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Record W2076804758 · doi:10.1371/journal.pone.0036590

Identification of a Known Mutation in Notch 3 in Familiar CADASIL in China

2012· article· en· W2076804758 on OpenAlexaff
Zhen-Xuan Tan, Fei-Feng Li, Youyang Qu, Ji Liu, Guirong Liu, Jin Zhou, Yulan Zhu, Shu‐Lin Liu

Bibliographic record

VenuePLoS ONE · 2012
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and genetic disorders
Canadian institutionsUniversity of Calgary
FundersSpecialized Research Fund for the Doctoral Program of Higher Education of ChinaNatural Science Foundation of Heilongjiang ProvinceNational Natural Science Foundation of ChinaHarbin Medical University
KeywordsCADASILLeukoencephalopathyPenetranceMutationGeneticsDiseaseDementiaMedicineBiologyPathologyBioinformaticsGenePhenotype

Abstract

fetched live from OpenAlex

BACKGROUND: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an inherited disease leading to recurrent ischemic stroke and vascular dementia. Numerous mutations in the 23 exons of the NOTCH3 gene have been reported to cause CADASIL in Caucasian populations, but the full spectrum of genetic changes leading to this disease is yet to be known and, especially, very few reports are available on CADASIL in Asian populations. METHODS AND RESULTS: We genotyped members of a 5-generational Han Chinese family with CADASIL patients and identified an R133C mutation in the NOTCH3 gene. Clinical analysis demonstrated that the penetrance of the mutation was not complete. Five of the mutation carriers, not exposed to the known vascular risk factors, did not show any clinical feature of CADASIL, suggesting the importance of environmental factors to the development of this disease. CONCLUSIONS: Members of a 5-generational Han Chinese family with CADASIL patients had an R133C mutation in the NOTCH3 gene but only individuals exposed to known vascular risk factors developed CADASIL.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.248
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations27
Published2012
Admission routes1
Has abstractyes

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