J Telemed Telecare
December 2024
Telemedicine is comparable in quality to in-person care, adequate for many primary care concerns, acceptable to patients, and can overcome barriers to care. However, patients are reluctant to pay the same for telemedicine as in-person care and uncertainty about future payor reimbursement makes it risky to base a clinical practice primarily on telemedicine. Physical exam-supported information collection and relationship-building are limited in telemedicine, but can be mitigated through remote patient monitoring and ample access to a provider and clinical team.
View Article and Find Full Text PDFImportance: The association of race and detection of pathogenic variants using wide-panel genetic testing for inherited retinal diseases (IRD), to our knowledge, has not been studied previously.
Objective: To investigate the genetic detection rates of wide-panel testing in Black and non-Hispanic White patients with IRDs.
Design, Setting, Participants: This 2-group comparison used retrospective patient data that were collected at the University of Michigan (UM) and Blueprint Genetics (BG).
A hexanucleotide repeat expansion (HRE) in C9ORF72 is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Human brain imaging and experimental studies indicate early changes in brain structure and connectivity in C9-ALS/FTD, even before symptom onset. Because these early disease phenotypes remain incompletely understood, we generated iPSC-derived cerebral organoid models from C9-ALS/FTD patients, presymptomatic C9ORF72-HRE (C9-HRE) carriers, and controls.
View Article and Find Full Text PDFIntermediate-length repeat expansions in ATAXIN-2 (ATXN2) are the strongest genetic risk factor for amyotrophic lateral sclerosis (ALS). At the molecular level, ATXN2 intermediate expansions enhance TDP-43 toxicity and pathology. However, whether this triggers ALS pathogenesis at the cellular and functional level remains unknown.
View Article and Find Full Text PDFOphthalmol Retina
November 2022
Objective: To train a deep learning (DL) algorithm to perform fully automated semantic segmentation of multiple autofluorescence lesion types in Stargardt disease.
Design: Cross-sectional study with retrospective imaging data.
Subjects: The study included 193 images from 193 eyes of 97 patients with Stargardt disease.