sensing Platforms

2016· article· en· W2556365973 on OpenAlex
Rohit Chand, Dawoon Han, Yong‐Sang Kim

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on NanoBioscience · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAptamerCapacitive sensingCapacitanceMaterials scienceDetection limitAnalyteBiosensorMonolayerOptoelectronicsSemiconductorISFETTransistorNanotechnologyVoltageChemistryComputer scienceField-effect transistorElectrical engineeringElectrodeChromatography

Abstract

fetched live from OpenAlex

In this study, we developed a capacitive sensor for the one-step and label-free detection of protein kinase A (PKA) enzyme. Metal-insulator-semiconductor (MIS) and electrolyte-insulator-semiconductor (EIS) are a simple electronic transducer, which allows efficient detection of the target analyte. For this reason, we performed a comparative sensing of PKA on the MIS and EIS capacitive sensor. The PKA-specific aptamer was used for the one-step detection. For the immobilization of thiolated aptamer, the MIS sensor contained a thin gold layer, whereas the EIS sensor had a self-aligned monolayer of gold nanoparticles. The interaction of aptamer and PKA changed the charge and density of the sensor surface. The quantitative detection of PKA was performed by analyzing the capacitance-voltage curve after the aptamer-PKA interaction. The MIS and EIS sensor showed a detection limit of 5 U/mL and 1 U/mL, respectively, for the detection of PKA. This study suggests valuable sensing platforms for the rapid and sensitive biochemical diagnosis.

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.

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 score0.343
Threshold uncertainty score0.381

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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