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Record W4367175187 · doi:10.1002/ajmg.a.63223

De novo <scp><i>RANBP2</i></scp> variant in a fetal demise case with cerebral intraparenchymal hemorrhage

2023· article· en· W4367175187 on OpenAlexaff
Anna Meroni, Silvia Kalantari, Alessia Arossa, Arsenio Spinillo, Chiara Melito, Annachiara Licia Scatigno, Stefania Cesari, Elisa Giorgio, Milena Furione, Tessa Homfray, Fabio Sirchia

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2023
Typearticle
Languageen
FieldMedicine
TopicInfectious Encephalopathies and Encephalitis
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsFetusMedicineIntraparenchymal hemorrhageEncephalopathyAutopsyIntracerebral hemorrhagePathologyInternal medicinePregnancyBiologyGenetics

Abstract

fetched live from OpenAlex

Fetal intracranial hemorrhage (ICH) may result from a wide array of causes, either associated with maternal or fetal risk factors. In the last decade, monogenic causes of susceptibility to fetal ICH have been described, in particular in association with COL4A1 and COL4A2 genes. A peculiar form of ICH is acute necrotizing encephalitis (ANE), which is characterized by a rapid-onset severe encephalopathy following an abnormal inflammatory response to an otherwise banal infection. It usually affects healthy children and it is thought to be multifactorial, with a genetic predisposition. RANBP2 gene has been extensively associated with ANE susceptibility. We hereby present a unique case of a 42-year-old secundigravida with intrauterine fetal demise at 35 weeks of gestation. Trio-based whole-exome sequencing performed on both parents and fetal DNA showed a de novo likely pathogenic variant in the RANBP2 gene on 2q13. At the fetal autopsy, subtentorial hematoma and cerebral intraparenchymal hemorrhage were present. We speculate that this might be a new phenotypic presentation of RANBP2-associated disease. However, more similar fetal cases need to be reported in order to reinforce this hypothesis.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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 designCase report
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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