Pubmed du 06/09/21
1. Atherton G, Edisbury E, Piovesan A, Cross L. Autism Through the Ages: A Mixed Methods Approach to Understanding How Age and Age of Diagnosis Affect Quality of Life. Journal of autism and developmental disorders. 2021.
A significant proportion of autistic adults today were not diagnosed until later in life, a group referred to as the ‘lost generation,’ which may affect mental health. In Study 1 we explored quality of life and autistic trait levels in 420 autistic and TD adults, and in Study 2 we explored the experiences of 8 autistic adults diagnosed as adults. We found that autistic adults had lower quality of life outcomes and higher autistic trait levels which related to age of diagnosis, and qualitative findings indicated that while adults were empowered by their new diagnosis, they still require specialized supports. Our findings are discussed, emphasizing future directions and implications for the current care system in place for autistic adults.
Lien vers le texte intégral (Open Access ou abonnement)
2. Guo X, Wang J, Wang X, Liu W, Yu H, Xu L, Li H, Wu J, Dong M, Tan W, Chen W, Yang Y, Chen Y. Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms. European radiology. 2022; 32(2): 761-70.
OBJECTIVE: To develop and validate deep learning (DL) methods for diagnosing autism spectrum disorder (ASD) based on conventional MRI (cMRI) and apparent diffusion coefficient (ADC) images. METHODS: A total of 151 ASD children and 151 age-matched typically developing (TD) controls were included in this study. The data from these subjects were assigned to training and validation datasets. An additional 20 ASD children and 25 TD controls were acquired, whose data were utilized in an independent test set. All subjects underwent cMRI and diffusion-weighted imaging examination of the brain. We developed a series of DL models to separate ASD from TD based on the cMRI and ADC data. The seven models used include five single-sequence models (SSMs), one dominant-sequence model (DSM), and one all-sequence model (ASM). To enhance the feature detection of the models, we embed an attention mechanism module. RESULTS: The highest AUC (0.824 ~ 0.850) was achieved when applying the SSM based on either FLAIR or ADC to the validation and independent test sets. A DSM using the combination of FLAIR and ADC showed an improved AUC in the validation (0.873) and independent test sets (0.876). The ASM also showed better diagnostic value in the validation (AUC = 0.838) and independent test sets (AUC = 0.836) compared to the SSMs. Among the models with attention mechanism, the DSM achieved the highest diagnostic performance with an AUC, accuracy, sensitivity, and specificity of 0.898, 84.4%, 85.0%, and 84.0% respectively. CONCLUSIONS: This study established the potential of DL models to distinguish ASD cases from TD controls based on cMRI and ADC images. KEY POINTS: • Deep learning models based on conventional MRI and ADC can be used to diagnose ASD. • The model (DSM) based on the FLAIR and ADC sequence achieved the best diagnostic performance with an AUC of 0.836 in the independent test sets. • The attention mechanism further improved the diagnostic performance of the models.
Lien vers le texte intégral (Open Access ou abonnement)
3. Hasegawa Y, Nishi E, Mishima Y, Sakaguchi T, Sekiguchi F, Miyake N, Kojima K, Osaka H, Matsumoto N, Okamoto N. Novel variants in aromatic L-amino acid decarboxylase deficiency: Case report of sisters with mild phenotype. Brain & development. 2021; 43(10): 1023-8.
BACKGROUND: Aromatic L-amino acid decarboxylase (AADC) deficiency, caused by a pathogenic variant in the dopa decarboxylase (DDC) gene, is a rare neurometabolic disorder in which catecholamine and serotonin are not synthesized. From a large number of reports, it has been recognized that most affected patients show severe developmental delay in a bedridden state and are unable to speak. On the other hand, patients with a mild phenotype with AADC deficiency have been reported, but they number only a few cases. Therefore, the variation of phenotypes of the disease appears to be broad, and it may be challenging to diagnose an atypical phenotype as AADC deficiency. CASE REPORT: We report novel compound heterozygous variants in DDC (c.202G > A and c.254C > T) in two sisters, whose main complaint was mild developmental delay, by whole-exome sequencing (WES). Additionally, we describe their clinical features and provide an image that shows the variants located at different sites responsible for the catalysis of AADC in a three-dimensional structure. The patients were prescribed a Monoamine oxidase (MAO) inhibitor after diagnosis. INTERPRETATION: Our cases indicate that a comprehensive genomic approach helps to diagnose AADC deficiency with atypical features, and underscore the significance of understanding the variations of this disorder for diagnosis and appropriate treatment.
