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Aptamers for safety and quality assurance in the food industry: detection of pathogens

2011· article· en· W1528469357 on OpenAlexfundno aff
Riikka M. Kärkkäinen, Mette Ryun Drasbek, Ian L. McDowall, Christopher J. Smith, Niall W.G. Young, Graham Bonwick

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
FundersUniversity of ChesterMcMaster University
KeywordsAptamerFood safetyBiochemical engineeringComputational biologySystematic evolution of ligands by exponential enrichmentBiotechnologyConsumer safetyNanotechnologyBiologyRisk analysis (engineering)BusinessRNABiochemistryMolecular biologyEngineeringFood science

Abstract

fetched live from OpenAlex

Summary Aptamers are biomolecular ligands composed of nucleic acids. They can be developed to bind specifically to a range of target molecules and subsequently exploited in a fashion analogous to more traditional biomolecules such as antibodies. Methods for the production of aptamers and their potential applications to the food industry in the form of rapid assays and biosensors are discussed. In contrast to antibody‐based diagnostics, aptamers can be produced in animal‐free systems which have clear ethical and financial benefits. This review identifies a need for the development of new aptamers with specificity against micro‐organisms and highlights their potential use for the detection of food‐borne pathogens.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.313
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2011
Admission routes1
Has abstractyes

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