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Sacs R272C missense homozygous mice develop an ataxia phenotype

2019· other· en· W6977676974 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2019
Typeother
Languageen
FieldSocial Sciences
TopicHealth, Education, and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMissense mutationAtaxiaPhenotypeMutationHeterozygote advantageNeurofilament

Abstract

fetched live from OpenAlex

Abstract Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS [MIM 270550]) is an early-onset neurodegenerative disorder caused by mutations in the SACS gene. Over 200 SACS mutations have been identified. Most mutations lead to a complete loss of a sacsin, a large 520 kD protein, although some missense mutations are associated with low levels of sacsin expression. We previously showed that Sacs knock-out mice demonstrate early-onset ataxic phenotype with neurofilament bundling in many neuronal populations. To determine if the preservation of some mutated sacsin protein resulted in the same cellular and behavioral alterations, we generated mice expressing an R272C missense mutation, a homozygote mutation found in some affected patients. Though SacsR272C mice express 21% of wild type brain sacsin and sacsin is found in many neurons, they display similar abnormalities to Sacs knock-out mice, including the development of an ataxic phenotype, reduced Purkinje cell firing rates, and somatodendritic neurofilament bundles in Purkinje cells and other neurons. Together our results support that Sacs missense mutation largely lead to loss of sacsin function.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.007

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 head0.068
GPT teacher head0.342
Teacher spread0.273 · 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
Published2019
Admission routes1
Has abstractyes

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