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Causation for inherited equine night blindness

2010· article· en· W165384661 on OpenAlexaffabout
Michelle Lynn Scott, George W. Forsyth, Lynne S Sandmeyer, Rebecca R. Bellone, Matthew E. Loewen, Bruce H Grahn

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiologyGeneticsZebrafishRetinaCell biologyMolecular biologyGeneNeuroscience

Abstract

fetched live from OpenAlex

Mutations associated with equine night blindness (CSNB) have been mapped to the Transient Receptor Potential cation channel family Member 1 (TRPM1) gene on ECA1. Reduced TRPM1 mRNA expression has been reported in retina and melanocytes in CSNB horses. A single unique intronic snp has been found in all CSNB horses examined to date. We hypothesize that this unique snp creates a binding site for the Nova intronic splicing enhancer which promotes aberrant splicing of an 80 bp downstream acceptor, therefore targeting TRPM1 mRNA for degradation. Nova‐1 and TRPM1 mRNA levels were measured in retina and in primary melanocyte cell lines established from the choroid of normal and CSNB horses. TRPM1 mRNA level in retina from a CSNB horse was 0.2% of that in the control horse retina. Choroidal melanocyes from normal and CSNB horses both had low TRPM1 mRNA levels similar to those observed in CSNB retina. We are investigating whether similar levels of TRPM1 mRNA in CSNB and normal melanocytes are due to removal of the effect of some upstream enhancer element in tissue culture, or result from insufficient Nova‐1 expression to cause the destabilizing TRPM1 mRNA splice event in CSNB melanocytes. Supported by the WCVM Canadian Vitamins Class Action Settlement Research Grant.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0160.001

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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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
Published2010
Admission routes2
Has abstractyes

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