MétaCan
Menu
Back to cohort
Record W2792332276 · doi:10.1111/jnc.14323

Animal models of Wilson disease

2018· review· en· W2792332276 on OpenAlexaboutno aff
Emily Reed, Svetlana Lutsenko, Oliver Bandmann

Bibliographic record

VenueJournal of Neurochemistry · 2018
Typereview
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of General Medical Sciences
KeywordsZebrafishDiseaseHuman diseaseDanioModel organismPhenotypeBiologyAnimal modelGeneGeneticsBioinformaticsNeuroscienceMedicinePathologyEndocrinology

Abstract

fetched live from OpenAlex

Wilson disease (WD) is an autosomal recessive disorder of copper metabolism manifesting with hepatic, neurological and psychiatric symptoms. The limitations of the currently available therapy for WD (particularly in the management of neuropsychiatric disease), together with our limited understanding of key aspects of this illness (e.g. neurological vs. hepatic presentation) justify the ongoing need to study WD in suitable animal models. Four animal models of WD have been established: the Long-Evans Cinnamon rat, the toxic-milk mouse, the Atp7b knockout mouse and the Labrador retriever. The existing models of WD all show good similarity to human hepatic WD and have been helpful in developing an improved understanding of the human disease. As mammals, the mouse, rat and canine models also benefit from high homology to the human genome. However, important differences exist between these mammalian models and human disease, particularly the absence of a convincing neurological phenotype. This review will first provide an overview of our current knowledge of the orthologous genes encoding ATP7B and the closely related ATP7A protein in C. elegans, Drosophila and zebrafish (Danio rerio) and then summarise key characteristics of rodent and larger mammalian models of ATP7B-deficiency.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.396
Teacher spread0.302 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations55
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of NeurochemistrySame topicTrace Elements in HealthFrench-language works237,207