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Record W2909452100 · doi:10.1111/nan.12540

Short Review: Investigating ARSACS: models for understanding cerebellar degeneration

2019· review· en· W2909452100 on OpenAlexaboutno aff
Ana Artero‐Castro, Candela Machuca, Francisco Javier Rodríguez Jiménez, Pavla Jendelová, Slaven Erceg

Bibliographic record

VenueNeuropathology and Applied Neurobiology · 2019
Typereview
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersInstituto de Salud Carlos III
KeywordsInduced pluripotent stem cellNeuroscienceCerebellar ataxiaBiologyDiseaseMutationCerebellumDrug discoveryAtaxiaMedicinePathologyGeneBioinformaticsGeneticsEmbryonic stem cell

Abstract

fetched live from OpenAlex

Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is an early-onset neurodegenerative disease that includes progressive cerebellar dysfunction. ARSACS is caused by an autosomal recessive loss-of-function mutation in the SACS gene, which encodes for SACSIN. Although animal models are still necessary to investigate the role of SACSIN in the pathology of this disease, more reliable human cellular models need to be generated to better understand the cerebellar pathophysiology of ARSACS. The discovery of human induced pluripotent stem cells (hiPSC) has permitted the derivation of patient-specific cells. These cells have an unlimited self-renewing capacity and the ability to differentiate into different neural cell types, allowing studies of disease mechanism, drug discovery and cell replacement therapies. In this study, we discuss how the hiPSC-derived cerebellar organoid culture offers novel strategies for targeting the pathogenic mutations related to ARSACS. We also highlight the advantages and challenges of this 3D cellular model, as well as the questions that still remain unanswered.

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)0.001
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 score0.872
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.275
GPT teacher head0.354
Teacher spread0.079 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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