Conformational change and GTPase activity of human tubulin: A comparative study on Alzheimer’s disease and healthy brain

2020 ◽  
Vol 155 (2) ◽  
pp. 207-224
Author(s):  
Shima Rajaei ◽  
Saeed Karima ◽  
Hessam Sepasi Tehrani ◽  
Somayeh Shateri ◽  
Somayeh Mahmoodi Baram ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Comparative Study ◽  
Conformational Change ◽  
Gtpase Activity

scholarly journals Disease‐modifying treatment with I2 imidazoline receptor ligand LSL60101 in an Alzheimer's disease mouse model: A Comparative study with donepezil

2021 ◽  
Author(s):  
Foteini Vasilopoulou ◽  
Sergio Rodríguez‐Arévalo ◽  
Andrea Bagán ◽  
Carmen Escolano ◽  
Christian Griñán‐Ferré ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Model ◽  
Comparative Study ◽  
Receptor Ligand ◽  
Imidazoline Receptor ◽  
Disease Modifying ◽  
Disease Modifying Treatment

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

scholarly journals E22Δ Mutation in Amyloidβ-Protein Promotesβ-Sheet Transformation, Radical Production, and Synaptotoxicity, But Not Neurotoxicity

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Takayuki Suzuki ◽  
Kazuma Murakami ◽  
Naotaka Izuo ◽  
Toshiaki Kume ◽  
Akinori Akaike ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Conformational Change ◽  
Deletion Mutant ◽  
Protein A ◽  
Type A ◽  
Synaptic Dysfunction ◽  
Wild Type ◽  
Radical Production

Oligomers of 40- or 42-mer amyloidβ-protein (Aβ40, Aβ42) cause cognitive decline and synaptic dysfunction in Alzheimer's disease. We proposed the importance of a turn at Glu22 and Asp23 of Aβ42 to induce its neurotoxicity through the formation of radicals. Recently, a novel deletion mutant at Glu22 (E22Δ) of Aβ42 was reported to accelerate oligomerization and synaptotoxicity. To investigate this mechanism, the effects of the E22Δ mutation in Aβ42 and Aβ40 on the transformation ofβ-sheets, radical production, and neurotoxicity were examined. Both mutants promotedβ-sheet transformation and the formation of radicals, while their neurotoxicity was negative. In contrast, E22P-Aβ42 with a turn at Glu22 and Asp23 exhibited potent neurotoxicity along with the ability to form radicals and potent synaptotoxicity. These data suggest that conformational change in E22Δ-Aβis similar to that in E22P-Aβ42 but not the same, since E22Δ-Aβ42 exhibited no cytotoxicity, unlike E22P-Aβ42 and wild-type Aβ42.

scholarly journals Tracking neurodegeneration in frontotemporal dementia and Alzheimer’s disease: A comparative study of plasma tau and neurofilament light chain

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Ignacio Illán‐Gala ◽  
Alberto Lleó ◽  
Anna M. Karydas ◽  
Adam M. Staffaroni ◽  
Henrik Zetterberg ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Comparative Study ◽  
Frontotemporal Dementia ◽  
Light Chain ◽  
Neurofilament Light Chain ◽  
Neurofilament Light

Comparative Study on the Inhibitory Effect of Caffeic and Chlorogenic Acids on Key Enzymes Linked to Alzheimer’s Disease and Some Pro-oxidant Induced Oxidative Stress in Rats’ Brain-In Vitro

2012 ◽  
Vol 38 (2) ◽  
pp. 413-419 ◽  
Author(s):  
Ganiyu Oboh ◽  
Odunayo M. Agunloye ◽  
Ayodele J. Akinyemi ◽  
Adedayo O. Ademiluyi ◽  
Stephen A. Adefegha
Keyword(s):  
Oxidative Stress ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Comparative Study ◽  
Inhibitory Effect ◽  
Chlorogenic Acids ◽  
Key Enzymes

Linguistic abilities in major vascular cognitive impairment: a comparative study with Alzheimer’s disease

2018 ◽  
Vol 118 (3) ◽  
pp. 465-473 ◽  
Author(s):  
Maria Isabel D’Avila Freitas ◽  
Claudia S. Porto ◽  
Maira O. Oliveira ◽  
Sonia M. D. Brucki ◽  
Leticia L. Mansur ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Comparative Study ◽  
Vascular Cognitive Impairment

scholarly journals Tau and Amyloid- Conformational Change to  -Sheet Structures as Effectors in the Development of Alzheimer's Disease

10.5772/37909 ◽  
2012 ◽  
Author(s):  
Martha A. De Anda-Hernndez ◽  
Karla I. Lira-De Len ◽  
Ral Mena ◽  
Victoria Campos-Pea ◽  
Marco A.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Conformational Change
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