Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.
Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future.
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DOI：
10.1093/jnen/nlab122
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发表时间：
2022-01-21
影响因子：
3.2
通讯作者：
Dugger BN
中科院分区：
文献类型：
--
作者：
Shakir MN;Dugger BN
阿尔茨海默病(AD)是一种神经退行性疾病，病理特征是大脑中存在神经原纤维缠结和淀粉样β蛋白(Aβ)斑块。1906年，阿洛伊斯·阿尔茨海默首次描述了这种疾病，从那时起，技术上取得了许多进步，帮助揭开了这种毁灭性疾病的秘密。这些进展包括改进显微镜和染色技术，完善疾病诊断标准，以及提高对疾病异质性的认识，包括异常的神经解剖位置以及与其他脑部疾病的重叠；例如，路易体病和血管性痴呆。尽管取得了许多进展，但仍有许多工作要完成，因为AD尚不能治愈，死后组织学分析仍然是评价AD神经病理变化的金标准。最近的技术进步，如体内生物标记物和机器学习算法，使疾病理解方面取得了巨大进展，并为潜在的新疗法和精确医学方法铺平了道路。在这里，我们回顾人类阿尔茨海默病神经病理学研究的历史，包括在理解阿尔茨海默病常见共同病理以及显微镜和染色方法方面的显著进展。我们还讨论了未来的方法，特别关注使用机器学习的深度表型。
Alzheimer disease (AD) is a neurodegenerative disorder characterized pathologically by the presence of neurofibrillary tangles and amyloid beta (Aβ) plaques in the brain. The disease was first described in 1906 by Alois Alzheimer, and since then, there have been many advancements in technologies that have aided in unlocking the secrets of this devastating disease. Such advancements include improving microscopy and staining techniques, refining diagnostic criteria for the disease, and increased appreciation for disease heterogeneity both in neuroanatomic location of abnormalities as well as overlap with other brain diseases; for example, Lewy body disease and vascular dementia. Despite numerous advancements, there is still much to achieve as there is not a cure for AD and postmortem histological analyses is still the gold standard for appreciating AD neuropathologic changes. Recent technological advances such as in-vivo biomarkers and machine learning algorithms permit great strides in disease understanding, and pave the way for potential new therapies and precision medicine approaches. Here, we review the history of human AD neuropathology research to include the notable advancements in understanding common co-pathologies in the setting of AD, and microscopy and staining methods. We also discuss future approaches with a specific focus on deep phenotyping using machine learning.
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影响因子：
12.7
作者：
Attems J;Toledo JB;Walker L;Gelpi E;Gentleman S;Halliday G;Hortobagyi T;Jellinger K;Kovacs GG;Lee EB;Love S;McAleese KE;Nelson PT;Neumann M;Parkkinen L;Polvikoski T;Sikorska B;Smith C;Grinberg LT;Thal DR;Trojanowski JQ;McKeith IG
通讯作者：
McKeith IG
影响因子：
78.8
作者：
Bera, Kaustav;Schalper, Kurt A.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant
影响因子：
9.9
作者：
Ala, TA;Beh, GO;Frey, WH
通讯作者：
Frey, WH
影响因子：
9.9
作者：
Barnes, Lisa L.;Leurgans, Sue;Schneider, Julie A.
通讯作者：
Schneider, Julie A.
DOI：
10.1093/jhmas/42.3.327
发表时间：
1987-07-01
影响因子：
0.5
作者：
BEACH, TG
通讯作者：
BEACH, TG