Can the World Afford Autistic Spectrum Disorder? Nonverbal Communication, Asperger Syndrome and the Interbrain

2010 ◽  
Vol 35 (1) ◽  
pp. 50-51
Author(s):  
Jacqueline Roberts
Keyword(s):  
Asperger Syndrome ◽  
Nonverbal Communication ◽  
Autistic Spectrum Disorder ◽  
Spectrum Disorder ◽  
Autistic Spectrum ◽  
The World

Clinical and anatomical heterogeneity in autistic spectrum disorder: a structural MRI study

2009 ◽  
Vol 40 (7) ◽  
pp. 1171-1181 ◽  
Author(s):  
F. Toal ◽  
E. M. Daly ◽  
L. Page ◽  
Q. Deeley ◽  
B. Hallahan ◽  
...  
Keyword(s):  
Asperger Syndrome ◽  
Autistic Spectrum Disorder ◽  
Grey Matter ◽  
Clinical Phenotype ◽  
Brain Regions ◽  
Spectrum Disorder ◽  
Grey Matter Volume ◽  
Brain Anatomy ◽  
Autistic Spectrum ◽  
Adults With Asd

BackgroundAutistic spectrum disorder (ASD) is characterized by stereotyped/obsessional behaviours and social and communicative deficits. However, there is significant variability in the clinical phenotype; for example, people with autism exhibit language delay whereas those with Asperger syndrome do not. It remains unclear whether localized differences in brain anatomy are associated with variation in the clinical phenotype.MethodWe used voxel-based morphometry (VBM) to investigate brain anatomy in adults with ASD. We included 65 adults diagnosed with ASD (39 with Asperger syndrome and 26 with autism) and 33 controls who did not differ significantly in age or gender.ResultsVBM revealed that subjects with ASD had a significant reduction in grey-matter volume of medial temporal, fusiform and cerebellar regions, and in white matter of the brainstem and cerebellar regions. Furthermore, within the subjects with ASD, brain anatomy varied with clinical phenotype. Those with autism demonstrated an increase in grey matter in frontal and temporal lobe regions that was not present in those with Asperger syndrome.ConclusionsAdults with ASD have significant differences from controls in the anatomy of brain regions implicated in behaviours characterizing the disorder, and this differs according to clinical subtype.

Orthodontic management of patients with autistic spectrum disorder

Dental Update ◽  
2019 ◽  
Vol 46 (7) ◽  
pp. 646-652
Author(s):  
Hesham Ali ◽  
Brekhna Mushtaq ◽  
Ovais Malik
Keyword(s):  
Social Interaction ◽  
Developmental Disability ◽  
Clinical Relevance ◽  
Orthodontic Treatment ◽  
Autistic Spectrum Disorder ◽  
Normal Population ◽  
Spectrum Disorder ◽  
Autistic Spectrum ◽  
The World ◽  
Experience Difficulty

Autism is a life-long developmental disability that affects how people perceive the world and interact with others. The aetiology of autism has not been clearly identified. Affected individuals experience difficulty in social interaction and changes in their environment. There appears to be a higher percentage of malocclusion within the population with special needs than the normal population, and the demand for orthodontic treatment from this group of patients is likely to increase in line with awareness of autism and access to care. The strategies available to manage patients with autism successfully are discussed here. CPD/Clinical Relevance: Understanding the difficulties faced by individuals affected by autistic spectrum disorder and anticipating the challenges in the provision of their care is of paramount importance in the context of their orthodontic management. These challenges, and the strategies available to successfully negotiate them, are concisely presented.

Regulation of Protein Synthesis: an Approach to Treat Autistic Spectrum Disorder (ASD)

2021 ◽  
Vol 28 ◽  
Author(s):  
Jéssika Poliana Teixeira ◽  
Teodorico Castro Ramalho
Keyword(s):  
Protein Synthesis ◽  
Cognitive Skills ◽  
Autistic Spectrum Disorder ◽  
Spectrum Disorder ◽  
Synaptic Proteins ◽  
Autistic Spectrum ◽  
Large Numbers ◽  
The World ◽  
Neurological Functioning

: Autistic Spectrum Disorder (ASD) is a disorder with different etiologies and poor elucidation, characterized by changes in social and cognitive skills. ASD has been characterized that affect large numbers of people in the world. In spite of its great importance, surprisingly just a modest progress has been achieved toward comprehending this pathology and designing new therapies. The molecular dysfunctions observed in people with autism are evidenced by the interference in the synthesis of synaptic proteins, which impairs their development and plasticity, leading to characteristics of individuals with ASD. The present work investigates in detail the mTOR pathway and the proteins related to its regulation and neurological functioning. The path of protein synthesis and translation is promising for the treatment of various disorders and its elucidation may, for example, result in drugs that facilitate the diagnosis and open the range of treatments, improving the quality of life of ASD patients.

PERMODELAN PREDIKTIF AUTISTIC SPECTRUM DISORDER DENGAN ALGORITMA C.45

2018 ◽  
Vol 15 (2) ◽  
pp. 99
Author(s):  
Oktafian Farhan ◽  
Agus Subekti
Keyword(s):  
Autistic Spectrum Disorder ◽  
Spectrum Disorder ◽  
Data Set ◽  
Autistic Spectrum

Autisme merupakan disabilitas perkembangan yang dialami sepanjang hidup penderita Autistic Spectrum Disorder (ASD). Semakin cepat ditangani, semakin besar kemungkinan anak akan kembali normal. Untuk alasan ini, diperlukan metode baru yang dapat membantu orang tua dengan cepat mengenali gejala autisme pada anak-anak mereka. Dalam studi sebelumnya yang dilakukan oleh Fadi Fayez Tabhtah, suatu data set dihasilkan untuk mendeteksi apakah seorang anak memiliki autisme atau tidak. Tetapi penelitiannya hanya menghasilkan data set, ia tidak memeriksa lebih lanjut dimana algoritma cocok untuk data set yang telah dihasilkan. Atribut data set ternyata memiliki nilai yang salah, yang mengundang pertanyaan tentang keakurasian data. Dalam penelitian ini peneliti menggunakan metode CRISP-DM dan menguji keakuratan data set penelitian sebelumnya menggunakan algoritma C.45. Selanjutnya, aplikasi WEKA menggunakan pemilihan fitur dan pengaruh dari nilai yang salah untuk setiap atribut dan menemukan atribut yang paling signifikan. Atribut-atribut ini kemudian diuji dengan algoritma C.45 sehingga model prediksi dari data set diperoleh. Atribut A6 dari perhitungan pohon keputusan tidak muncul sama sekali sebagai cabang. Sebuah model baru diperoleh di mana atribut A6 dihilangkan, sehingga ketika diukur oleh algoritma C.45, nilai akurasi yang lebih baik diperoleh. Hasil model baru kemudian diuji pada data kuesioner baru, yang menghasilkan prediksi yang tepat.

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