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Record W4321373212 · doi:10.3390/mi14020472

Strategies for the Voltammetric Detection of Loop-Mediated Isothermal Amplification

2023· review· en· W4321373212 on OpenAlexafffund
Jesse M. Marangoni, Kenneth Ng, Arezoo Emadi

Bibliographic record

VenueMicromachines · 2023
Typereview
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsLoop-mediated isothermal amplificationAptamerPyrophosphateBiosensorMiniaturizationMaterials scienceNanotechnologySensitivity (control systems)AmpliconOligonucleotideChemistryDNAElectronic engineeringPolymerase chain reactionEngineering

Abstract

fetched live from OpenAlex

Loop-mediated isothermal amplification (LAMP) is rapidly developing into an important tool for the point-of-use detection of pathogens for both clinical and environmental samples, largely due to its sensitivity, rapidity, and adaptability to portable devices. Many methods are used to monitor LAMP, but not all are amenable to point-of-use applications. Common methods such as fluorescence often require bulky equipment, whereas colorimetric and turbidimetric methods can lack sensitivity. Electrochemical biosensors are becoming increasingly important for these applications due to their potential for low cost, high sensitivity, and capacity for miniaturization into integrated devices. This review provides an overview of the use of voltammetric sensors for monitoring LAMP, with a specific focus on how electroactive species are used to interface between the biochemical products of the LAMP reaction and the voltammetric sensor. Various strategies for the voltammetric detection of DNA amplicons as well as pyrophosphate and protons released during LAMP are presented, ranging from direct DNA binding by electroactive species to the creative use of pyrophosphate-detecting aptamers and pH-sensitive oligonucleotide structures. Hurdles for adapting these devices to point-of-use applications are also discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.294
Teacher spread0.249 · 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 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

Citations11
Published2023
Admission routes2
Has abstractyes

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