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Autosomal Recessive Infantile Hyaline Fibromatosis Identified Using Artificial Intelligence-Assisted Rapid Whole Genome Sequencing: A Rare, Multisystemic, Hereditary Disorder.
Ye, George X; Ontiveros, Eric; Ivander, Axel; Velinov, Milen; Simotas, Christopher.
Affiliation
  • Ye GX; Pediatrics, Rutgers Cancer Institute of New Jersey, New Brunswick, USA.
  • Ontiveros E; Pediatrics, Rutgers Robert Wood Johnson Medical School, New Brunswick, USA.
  • Ivander A; Clinical Genomics, Rady Children's Hospital, San Diego, USA.
  • Velinov M; Pediatrics, Robert Wood Johnson University Hospital, New Brunswick, USA.
  • Simotas C; Pediatrics, Robert Wood Johnson University Hospital, New Brunswick, USA.
Cureus ; 16(6): e62037, 2024 Jun.
Article in En | MEDLINE | ID: mdl-38989346
ABSTRACT
Infantile hyaline fibromatosis syndrome (HFS) is an ultra-rare genetic condition characterized by the deposition of hyaline material in the skin, muscle, and viscera. Potential complications include debilitating joint contractures, coarse facial features, recurrent infections, failure to thrive, and death. Here, we present the case of a six-month-old infant with a history of painful extremity contractures, global developmental delay, neck hemangioma, and feeding intolerance presenting to our institution with abdominal distension. The multi-systemic, rapidly progressing, severe nature of her symptoms prompted consultation with inpatient pediatric genetics. Per their recommendation, rapid whole-genome sequencing (rWGS) was done with Fabric GEM®-assisted artificial intelligence (Fabric Genomics, Oakland, California, United States) at Rady Children's Hospital Institute for Genomic Medicine (San Diego, California, United States), revealing homozygous pathogenic variant c.652T>C; P.Cys218Arg in the ANTXR2 gene consistent with HFS. This case was significant not only for its rarity, but also its early manifestation of symptoms, wide range of affected body systems, and severity of symptoms, which together present a fascinating diagnostic dilemma for future clinicians that should be taken into consideration. It also highlights the increasing utility of AI-assisted rWGS as a diagnostic tool for medically complex patients with unknown multisystemic hereditary conditions.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Cureus Year: 2024 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Cureus Year: 2024 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos