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Record W4220869538 · doi:10.3389/fneur.2022.845318

A Novel SACS Variant Identified in a Chinese Patient: Case Report and Review of the Literature

2022· article· en· W4220869538 on OpenAlexaboutno aff
Yuchao Chen, Xiaodong Lü, Yi Jin, Dan Li, Xiaojun Ye, Chenjuan Tao, Menglu Zhou, Haibo Jiang, Hao Yu

Bibliographic record

VenueFrontiers in Neurology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
FundersScience and Technology Program of Zhejiang ProvinceMedical Science and Technology Project of Zhejiang Province
KeywordsAtaxiaSpasticityCerebellar ataxiaMutationAtrophyPeripheral neuropathyMedicineHereditary spastic paraplegiaGenetic heterogeneityPathologyGeneticsNeurosciencePhenotypeGeneBiologyPhysical medicine and rehabilitationEndocrinology

Abstract

fetched live from OpenAlex

Mutations in the SACS gene have been linked to autosomal recessive spastic ataxia of Charlevoix Saguenay (ARSACS). It is a clinically and genetically heterogeneous disease characterized by slow progressive ataxia, spasticity, sensorimotor neuropathy, and a combination of other manifestations, such as lack of spasticity, hearing loss, and epileptic seizures. Currently, there have been very few case reports regarding the SACS gene mutation in Chinese patients. Here, we describe a 35-year-old Chinese patient carrying a novel variant in SACS (c.11486C>T) presenting with progressive ataxia and demyelinating peripheral neuropathy. We then reviewed 22 Chinese cases carrying SACS gene mutations, including our patient. All of them had a cerebellar ataxia gait and showed cerebellar atrophy on brain magnetic resonance imaging (MRI). A total of 28 SACS mutations were identified in these patients. Our study further expands the mutation spectrum of the SACS gene and contributes to the evaluation of genotype-phenotype correlations.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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 designCase report
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

Citations5
Published2022
Admission routes1
Has abstractyes

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