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Record W4319027648 · doi:10.3390/toxins15020120

Urinary and Serum Concentration of Deoxynivalenol (DON) and DON Metabolites as an Indicator of DON Contamination in Swine Diets

2023· article· en· W4319027648 on OpenAlexafffund
Josiane C Panisson, Michael O Wellington, Michael A Bosompem, Veronika Nagl, Heidi Schwartz, Daniel A Columbus

Bibliographic record

VenueToxins · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsGenome PrairieUniversity of Saskatchewan
FundersMitacsBiominMinistry of Agriculture - Saskatchewan
KeywordsContaminationUrinary systemMycotoxinVomitoxinUrineChemistryFood contaminantFood scienceChromatographyEnvironmental chemistryBiologyBiochemistryEndocrinologyZearalenoneEcology

Abstract

fetched live from OpenAlex

Pig health is impaired and growth performance is reduced when exposed to deoxynivalenol (DON). The measurement of DON in individual feedstuffs and complete swine diets is variable because of the inconsistent distribution of mycotoxins in feed and the difficulties in obtaining representative samples. We investigated whether measuring DON and its metabolites in biological samples could be used as a predictor of DON ingestion by pigs. Blood samples were collected between 3 and 4 h after the morning meal and urine samples were quantitatively collected over a 24 h period on d 40 and 82 of the study to evaluate serum and urinary content of DON and DON metabolites (iso-deoxynivalenol, DON-3-glucuronide, DON-15-glcurunide, deepoxy-deoxynivalenol, iso-deepoxy-deoxynivalenol, deepoxy-deoxynivalenol-3-glucuronide, and deepoxy-deoxynivalenol-15-glucuronide). The intake of DON was positively correlated with urinary DON output. Similarly, there was an increase in serum DON level with increasing DON intake. Overall, it was found that DON intake correlated with DON concentration in urine and blood serum when samples were collected under controlled conditions. Analyzing DON levels in urine and blood serum could be used to predict a pig's DON intake.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.156

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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 designObservational
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

Citations13
Published2023
Admission routes2
Has abstractyes

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