Kunio Kawanishi, Masaki Baba, Ryosuke Kobayashi, Ryotaro Hori, Kentaro Hashikami, Kenta Danbayashi, Takako Iwachido, Mitsuyasu Kat
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引用次数: 0
Abstract
Alport syndrome is a rare kidney disease typically more severe in males due to its X-linked inheritance. However, female patients with heterozygous X-linked Alport syndrome (XLAS) can develop renal failure over time, necessitating accurate pathologic assessment for effective therapy. A key pathologic finding in female patients with XLAS is the mosaic pattern of partial loss of α5 chains of type IV collagen. This study, using a mouse model of XLAS with a nonsense mutation (R471∗) in the Col4a5 gene, analogous to human XLAS, aimed to examine the consistency of this pattern with the glomerular basement membrane (GBM) structure. A modified periodic acid-methenamine silver staining method was developed for clearer GBM visualization. The integrated images from COL4α5-stained fluorescence, periodic acid-methenamine silver, and low-vacuum scanning electron microscopy into a single-slide section and applied supervised deep learning to predict GBM lesions. Results showed significant individual variability in urinary protein levels and histologic lesions. Pathologic parameters, including crescent formation, focal segmental glomerulosclerosis, and the COL4α5/α2 ratio, correlated with clinical parameters like urinary protein and plasma creatinine levels. Integrated low-vacuum scanning electron microscopy analysis revealed dense GBM regions corresponded to areas where COL4α5 was preserved, whereas coarse GBM (basket-weave lesions) occurred in COL4α5-deficient regions. These advanced techniques can enhance biopsy-based diagnosis of Alport syndrome and aid in developing artificial intelligence diagnostic tools for diseases involving basement membrane lesions.
期刊介绍:
The American Journal of Pathology, official journal of the American Society for Investigative Pathology, published by Elsevier, Inc., seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. Foundational studies that incorporate deep learning and artificial intelligence are also welcome. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches.