Modeling Alzheimer’s and Other Age Related Human Diseases in Embryonic Systems

Lim, Chu Hsien; Mathuru, Ajay S.

doi:10.3390/jdb6010001

Cited by 8 publications

(3 citation statements)

References 33 publications

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“…Among the small organism models, the most commonly used have been flies ( Drosophila melanogaster ), zebrafish ( Danio rerio ) and worms ( Caenorhabditis elegans ). These models each possess their differential characteristics, advantages and limitations (reviewed in [ 6 , 7 , 8 , 9 , 10 ]). None of them manages to reproduce all the pathophysiological changes that occur in the life of a human being, and neither do the mammalian models, but they still show parallels in various age-related phenomena, such as loss of muscle strength or alterations in locomotion.…”

Section: Models To Study Alzheimer’s Diseasementioning

confidence: 99%

Modeling Alzheimer’s Disease in Caenorhabditis elegans

et al. 2022

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Alzheimer’s disease (AD) is the most frequent cause of dementia. After decades of research, we know the importance of the accumulation of protein aggregates such as β-amyloid peptide and phosphorylated tau. We also know that mutations in certain proteins generate early-onset Alzheimer’s disease (EOAD), and many other genes modulate the disease in its sporadic form. However, the precise molecular mechanisms underlying AD pathology are still unclear. Because of ethical limitations, we need to use animal models to investigate these processes. The nematode Caenorhabditis elegans has received considerable attention in the last 25 years, since the first AD models overexpressing Aβ peptide were described. We review here the main results obtained using this model to study AD. We include works studying the basic molecular mechanisms of the disease, as well as those searching for new therapeutic targets. Although this model also has important limitations, the ability of this nematode to generate knock-out or overexpression models of any gene, single or combined, and to carry out toxicity, recovery or survival studies in short timeframes with many individuals and at low cost is difficult to overcome. We can predict that its use as a model for various diseases will certainly continue to increase.

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Section: Models To Study Alzheimer’s Diseasementioning

confidence: 99%

Modeling Alzheimer’s Disease in Caenorhabditis elegans

et al. 2022

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“…Modeling phenotypes associated with human neuropsychiatric disorders in animals is essential to gain a mechanistic understanding of the molecular and genetic players that influence the phenomenon and to devise intervention strategies (Lim and Mathuru, 2017). Zebrafish are used extensively in both cellular and molecular modeling of diseases (Bourque and Houvras, 2011; Santoriello and Zon, 2012; Ablain and Zon, 2013) as well as in pharmacological studies relying on behavioral assays (Cachat et al, 2010; Stewart et al, 2012, 2015; Norton, 2013; Kalueff, 2017; Bao et al, 2019; Fontana et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

An Automated Assay System to Study Novel Tank Induced Anxiety

Haghani

Karia

Cheng

et al. 2019

Front. Behav. Neurosci.

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New environments are known to be anxiogenic initially for many animals including the zebrafish. In the zebrafish, a novel tank diving (NTD) assay for solitary fish has been used extensively to model anxiety and the effect of anxiolytics. However, studies can differ in the conditions used to perform this assay. Here, we report the development of an efficient, automated toolset and optimal conditions for effective use of this assay. Applying these tools, we found that two important variables in previous studies, the direction of illumination of the novel tank and the age of the subject fish, both influence endpoints commonly measured to assess anxiety. When tanks are illuminated from underneath, several parameters such as the time spent at the bottom of the tank, or the transitions to the top half of the tank become poor measures of acclimation to the novel environment. Older fish acclimate faster to the same settings. The size of the novel tank and the intensity of the illuminating light can also influence acclimation. Among the parameters measured, reduction in the frequency of erratic swimming (darting) is the most reliable indicator of anxiolysis. Open source pipeline for automated data acquisition and systematic analysis generated here and available to other researchers will improve accessibility and uniformity in measurements. They can also be directly applied to study other fish. As this assay is commonly used to model anxiety phenotype of neuropsychiatric ailments in zebrafish, we expect our tools will further aid comparative and meta-analyses.

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“…Another study shows that the death rate has decreased caused by heart disease, stroke, and prostate cancer, but it has increased by 145% due to AD. Global AD care cost is more than 600 billion US dollars [5].…”

Section: Introductionmentioning

confidence: 99%

Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

Mehmood¹,

Abugabah²,

AlZubi³

et al. 2022

Computer Systems Science and Engineering

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Alzheimer's disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of AD automatically. In the proposed system, we use segmented MRI scans. Input data samples of three classes include 110 normal control (NC), 110 mild cognitive impairment (MCI) and 105 AD subjects are used in this paper. The data is acquired from the ADNI database and gray matter (GM) images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models. The proposed approaches segregate among NC, MCI, and AD. While testing both methods applied on the segmented data samples, the highest performance results of the classification in terms of accuracy on NC vs. AD are 95.33% and 89.87%, respectively. The proposed methods distinguish between NC vs. MCI and MCI vs. AD patients with a classification accuracy of 90.74% and 86.69%. The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.

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Modeling Alzheimer’s and Other Age Related Human Diseases in Embryonic Systems

Cited by 8 publications

References 33 publications

Modeling Alzheimer’s Disease in Caenorhabditis elegans

Modeling Alzheimer’s Disease in Caenorhabditis elegans

An Automated Assay System to Study Novel Tank Induced Anxiety

Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

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