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Record W7074041917

Investigation of Genetic Modifiers of Copper Toxicosis in Labrador Retrievers

2020· article· en· W7074041917 on OpenAlexaboutno aff

Bibliographic record

VenueUtrecht University Repository (Utrecht University) · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCopperAlleleGeneCopper deficiencyFatty liverCopper toxicityMutationCohort
DOInot available

Abstract

fetched live from OpenAlex

Copper toxicosis is a complex genetic disorder in Labrador retrievers characterized by hepatic copper accumulation eventually leading to liver cirrhosis. The variation of hepatic copper levels in Labrador retrievers has been partly explained by mutations in ATP7A c.980C>T and ATP7B c.4358G>A. To further elucidate the genetic background of this disease, we used targeted Next Generation Sequencing (NGS) in a cohort of 95 Labrador retrievers to analyze 72 potential modifier genes for variations associated with hepatic copper levels. Variants associated with copper levels were subsequently evaluated in a replication cohort of 144 Labrador retrievers. A total of 44 variants in 25 different genes were identified, of which four showed significant association with copper levels. Of the four variants found associated with hepatic copper levels in the NGS cohort, one was validated in the replication cohort. The non-reference allele of the variant NC_006602.3.g.52434480C>T in RETN resulting in amino-acid change p.Leu7Phe was associated with decreased hepatic copper levels. In humans, resistin is associated with severity of non-alcoholic fatty liver disease, fibrosis, cirrhosis and mitochondrial dysfunction in hepatocytes. Further studies are needed to investigate the biological function of RETN p.Leu7Phe in the development of copper toxicosis in Labrador retrievers.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.016
GPT teacher head0.145
Teacher spread0.129 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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