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Record W4296167821 · doi:10.1016/j.cbpa.2022.102211

Insights into SACS pathological attributes in autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS)☆

2022· review· en· W4296167821 on OpenAlexafffundabout
Khaled A. Aly, Mohamed Taha Moutaoufik, Mara Zilocchi, Sadhna Phanse, Mohan Babu

Bibliographic record

VenueCurrent Opinion in Chemical Biology · 2022
Typereview
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversity of Regina
FundersCanadian Institutes of Health Research
KeywordsBiologyPhenotypeGeneAtaxiaGeneticsComputational biologyNeuroscience

Abstract

fetched live from OpenAlex

Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a rare early-onset neurodegenerative disease caused by mutations in the SACS gene, encoding Sacsin. Initial functional annotation of Sacsin was based on sequence homology, with subsequent experiments revealing the Sacsin requirement for regulating mitochondrial dynamics, along with its domains involved in promoting neurofilament assembly or resolving their bundling accumulations. ARSACS phenotypes associated with SACS loss-of-function are discussed, and how advancements in ARSACS disease models and quantitative omics approaches can improve our understanding of ARSACS pathological attributes. Lastly in the perspectives section, we address gene correction strategies for monogenic disorders such as ARSACS, along with their common delivery methods, representing a hopeful area for ARSACS therapeutics development.

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)0.004
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 score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.165
GPT teacher head0.392
Teacher spread0.227 · 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

Citations22
Published2022
Admission routes3
Has abstractyes

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