Amyloid is identified microscopically as an amorphous extracellular hyaline material that exhibits "apple-green" birefringence with Congo red stains. Amyloid is not a chemically distinct entity, and currently available molecular methods are capable of identifying over 20 amyloidogenic precursor proteins. Some of the more common diseases associated with amyloidosis include plasma cell dyscrasias, chronic inflammatory disorders, hereditary-familial mutations involving transthyretin, Alzheimer's disease, and so-called "senile" or age-related amyloidosis. The amyloid deposits in these various diseases may be isolated to a single organ such as the heart or brain, or the amyloidosis may be systemic. The senile types of cardiac amyloidosis can result from overproduction of atrial natriuretic factor or from accumulation of otherwise normal or wild-type transthyretin. We present the case of an 83-year-old hospitalized woman with known atrial fibrillation and previous pacemaker implantation who had cardiac arrest unresponsive to attempted resuscitation. Autopsy disclosed prominent amyloidosis involving the left atrium, and subsequent molecular studies identified the amyloidogenic material as alpha atrial natriuretic factor. Since the clinical management and genetic implications of the various diseases associated with amyloidosis are markedly different, we stress the importance of molecular classification whenever possible.
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http://dx.doi.org/10.1080/08998280.2013.11929013 | DOI Listing |
J Cheminform
January 2025
School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.
View Article and Find Full Text PDFNat Cancer
January 2025
Dept. of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.
The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility.
View Article and Find Full Text PDFGigascience
January 2025
State Key Laboratory of Developmental Biology of Freshwater Fish, Engineering Research Center of Polyploid Fish Reproduction and Breeding of the State Education Ministry, College of Life Sciences, Hunan Normal University, Changsha 410081, China.
Background: Genomic data have unveiled a fascinating aspect of the evolutionary past, showing that the mingling of different species through hybridization has left its mark on the histories of numerous life forms. However, the relationship between hybridization events and the origins of cyprinid fishes remains unclear.
Results: In this study, we generated de novo assembled genomes of 8 cyprinid fishes and conducted phylogenetic analyses on 24 species.
Annu Rev Biomed Data Sci
January 2025
1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA;
Cancer remains a leading cause of death globally. The complexity and diversity of cancer-related datasets across different specialties pose challenges in refining precision medicine for oncology. Foundation models offer a promising solution.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
European Molecular Biology Laboratory, Cell Biology and Biophysics Unit, Heidelberg, Germany.
The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data.
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