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Record W2999747699 · doi:10.1039/c9tb02584g

Advances in functional nucleic acid based paper sensors

2020· review· en· W2999747699 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Materials Chemistry B · 2020
Typereview
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAptamerComputer sciencePoint-of-care testingMicrofluidicsNanotechnologyNucleic acidDeoxyribozymeNucleic acid detectionSystems engineeringDNAMaterials scienceEngineeringBiology

Abstract

fetched live from OpenAlex

Recently, portable sensing devices with point of care testing (POCT) capability have attracted great attention due to their inherent affordability and accessibility in low resource areas. Paper sensors possess excellent potential as POCT platforms because of low cost, ease of operation, disposability and high-volume manufacturing. Paper sensors that incorporate functional nucleic acids (FNAs) as recognition elements are particularly attractive given that FNAs can be isolated from random-sequence nucleic acid pools to recognize, or respond to, virtually any target of interest. In this review, the advantages of FNAs, particularly DNA aptamers and DNAzymes, as recognition elements for the design of paper sensors are first discussed. This is followed by reviewing three specific types of FNA based paper sensors: dot blots, lateral flow assays, and microfluidic paper-based analytical devices. Furthermore, advances in the signal reporting methods used by FNA based paper sensors are summarized. Finally, limitations of current FNA based paper sensors are discussed along with considerations of future research directions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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