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Record W2802748162 · doi:10.1111/cge.13370

Refining the phenotype associated with biallelic <i>DNAJC21</i> mutations

2018· article· en· W2802748162 on OpenAlexaff
Guylaine D’Amours, Fátima Lopes, J Gauthier, Virginie Saillour, Christina Nassif, Robert Wynn, Nathalie Alos, Thierry Leblanc, Yline Capri, Sonia Nizard, Emmanuelle Lemyre, Jacques L. Michaud, Véronique Pelletier, Yves Pastore, J.‐F. Soucy

Bibliographic record

VenueClinical Genetics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBlood disorders and treatments
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersFundação para a Ciência e a Tecnologia
KeywordsDyskeratosis congenitaFailure to thrivePenetranceBone marrow failureDiamond–Blackfan anemiaPhenotypeMedicineExpressivityGeneticsBioinformaticsBiologyPathologyGene

Abstract

fetched live from OpenAlex

Inherited bone marrow failure syndromes (IBMFS) are caused by mutations in genes involved in genomic stability. Although they may be recognized by the association of typical clinical features, variable penetrance and expressivity are common, and clinical diagnosis is often challenging. DNAJC21, which is involved in ribosome biogenesis, was recently linked to bone marrow failure. However, the specific phenotype and natural history remain to be defined. We correlate molecular data, phenotype, and clinical history of 5 unreported affected children and all individuals reported in the literature. All patients present features consistent with IBMFS: bone marrow failure, growth retardation, failure to thrive, developmental delay, recurrent infections, and skin, teeth or hair abnormalities. Additional features present in some individuals include retinal abnormalities, pancreatic insufficiency, liver cirrhosis, skeletal abnormalities, congenital hip dysplasia, joint hypermobility, and cryptorchidism. We suggest that DNAJC21-related diseases constitute a distinct IBMFS, with features overlapping Shwachman-Diamond syndrome and Dyskeratosis congenita, and additional characteristics that are specific to DNAJC21 mutations. The full phenotypic spectrum, natural history, and optimal management will require more reports. Considering the aplastic anemia, the possible increased risk for leukemia, and the multisystemic features, we provide a checklist for clinical evaluation at diagnosis and regular follow-up.

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 score0.349
Threshold uncertainty score0.304

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 head0.040
GPT teacher head0.334
Teacher spread0.294 · 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

Citations40
Published2018
Admission routes1
Has abstractyes

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