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Record W2802145197 · doi:10.3390/s18051440

Fluorescent Nanobiosensors for Sensing Glucose

2018· review· en· W2802145197 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.

Bibliographic record

VenueSensors · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsFluorescenceNanomaterialsNanotechnologyOptical sensingBiosensorMaterials scienceBiochemical engineeringComputer scienceEngineeringOptoelectronics

Abstract

fetched live from OpenAlex

Glucose sensing in diabetes diagnosis and therapy is of great importance due to the prevalence of diabetes in the world. Furthermore, glucose sensing is also critical in the food and drug industries. Sensing glucose has been accomplished through various strategies, such as electrochemical or optical methods. Novel transducers made with nanomaterials that integrate fluorescent techniques have allowed for the development of advanced glucose sensors with superior sensitivity and convenience. In this review, glucose sensing by fluorescent nanobiosensor systems is discussed. Firstly, typical fluorescence emitting/interacting nanomaterials utilized in various glucose assays are discussed. Secondly, strategies for integrating fluorescent nanomaterials and biological sensing elements are reviewed and discussed. In summary, this review highlights the applicability of fluorescent nanomaterials, which makes them ideal for glucose sensing. Insight on the future direction of fluorescent nanobiosensor systems is also provided.

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 score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.338
Teacher spread0.308 · 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