MétaCan
Menu
Back to cohort
Record W2046363309 · doi:10.1109/tbcas.2014.2304718

Developing Trends in Aptamer-Based Biosensor Devices and Their Applications

2014· review· en· W2046363309 on OpenAlex
Scott MacKay, David S. Wishart, James Z. Xing, Jie Chen

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 Biomedical Circuits and Systems · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAptamerBiosensorNanotechnologyComputer scienceComputational biologyMaterials scienceBiologyMolecular biology

Abstract

fetched live from OpenAlex

Aptamers are, in general, easier to produce, easier to store and are able to bind to a wider variety of targets than antibodies. For these reasons, aptamers are gaining increasing popularity in environmental monitoring as well as disease detection and disease management applications. This review article examines the research and design of RNA and DNA aptamer based biosensor systems and applications as well as their potential for integration in effective biosensor devices. As single stranded DNA or RNA molecules that can bind to specific targets, aptamers are well suited for biomolecular recognition and sensing applications. Beyond being able to be designed for a near endless number of specific targets, aptamers can also be made which change their conformation in a predictable and consistent way upon binding. This can lead to many unique and effective detection methods using a variety of optical and electrochemical means.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.042
GPT teacher head0.329
Teacher spread0.287 · 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