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| http://dx.doi.org/10.1310/hpj5005-345 | DOI Listing |
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Testing for myelin oligodendrocyte glycoprotein antibodies: Who, what, where, when, why, and how.
Mult Scler
January 2025
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Testing for myelin oligodendrocyte glycoprotein immunoglobulin G antibodies (MOG-IgG) is essential to the diagnosis of MOG antibody-associated disease (MOGAD). Due to its central role in the evaluation of suspected inflammatory demyelinating disease, the last 5 years has been marked by an abundance of research into MOG-IgG testing ranging from appropriate patient selection, to assay performance, to utility of serum titers as well as cerebrospinal fluid (CSF) testing. In this review, we synthesize current knowledge pertaining to the "who, what, where, when, why, and how" of MOG-IgG testing, with the aim of facilitating accurate MOGAD diagnosis in clinical practice.
View Article and Find Full Text PDFIncidence and Impact of Myocarditis in Genetic Cardiomyopathies: Inflammation as a Potential Therapeutic Target.
Genes (Basel)
January 2025
Institute of Clinical Medicine, V.N. Vinogradov Faculty Therapeutic Clinic, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia.
Background: Myocardial disease is an important component of the wide field of cardiovascular disease. However, the phenomenon of multiple myocardial diseases in a single patient remains understudied.
Aim: To investigate the prevalence and impact of myocarditis in patients with genetic cardiomyopathies and to evaluate the outcomes of myocarditis treatment in the context of cardiomyopathies.
Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity.
J Pediatr Surg
January 2025
McGill University Faculty of Medicine and Health Sciences, Canada; Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Qc, Canada.
Purpose: This study evaluates the effectiveness of machine learning (ML) algorithms for improving the preoperative diagnosis of acute appendicitis in children, focusing on the accurate prediction of the severity of disease.
Methods: An anonymized clinical and operative dataset was retrieved from the medical records of children undergoing emergency appendectomy between 2014 and 2021. We developed an ML pipeline that pre-processed the dataset and developed algorithms to predict 5 appendicitis grades (1 - non-perforated, 2 - localized perforation, 3 - abscess, 4 - generalized peritonitis, and 5 - generalized peritonitis with abscess).
Industrial air emissions and breast cancer incidence in a United States-wide prospective cohort.
Epidemiology
January 2025
Epidemiology Branch, Division of Intramural Research and Training, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Background: We evaluated air emissions of industrial compounds, many of which have carcinogenic or endocrine disrupting properties, in relation to breast cancer incidence.
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Oncology Provider and Patient Perspectives on a Cardiovascular Health Assessment Tool utilized during Post-treatment Survivorship Care in Community Oncology (Results from WF-1804CD): Mixed Methods Observational Study.
J Med Internet Res
January 2025
Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, US.
Background: Most cancer survivors have multiple cardiovascular risk factors, increasing their risk of poor cardiovascular and cancer outcomes. The Automated Heart-Health Assessment (AH-HA) tool is a novel electronic health record clinical decision support tool based on the American Heart Association's Life's Simple 7 cardiovascular health (CVH) metrics to promote CVH assessment and discussion in outpatient oncology. Before proceeding to future implementation trials, it is critical to establish the acceptability of the tool among providers and survivors.
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