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Record W3131025579 · doi:10.1111/cge.13948

Heterozygous <scp><i>NOTCH1</i></scp> deletion associated with variable congenital heart defects

2021· article· en· W3131025579 on OpenAlexaff
Maian Roifman, Brian Hon‐Yin Chung, Diane Reid, Ronni Teitelbaum, Nicole Martin, Lynne E. Nield, Megan Thompson, Patrick Shannon, David Chitayat

Bibliographic record

VenueClinical Genetics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids FoundationMount Sinai Hospital
Fundersnot available
KeywordsPenetranceHaploinsufficiencyVariable ExpressionExpressivityBicuspid aortic valveBiologyGeneticsPhenotypeGeneGene dosageGene expressionInternal medicineMedicineAortic valve

Abstract

fetched live from OpenAlex

Pathogenic heterozygous variants in the NOTCH1 gene are known to be associated with both left and right-sided congenital cardiac anomalies with strikingly incomplete penetrance and variable phenotypic expressivity. De novo NOTCH1 whole gene deletion has been reported rarely in the literature and its association with cardiac defects is less well established. Here, we report four cases of NOTCH1 gene deletion from two families associated with a spectrum of congenital heart defects from bicuspid aortic valve to complex cardiac anomalies. This is the first description of a familial NOTCH1 deletion, showing apparently high penetrance, which may be unique to this mechanism of disease. Immunohistochemical staining of cardiac tissue demonstrated reduced levels of NOTCH1 expression in both the left and right ventricular outflow tracts. These cases suggest that haploinsufficiency caused by NOTCH1 gene deletion is associated with both mild and severe cardiac defects, similar to those caused by pathogenic variants in the gene, but with apparently higher, if not complete, penetrance.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.325
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 source (direct Gemma or distilled Codex), 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

Citations15
Published2021
Admission routes1
Has abstractyes

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