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一种使用扩展半暹罗U-Net对死后样本海马体和内嗅皮质中tau蛋白病生物标志物进行新型自动定量分析的方案

A Novel Automatic Quantification Protocol for Biomarkers of Tauopathies in the Hippocampus and Entorhinal Cortex of Post-Mortem Samples Using an Extended Semi-Siamese U-Net.

作者信息

Campero-Garcia Luis A, Cantoral-Ceballos Jose A, Martinez-Maldonado Alejandra, Luna-Muñoz Jose, Ontiveros-Torres Miguel A, Gutierrez-Rodriguez Andres E

机构信息

School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico.

Health Sciences Faculty, Universidad Anahuac Mexico Norte, Mexico City 52786, Mexico.

出版信息

Biology (Basel). 2022 Jul 28;11(8):1131. doi: 10.3390/biology11081131.

Abstract

Efforts have been made to diagnose and predict the course of different neurodegenerative diseases through various imaging techniques. Particularly tauopathies, where the tau polypeptide is a key participant in molecular pathogenesis, have significantly increased their morbidity and mortality in the human population over the years. However, the standard approach to exploring the phenomenon of neurodegeneration in tauopathies has not been directed at understanding the molecular mechanism that causes the aberrant polymeric and fibrillar behavior of the tau protein, which forms neurofibrillary tangles that replace neuronal populations in the hippocampal and cortical regions. The main objective of this work is to implement a novel quantification protocol for different biomarkers based on pathological post-translational modifications undergone by tau in the brains of patients with tauopathies. The quantification protocol consists of an adaptation of the U-Net neural network architecture. We used the resulting segmentation masks for the quantification of combined fluorescent signals of the different molecular changes tau underwent in neurofibrillary tangles. The quantification considers the neurofibrillary tangles as an individual study structure separated from the rest of the quadrant present in the images. This allows us to detect unconventional interaction signals between the different biomarkers. Our algorithm provides information that will be fundamental to understanding the pathogenesis of dementias with another computational analysis approach in subsequent studies.

摘要

人们已通过各种成像技术努力诊断和预测不同神经退行性疾病的病程。特别是在tau蛋白病中,tau多肽是分子发病机制的关键参与者,多年来其在人群中的发病率和死亡率显著增加。然而,探索tau蛋白病中神经退行性变现象的标准方法并未着眼于理解导致tau蛋白异常聚合和形成纤维状行为的分子机制,而这种行为会形成神经原纤维缠结,取代海马体和皮质区域的神经元群体。这项工作的主要目标是基于tau蛋白病患者大脑中tau蛋白经历的病理性翻译后修饰,为不同生物标志物实施一种新的量化方案。该量化方案包括对U-Net神经网络架构的调整。我们使用生成的分割掩码来量化tau蛋白在神经原纤维缠结中经历的不同分子变化的组合荧光信号。该量化将神经原纤维缠结视为与图像中存在的象限其余部分分开的单独研究结构。这使我们能够检测不同生物标志物之间的非常规相互作用信号。我们的算法提供的信息对于后续研究中用另一种计算分析方法理解痴呆症的发病机制至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81d3/9404816/07123420f52c/biology-11-01131-g001.jpg

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