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Record W2912706849 · doi:10.1002/hep.30515

Identification of Polycystic Kidney Disease 1 Like 1 Gene Variants in Children With Biliary Atresia Splenic Malformation Syndrome

2019· article· en· W2912706849 on OpenAlexafffund
John‐Paul Berauer, Anya Mezina, David T. Okou, Aniko Sabo, Donna M. Muzny, Richard A. Gibbs, Madhuri Hegde, Pankaj Chopra, David J. Cutler, David H. Perlmutter, Laura N. Bull, Richard J. Thompson, Kathleen M. Loomes, Nancy B. Spinner, Ramakrishnan Rajagopalan, Stephen L. Guthery, Barry Moore, Mark Yandell, Sanjiv Harpavat, John C. Magee, Binita M. Kamath, Jean P. Molleston, Jorge A. Bezerra, Karen F. Murray, Estella M. Alonso, Philip Rosenthal, Robert H. Squires, Kasper S. Wang, Milton J. Finegold, Pierre Russo, Averell H. Sherker, Ronald J. Sokol, Saul J. Karpen

Bibliographic record

VenueHepatology · 2019
Typearticle
Languageen
FieldMedicine
TopicPediatric Hepatobiliary Diseases and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institutes of HealthHospital for Sick ChildrenChildren's Healthcare of AtlantaNational Center for Research ResourcesGeorgia Clinical and Translational Science AllianceSeattle Children's Research InstituteAmerican Gastroenterological AssociationJohns Hopkins Children's CenterNational Institute of Diabetes and Digestive and Kidney DiseasesJohns Hopkins UniversityChildren's Hospital of PittsburghNational Center for Advancing Translational SciencesNational Human Genome Research InstituteChildren's Hospital ColoradoTexas Children's HospitalCincinnati Children's Hospital Medical CenterEmory UniversitySchool of Medicine, Emory UniversityUniversity of MichiganUniversity of California, San FranciscoChildren's Hospital of Philadelphia
KeywordsBiliary atresiaPolydactylyBiologyAlagille syndromePopulationExome sequencingPolycystic kidney diseaseLiver diseaseSitus inversusPathologyPolyspleniaInternal medicineGastroenterologyMedicineKidneyLiver transplantationEndocrinologyCholestasisTransplantationGeneticsMutationGene

Abstract

fetched live from OpenAlex

Biliary atresia (BA) is the most common cause of end-stage liver disease in children and the primary indication for pediatric liver transplantation, yet underlying etiologies remain unknown. Approximately 10% of infants affected by BA exhibit various laterality defects (heterotaxy) including splenic abnormalities and complex cardiac malformations-a distinctive subgroup commonly referred to as the biliary atresia splenic malformation (BASM) syndrome. We hypothesized that genetic factors linking laterality features with the etiopathogenesis of BA in BASM patients could be identified through whole-exome sequencing (WES) of an affected cohort. DNA specimens from 67 BASM subjects, including 58 patient-parent trios, from the National Institute of Diabetes and Digestive and Kidney Diseases-supported Childhood Liver Disease Research Network (ChiLDReN) underwent WES. Candidate gene variants derived from a prespecified set of 2,016 genes associated with ciliary dysgenesis and/or dysfunction or cholestasis were prioritized according to pathogenicity, population frequency, and mode of inheritance. Five BASM subjects harbored rare and potentially deleterious biallelic variants in polycystic kidney disease 1 like 1 (PKD1L1), a gene associated with ciliary calcium signaling and embryonic laterality determination in fish, mice, and humans. Heterozygous PKD1L1 variants were found in 3 additional subjects. Immunohistochemical analysis of liver from the one BASM subject available revealed decreased PKD1L1 expression in bile duct epithelium when compared to normal livers and livers affected by other noncholestatic diseases. Conclusion: WES identified biallelic and heterozygous PKD1L1 variants of interest in 8 BASM subjects from the ChiLDReN data set; the dual roles for PKD1L1 in laterality determination and ciliary function suggest that PKD1L1 is a biologically plausible, cholangiocyte-expressed candidate gene for the BASM syndrome.

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 score0.006
Threshold uncertainty score0.547

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 head0.005
GPT teacher head0.224
Teacher spread0.219 · 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

Citations84
Published2019
Admission routes2
Has abstractyes

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