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Noonan syndrome and neurofibromatosis type I in a family with a novel mutation in <i>NF1</i>

2009· article· en· W1972964891 on OpenAlexaff
AM Nyström, Sara Ekvall, Judith Allanson, Christina Edeby, M Elinder, Gerd Holmström, ML Bondeson, Göran Annerén

Bibliographic record

VenueClinical Genetics · 2009
Typearticle
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsNoonan syndromeNeurofibromatosisPTPN11Short statureMissense mutationNeurofibromin 1Costello syndromeGeneticsMutationNeurofibromatosis type IExonMedicineEtiologyBiologyPathologyGeneInternal medicineKRAS

Abstract

fetched live from OpenAlex

Noonan syndrome (NS) and neurofibromatosis type I (NF1) belong to a group of clinically related disorders that share a common pathogenesis, dysregulation of the RAS-MAPK pathway. NS is characterized by short stature, heart defect, pectus deformity and facial dysmorphism, whereas skin manifestations, skeletal defects, Lisch nodules and neurofibromas are characteristic of NF1. Both disorders display considerable clinical variability. Features of NS have been observed in individuals with NF1 -a condition known as neurofibromatosis-Noonan syndrome (NFNS). The major gene causing NFNS is NF1. Rarely, a mutation in PTPN11 in addition to an NF1 mutation is present. We present the clinical and molecular characterization of a family displaying features of both NS and NF1, with complete absence of neurofibromas. To investigate the etiology of the phenotype, mutational analysis of NF1 was conducted, revealing a novel missense mutation in exon 24, p.L1390F, affecting the GAP-domain. Additional RAS-MAPK pathway genes were examined, but no additional mutations were identified. We confirm that NF1 mutations are involved in the etiology of NFNS. Furthermore, based on our results and previous studies we suggest that evaluation of the GAP-domain of NF1 should be prioritized in NFNS.

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 score0.036
Threshold uncertainty score0.490

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 head0.052
GPT teacher head0.329
Teacher spread0.278 · 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

Citations39
Published2009
Admission routes1
Has abstractyes

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