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Record W4298003644 · doi:10.3390/toxins14100672

Optimization of Aflatoxin B1-Lysine Analysis for Public Health Exposure Studies

2022· article· en· W4298003644 on OpenAlexafffund
Justin B. Renaud, Jacob P. Walsh, Mark W. Sumarah

Bibliographic record

VenueToxins · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsWestern UniversityAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsAflatoxinDigestion (alchemy)ChemistryCarcinogenAlbuminPronaseSerum albuminFood scienceChromatographyAspergillus flavusMycotoxinAspergillusHuman serum albuminBiochemistryBiologyTrypsinEnzymeBotany

Abstract

fetched live from OpenAlex

Aflatoxin B1 is a potent human carcinogen produced by several species of Aspergillus mainly found on nuts and maize. Exposures in parts of Africa, Latin America and Asia can be at multiples, sometimes orders of magnitude above tolerable daily levels. Although human exposure to aflatoxin can be estimated by analysis of the diet, only determination of the serum albumin aflatoxin adduct provides a health-relevant exposure measure. The lack of a reference serum limits interlaboratory method validation and data comparisons. In this study, we synthetically produced AFB1-dialdehyde and covalently coupled it to serum albumin in human serum. This synthetic produced aflatoxin-serum reference material was used in conjunction with isotopically labelled internal standards to evaluate sample digestion methods. This showed using sufficient Pronase in the digestion step was critical to ensure complete proteolytic digestion, which occurs within 4 h. Increasing the digestion temperature from 37 °C to 50 °C also provided a benefit to the overall analysis. In addition, the use of dried blood spots and Volumetric Absorptive Microsampling (VAMS) were investigated showing samples stored with VAMS produced equivalent results to serum samples.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.281
Teacher spread0.193 · 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 designNot applicable
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

Citations9
Published2022
Admission routes2
Has abstractyes

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