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<i>DHCR7</i> mutations and genotype–phenotype correlation in 37 Polish patients with Smith–Lemli–Opitz syndrome

2004· article· en· W2126702301 on OpenAlexaff
Elżbieta Ciara, MJM Nowaczyk, Martina Witsch‐Baumgartner, E Małunowicz, Ewa Popowska, Aleksandra Jezela‐Stanek, Małgorzata Piotrowicz, JS Waye, Gerd Utermann, Małgorzata Krajewska‐Walasek

Bibliographic record

VenueClinical Genetics · 2004
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSmith–Lemli–Opitz syndromeMissense mutationPhenotypeGeneticsCompound heterozygosityAlleleGenotypeMutationBiologyHeterozygote advantageGeneReductase7-Dehydrocholesterol reductaseEnzyme

Abstract

fetched live from OpenAlex

Smith-Lemli-Opitz syndrome (SLOS) is an autosomal recessive disorder of cholesterol biosynthesis caused by mutations in the DHCR7 gene. Thirty-seven ethnic Polish patients with SLOS underwent mutation analysis. The mutation frequencies in Polish patients were significantly different from those observed in Western European populations. Two mutations, W151X (22/68 alleles, 32%) and V326L (19/68 alleles, 28%), accounted for 60% of all observed in our cohort. Two missense mutations L68P and L360P have not been reported previously. In total, we report 15 DHCR7 mutations identified in Polish patients. By comparing clinical severity scores and the biochemical and molecular data, a genotype-phenotype correlation was attempted. In compound heterozygotes with one null mutation, the phenotype severity depends on the localization and type of the second mutation: mild phenotypes are correlated with mutations affecting the putative transmembrane domains TM1-TM6 or CT regions and severe phenotypes with mutations localized in TM7 and 4L region. The phenotypic differences of patients with the same genotype suggest that severity of the disease may be affected by other factors.

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 score0.012
Threshold uncertainty score0.500

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 head0.018
GPT teacher head0.294
Teacher spread0.276 · 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

Citations29
Published2004
Admission routes1
Has abstractyes

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