Metabolomics refers to the detection and measurement of small molecules (metabolites) within biological systems, and is therefore a powerful tool for identifying dysfunctional cellular physiologies. For pheochromocytomas and paragangliomas (PPGLs), metabolomics has the potential to become a routine addition to histology and genomics for precise diagnostic evaluation. Initial metabolomic studies of tumors confirmed, as expected, succinate accumulation in PPGLs associated with pathogenic variants in genes encoding succinate dehydrogenase subunits or their assembly factors (). Metabolomics has now shown utility in clarifying variants of uncertain significance, as well as the accurate diagnosis of PPGLs associated with fumarate hydratase (), isocitrate dehydrogenase (), malate dehydrogenase () and aspartate transaminase (). The emergence of metabolomics resembles the advent of genetic testing in this field, which began with single-gene discoveries in research laboratories but is now done by standardized massively parallel sequencing (targeted panel/exome/genome testing) in pathology laboratories governed by strict credentialing and governance requirements. In this setting, metabolomics is poised for rapid translation as it can utilize existing infrastructure, namely liquid chromatography-tandem mass spectrometry (LC-MS/MS), for the measurement of catecholamine metabolites. Metabolomics has also proven tractable to diagnosis of SDH-deficient PPGLs using magnetic resonance spectroscopy (MRS). The future of metabolomics - embedded as a diagnostic tool - will require adoption by pathologists to shepherd development of standardized assays and sample preparation, reference ranges, gold standards, and credentialing.
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http://dx.doi.org/10.1055/a-0926-3790 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Biofactors
January 2025
Natural Products and Analytical Chemistry Laboratory, MIGAL - Galilee Research Institute, Kiryat Shemona, Israel.
Atherosclerosis is a major cause of morbidity and mortality worldwide; in Israel, ischemic heart disease is the second leading cause of death for both genders aged 45 and above. Atherosclerosis involves stiffening of the arteries due to the accumulation of lipids and oxidized lipids on the blood vessel walls, triggering the development of artery plaque. Coronary artery disease (CAD) is the most common manifestation of atherosclerosis.
View Article and Find Full Text PDFNat Prod Res
January 2025
Programa de Pós-Graduação em Farmacologia, Universidade Federal de Santa Maria (UFSM), Santa Maria, Brazil.
(L.) R. Br.
View Article and Find Full Text PDFBackground: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients.
View Article and Find Full Text PDFCurr Alzheimer Res
January 2025
Student's Scientific Research Center, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative condition with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism.
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