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Record W3003836971 · doi:10.3390/s20030813

Immunosensor Based on Long-Period Fiber Gratings for Detection of Viruses Causing Gastroenteritis

2020· article· en· W3003836971 on OpenAlexaff
Marta Janczuk-Richter, Beata Gromadzka, Łukasz Richter, Mirosława Panasiuk, Karolina Zimmer, Predrag Mikulic, Wojtek J. Bock, Sebastian Maćkowski, Mateusz Śmietana, Joanna Niedziółka‐Jönsson

Bibliographic record

VenueSensors · 2020
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsUniversité du Québec en Outaouais
FundersNarodowe Centrum Badań i RozwojuNarodowe Centrum Nauki
KeywordsPeriod (music)FiberOptical fiberVirologyMaterials scienceMedicineOpticsComposite materialPhysicsAcoustics

Abstract

fetched live from OpenAlex

Since the norovirus is the main cause of acute gastroenteritis all over the world, its fast detection is crucial in medical diagnostics. In this work, a rapid, sensitive, and selective optical fiber biosensor for the detection of norovirus virus-like particles (VLPs) is reported. The sensor is based on highly sensitive long-period fiber gratings (LPFGs) coated with antibodies against the main coat protein of the norovirus. Several modification methods were verified to obtain reliable immobilization of protein receptors on the LPFG surface. We were able to detect 1 ng/mL norovirus VLPs in a 40-min assay in a label-free manner. Thanks to the application of an optical fiber as the sensor, there is a possibility to increase the user's safety by separating the measurement point from the signal processing setup. Moreover, our sensor is small and light, and the proposed assay is straightforward. The designed LPFG-based biosensor could be applied in both fast norovirus detection and in vaccine testing.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.044
GPT teacher head0.318
Teacher spread0.274 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations36
Published2020
Admission routes1
Has abstractyes

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