MétaCan
Menu
Back to cohort
Record W2560330288 · doi:10.3390/s16122061

Integrating Deoxyribozymes into Colorimetric Sensing Platforms

2016· review· en· W2560330288 on OpenAlexafffund
Dingran Chang, Sandy Zakaria, Mimi Deng, Nicholas Allen, Kha Tram, Yingfu Li

Bibliographic record

VenueSensors · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeBiosensorBiodefenseAptamerNanotechnologyAnalyteNucleic acidNaked eyeChemistryBiochemical engineeringDNABiochemistryMaterials scienceBiologyDetection limitEngineeringMolecular biology

Abstract

fetched live from OpenAlex

Biosensors are analytical devices that have found a variety of applications in medical diagnostics, food quality control, environmental monitoring and biodefense. In recent years, functional nucleic acids, such as aptamers and nucleic acid enzymes, have shown great potential in biosensor development due to their excellent ability in target recognition and catalysis. Deoxyribozymes (or DNAzymes) are single-stranded DNA molecules with catalytic activity and can be isolated to recognize a wide range of analytes through the process of in vitro selection. By using various signal transduction mechanisms, DNAzymes can be engineered into fluorescent, colorimetric, electrochemical and chemiluminescent biosensors. Among them, colorimetric sensors represent an attractive option as the signal can be easily detected by the naked eye. This reduces reliance on complex and expensive equipment. In this review, we will discuss the recent progress in the development of colorimetric biosensors that make use of DNAzymes and the prospect of employing these sensors in a range of chemical and biological applications.

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 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)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.332
Teacher spread0.312 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations47
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueSensorsSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207