scholarly journals Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis

2014 ◽  
Vol 20 (1) ◽  
pp. 61 ◽  
Author(s):  
Ga-Young Lee ◽  
Jeonghun Kim ◽  
Ju Han Kim ◽  
Kiwoong Kim ◽  
Joon-Kyung Seong
2020 ◽  
Vol 6 (6) ◽  
pp. e517
Author(s):  
Young Ho Park ◽  
Angela Hodges ◽  
Andrew Simmons ◽  
Simon Lovestone ◽  
Michael W. Weiner ◽  
...  

ObjectiveTo determine whether transcriptional risk scores (TRSs), a summation of polarized expression levels of functional genes, reflect the risk of Alzheimer disease (AD).MethodsBlood transcriptome data were from Caucasian participants, which included AD, mild cognitive impairment, and cognitively normal controls (CN) in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 661) and AddNeuroMed (n = 674) cohorts. To calculate TRSs, we selected functional genes that were expressed under the control of the AD risk loci and were identified as being responsible for AD by using Bayesian colocalization and mendelian randomization methods. Regression was used to investigate the association of the TRS with diagnosis (AD vs CN) and MRI biomarkers (entorhinal thickness and hippocampal volume). Regression was also used to evaluate whether expression of each functional gene was associated with AD diagnosis.ResultsThe TRS was significantly associated with AD diagnosis, hippocampal volume, and entorhinal cortical thickness in the ADNI. The association of the TRS with AD diagnosis and entorhinal cortical thickness was also replicated in AddNeuroMed. Among functional genes identified to calculate the TRS, CD33 and PILRA were significantly upregulated, and TRAPPC6A was significantly downregulated in patients with AD compared with CN, all of which were identified in the ADNI and replicated in AddNeuroMed.ConclusionsThe blood-based TRS is significantly associated with AD diagnosis and neuroimaging biomarkers. In blood, CD33 and PILRA were known to be associated with uptake of β-amyloid and herpes simplex virus 1 infection, respectively, both of which may play a role in the pathogenesis of AD.Classification of evidenceThe study is rated Class III because of the case control design and the risk of spectrum bias.


2017 ◽  
Vol 35 ◽  
pp. 570-581 ◽  
Author(s):  
Sieun Lee ◽  
Nicolas Charon ◽  
Benjamin Charlier ◽  
Karteek Popuri ◽  
Evgeniy Lebed ◽  
...  

Informatics ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 32 ◽  
Author(s):  
Alessandra Antonaci ◽  
Roland Klemke ◽  
Marcus Specht

Gamification has recently been presented as a successful strategy to engage users, with potential for online education. However, while the number of publications on gamification has been increasing in recent years, a classification of its empirical effects is still missing. We present a systematic literature review conducted with the purpose of closing this gap by clarifying what effects gamification generates on users’ behaviour in online learning. Based on the studies analysed, the game elements most used in the literature are identified and mapped with the effects they produced on learners. Furthermore, we cluster these empirical effects of gamification into six areas: performance, motivation, engagement, attitude towards gamification, collaboration, and social awareness. The findings of our systematic literature review point out that gamification and its application in online learning and in particular in Massive Online Open Courses (MOOCs) are still a young field, lacking in empirical experiments and evidence with a tendency of using gamification mainly as external rewards. Based on these results, important considerations for the gamification design of MOOCs are drawn.


2011 ◽  
Vol 218 (1) ◽  
pp. 51-58 ◽  
Author(s):  
C. Christoph Schultz ◽  
Gerd Wagner ◽  
Kathrin Koch ◽  
Christian Gaser ◽  
Martin Roebel ◽  
...  

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