Autism Spectrum Disorder (ASD); The Past, The Present, and The Future

Zaky, Eman Ahmed

doi:10.4172/2375-4494.1000e116

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Gluteomorphin C reduced anxiety and developed learning ability after consumption [9]. Casomorphins and gluteomorphins are associated with autism spectrum disorder because these opioid peptides found in the urine samples of autistic patients and removing casein and gluten proteins from autistic children's diet may improve learning abilities, concentration, attention and language problems, eye contact, and digestion problems [21,31].…”

Section: Opioid Peptides Derived From Plant/cereal Proteinmentioning

confidence: 99%

“…The peptides produced during fermentation exhibit a high bioactivity and better opioid functions. For the fermentation of protein-rich sources to produce bioactive peptides, Lactobacillus is one of the most widely employed genera [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Due to the use of lactic acid bacteria, protein-rich substrates become acidified because of lactic acid production.…”

Section: Microbial Productionmentioning

confidence: 99%

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Food-Derived Opioids: Production and the Effects of Opioids on Human Health

Arısoy¹,

Çoban²,

Üstün-Aytekin³

2019

From Conventional to Innovative Approaches for Pain Treatment

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Traditional opioids have been used for the people who suffer from cancer, burns, surgery, HIV/AIDS, and other serious illness pains for years. However, numerous side effects like dizziness, apnea, physical dependence, tolerance, addiction, nausea, and vomiting push the researchers to look forward to the new opioid options. The opioid peptides which derived from foods provide significant advantages as the safe and natural alternative. The researchers reported that it is also promising a new functional food and nutraceutical. In this chapter, the type of food-derived opioids, their origins, possible receptors, their amino acid sequences, opioid effects, production techniques, and health benefits are reviewed.

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Section: Opioid Peptides Derived From Plant/cereal Proteinmentioning

confidence: 99%

Section: Microbial Productionmentioning

confidence: 99%

Food-Derived Opioids: Production and the Effects of Opioids on Human Health

Arısoy¹,

Çoban²,

Üstün-Aytekin³

2019

From Conventional to Innovative Approaches for Pain Treatment

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“…Identifying ASD patients directly in comparison to healthy controls is important for early detection and intervention. ASD's exact cause is still unknown [ 10 ]. Due to lack of knowledge of neuropathology, symptom-based diagnosis often results in poor treatment.…”

Section: Introductionmentioning

confidence: 99%

Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images

Koc,

Kalkan,

Bilgen

2023

Autism Research and Treatment

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This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous studies.

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“…The formed systems share highlights with different systems from organic and physical frameworks, and thus inalienably can be delegated complex systems (Jamal et al, 2014). ASD is known as a brain disorder, and here are the symptomatic characteristics: difficulties in social communication; exhibiting restricted, stereotypic patterns, and repetitive behaviours; a lack of interest in social activities (Zaky, 2017). Autism is no respecter of people; it occurs in all socioeconomic, ethnic, and racial groups.…”

Section: Introductionmentioning

confidence: 99%

Autism Spectrum Disorder Classification Using Deep Learning

Saleh

Chern

2021

Int. J. Onl. Eng.

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<p class="0abstract">The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. ASD is a developmental disability that causes the affected people to have significant communication, social, and behavioural challenges. People with autism are saddled with communication problems, difficulties in social interaction and displaying repetitive behaviours. Several methods have been used to classify the ASD from non-ASD people. However, there is a need to explore more algorithms that can yield better classification performance. Recently, deep learning methods have significantly sharpened the cutting edge of learning algorithms in a wide range of artificial intelligence tasks. These artificial intelligence tasks refer to object detection, speech recognition, and machine translation. In this research, the convolutional neural network (CNN) is employed. This algorithm is used to find processes that can classify ASD with a higher level of accuracy. The image data is pre-processed; the CNN algorithm is then applied to classify the ASD and non-ASD, and the steps of implementing the CNN algorithm are clearly stated. Finally, the effectiveness of the algorithm is evaluated based on the accuracy performance. The support vector machine (SVM) is utilised for the purpose of comparison. The CNN algorithm produces better results with an accuracy of 97.07%, compared with the SVM algorithm. In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.</p>

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Autism Spectrum Disorder (ASD); The Past, The Present, and The Future

Cited by 11 publications

References 33 publications

Food-Derived Opioids: Production and the Effects of Opioids on Human Health

Food-Derived Opioids: Production and the Effects of Opioids on Human Health

Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images

Autism Spectrum Disorder Classification Using Deep Learning

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