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Record W4386499139 · doi:10.1117/12.2692199

The application of loop-mediated isothermal amplification (LAMP) in the rapid detection of banana allergen

2023· article· en· W4386499139 on OpenAlexaff
Shangzhi Hu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsLoop-mediated isothermal amplificationAllergenFood allergensDNASensitivity (control systems)BiologyAllergyGeneticsImmunology

Abstract

fetched live from OpenAlex

Bananas (Musa acuminata) have been recognized as a common cause of food allergies worldwide. To avoid allergic reactions in patients with allergies, it is important to avoid eating bananas. Therefore, sensitive and specific banana detection methods are needed to verify bananas in food. Loop-mediated isothermal amplification (LAMP) is a fast and simple DNA-based detection method, which is mainly suitable for field application or on-site analysis and screening of allergens in food production. This work describes the systematic development and selection of LAMP primers based on multiple copy genes of banana. The chemical method used allows the detection of amplified DNA using either direct observation of precipitated products or gel electrophoresis. LAMP based on AJ277278 gene is highly specific for bananas, allowing detection sensitivity of approximately sensitivity of 3.9 × 10<sup>−3</sup> ng/μL DNA extracting solution. Different banana products can be detected at the same sensitivity level. LAMP enables simple, highly specific and sensitive detection of bananas without the need for expensive analytical equipment.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.135

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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