Journal of Alzheimer's Disease - Volume 91, issue 2

Authors: Xu, Ling-Zhi | Li, Bing-Qiu | Li, Fang-Yu | Li, Ying | Qin, Wei | Zhao, Yu | Jia, Jian-Ping

Article Type: Research Article

Abstract: Background: Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia among the elderly. Excitotoxicity has been implicated as playing a dominant role in AD, especially related to the hyperactivation of excitatory neurons. Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin-dependent kinase and involved in the pathogenesis of AD, but the roles and mechanisms of DAPK1 in excitotoxicity in AD are still uncertain. Objective: We mainly explored the underlying mechanisms of DAPK1 involved in the excitotoxicity of AD and its clinical relevance. Methods: Differentiated SH-SY5Y human neuroblastoma cells, PS1 V97 L transgenic mice, and human plasma …samples were used. Protein expression was assayed by immunoblotting, and intracellular calcium and neuronal damage were analyzed by flow cytometry. Plasma DAPK1 was measured by ELISA. Results: We found that DAPK1 was activated after amyloid-β oligomers (AβOs) exposure in differentiated SH-SY5Y cells. Besides, we found the phosphorylation of GluN2B subunit at Ser1303 was increased, which contributing to excitotoxicity and Ca2+ overload in SH-SY5Y cells. Inhibiting DAPK1 activity, knockdown of DAPK1 expression, and antagonizing GluN2B subunits could effectively prevent AβOs-induced activation of GluN2B subunit, Ca2+ overload, and neuronal apoptosis. Additionally, we found that DAPK1 was elevated in the brain of AD transgenic mouse and in the plasma of AD patients. Conclusion: Our finding will help to understand the mechanism of DAPK1 in the excitotoxicity in AD and provide a reference for the diagnosis and therapy of AD. Show more

Keywords: Alzheimer’s disease, amyloid-β oligomers, death-associated protein kinase 1, excitotoxicity, GluN2B, N-methyl-D-aspartic acid receptor

DOI: 10.3233/JAD-220747

Citation: Journal of Alzheimer's Disease, vol. 91, no. 2, pp. 877-893, 2023

Authors: Kleiman, Michael J. | Ariko, Taylor | Galvin, James E.

Article Type: Research Article

Abstract: Background: The detection of subtle cognitive impairment in a clinical setting is difficult. Because time is a key factor in small clinics and research sites, the brief cognitive assessments that are relied upon often misclassify patients with very mild impairment as normal. Objective: In this study, we seek to identify a parsimonious screening tool in one stage, followed by additional assessments in an optional second stage if additional specificity is desired, tested using a machine learning algorithm capable of being integrated into a clinical decision support system. Methods: The best primary stage incorporated measures of short-term …memory, executive and visuospatial functioning, and self-reported memory and daily living questions, with a total time of 5 minutes. The best secondary stage incorporated a measure of neurobiology as well as additional cognitive assessment and brief informant report questionnaires, totaling 30 minutes including delayed recall. Combined performance was evaluated using 25 sets of models, trained on 1,181 ADNI participants and tested on 127 patients from a memory clinic. Results: The 5-minute primary stage was highly sensitive (96.5%) but lacked specificity (34.1%), with an AUC of 87.5% and diagnostic odds ratio of 14.3. The optional secondary stage increased specificity to 58.6%, resulting in an overall AUC of 89.7% using the best model combination of logistic regression and gradient-boosted machine. Conclusion: The primary stage is brief and effective at screening, with the optional two-stage technique further increasing specificity. The hierarchical two-stage technique exhibited similar accuracy but with reduced costs compared to the more common single-stage paradigm. Show more

Keywords: Alzheimer’s disease, clinical decision support, machine learning, mild cognitive impairment, neuropsychological assessment

DOI: 10.3233/JAD-220891

Citation: Journal of Alzheimer's Disease, vol. 91, no. 2, pp. 895-909, 2023

Article Type: Correction

DOI: 10.3233/JAD-229020

Citation: Journal of Alzheimer's Disease, vol. 91, no. 2, pp. 911-911, 2023