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Record W4323658135 · doi:10.1002/anie.202213567

Monitoring Cardiac Biomarkers with Aptamer‐Based Molecular Pendulum Sensors

2023· article· en· W4323658135 on OpenAlexafffund
Alam Mahmud, Dingran Chang, Jagotamoy Das, Surath Gomis, Farid Foroutan, Jenise B. Chen, Laxman Pandey, Connor D. Flynn, Hanie Yousefi, Armin Geraili, Heather J. Ross, Edward H. Sargent, Shana O. Kelley

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsTed Rogers Centre for Heart ResearchYork UniversityUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAptamerMolecular biomarkersComputational biologyMedicineNanotechnologyInternal medicineBiologyMaterials scienceMolecular biology

Abstract

fetched live from OpenAlex

Reagent-free electronic biosensors capable of analyzing disease markers directly in unprocessed body fluids will enable the development of simple & affordable devices for personalized healthcare monitoring. Here we report a powerful and versatile nucleic acid-based reagent-free electronic sensing system. The signal transduction is based on the kinetics of an electrode-tethered molecular pendulum-a rigid double stranded DNA with one of the strands displaying an analyte-binding aptamer and the other featuring a redox probe-that exhibits field-induced transport modulated by receptor occupancy. Using chronoamperometry, which enables the sensor to circumvent the conventional Debye length limitation, the binding of an analyte can be monitored as these species increase the hydrodynamic drag. The sensing platform demonstrates a low femtomolar quantification limit and minimal cross-reactivity in analyzing cardiac biomarkers in whole blood collected from patients with chronic heart failure.

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 score0.007
Threshold uncertainty score0.577

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 head0.011
GPT teacher head0.273
Teacher spread0.262 · 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

Citations38
Published2023
Admission routes2
Has abstractyes

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