Publications by authors named "J Michael Chong"

Introduction: Autosomal dominant polycystic kidney disease (ADPKD) is a well-described condition in which approximately 80% of all cases have a genetic explanation; and among sporadic cases without a family history, the genetic bases remain unclear in approximately 30% of cases. This study aimed to identify genes associated with polycystic kidney disease (PKD) in patients with sporadic cystic kidney disease in which a clear genetic change was not identified in established genes.

Methods: A next-generation sequencing panel analyzed known genes related to kidney cysts in 118 sporadic cases, followed by whole-genome sequencing (WGS) on 47 unrelated individuals without identified candidate variants.

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A 27-year-old female with a history of acute lymphoblastic leukemia in remission presented with chest pain, liver cirrhosis, and a thrombus in the hepatic vein on ultrasound. Further workup with computed tomography (CT) and magnetic resonance imaging (MRI) revealed a mass extending from the inferior vena cava to the right atrium, 3.4 x 3.

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Background: Dietary therapy strategies play an important role in the treatment of pediatric patients with Crohn's disease (CD), but the relative efficacy of different dietary therapy strategies for Crohn's remission is unknown. This study aims to compare the effectiveness and tolerance of these dietary therapy strategies for active pediatric CD.

Methods: We searched the medical literature up to August 30, 2024 to identify randomized controlled trials (RCTs) of dietary therapy strategies for pediatric CD.

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Purpose: The Clinical Genome Resource (ClinGen) Gene Curation Expert Panels (GCEPs) have historically focused on specific organ systems or phenotypes; thus, the ClinGen Syndromic Disorders GCEP (SD-GCEP) was formed to address an unmet need.

Methods: The SD-GCEP applied ClinGen's framework to evaluate the clinical validity of genes associated with rare syndromic disorders. 111 Gene-Disease Relationships (GDRs) associated with 100 genes spanning the clinical spectrum of syndromic disorders were curated.

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Brain age is a powerful marker of brain health. Furthermore, brain age models are trained on large datasets, thus giving them a potential advantage in predicting outcomes - much like the success of finetuning large language models for specific applications. However, it is also well-accepted in machine learning that models trained to directly predict specific outcomes (i.

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