In this paper, we adapted and expanded a set of guidelines, also known as heuristics, to evaluate the usability of software to now be appropriate for software aimed at children with autism spectrum disorder (ASD). We started from the heuristics developed by Nielsen in 1990 and developed a modified set of 15 heuristics. The first 5 heuristics of this set are the same as those of the original Nielsen set, the next 5 heuristics are improved versions of Nielsen's, whereas the last 5 heuristics are new. We present two evaluation studies of our new heuristics. In the first, two groups compared Nielsen's set with the modified set of heuristics, with each group evaluating two interactive systems. The Nielsen's heuristics were assigned to the control group while the experimental group was given the modified set of heuristics, and a statistical analysis was conducted to determine the effectiveness of the modified set, the contribution of 5 new heuristics and the impact of 5 improved heuristics. The results show that the modified set is significantly more effective than the original, and we found a significant difference between the five improved heuristics and their corresponding heuristics in the original set. The five new heuristics are effective in problem identification using the modified set. The second study was conducted using a system which was developed to ascertain if the modified set was effective at identifying usability problems that could be fixed before the release of software. The post-study analysis revealed that the majority of the usability problems identified by the experts were fixed in the updated version of the system.
Potocki-Lupski syndrome (PTLS), also known as duplication 17p11.2 syndrome, trisomy 17p11.2 or dup(17)(p11.2p11.2) syndrome, is a developmental disorder and a rare contiguous gene syndrome affecting 1 in 20,000 live births. Among the key features of such patients are autism spectrum disorder, learning disabilities, developmental delay, attention-deficit disorder, infantile hypotonia and cardiovascular abnormalities. Previous studies using microarray identified variations in the size and extent of the duplicated region of chromosome 17p11.2. However, there are a few genes which are considered as candidates for PTLS which include RAI1, SREBF1, DRG2, LLGL1, SHMT1 and ZFP179. In this report, we investigated a case of a 3-year-old girl who has developmental delay. Her chromosome analysis showed a normal karyotype (46,XX). Analysis using array CGH (4X44 K, Agilent USA) identified an ~4.2 Mb de novo duplication in chromosome 17p11.2. The result was confirmed by fluorescence in situ hybridization (FISH) using probes in the critical PTLS region. This report demonstrates the importance of microarray and FISH in the diagnosis of PTLS.
Developmental delay and/or intellectual disability (DD/ID) affects 1-3% of all children. At least half of these are thought to have a genetic etiology. Recent studies have shown that massively parallel sequencing (MPS) using a targeted gene panel is particularly suited for diagnostic testing for genetically heterogeneous conditions. We report on our experiences with using massively parallel sequencing of a targeted gene panel of 355 genes for investigating the genetic etiology of eight patients with a wide range of phenotypes including DD/ID, congenital anomalies and/or autism spectrum disorder. Targeted sequence enrichment was performed using the Agilent SureSelect Target Enrichment Kit and sequenced on the Illumina HiSeq2000 using paired-end reads. For all eight patients, 81-84% of the targeted regions achieved read depths of at least 20×, with average read depths overlapping targets ranging from 322× to 798×. Causative variants were successfully identified in two of the eight patients: a nonsense mutation in the ATRX gene and a canonical splice site mutation in the L1CAM gene. In a third patient, a canonical splice site variant in the USP9X gene could likely explain all or some of her clinical phenotypes. These results confirm the value of targeted MPS for investigating DD/ID in children for diagnostic purposes. However, targeted gene MPS was less likely to provide a genetic diagnosis for children whose phenotype includes autism.
This study determined the accuracy of Modified Checklist for Autism in Toddlers (M-CHAT) in detecting toddlers with autism spectrum disorder (ASD) and other developmental disorders (DD) in community mother and child health clinics. We analysed 19,297 eligible toddlers (15-36 months) who had M-CHAT performed in 2006-2011. Overall sensitivities for detecting ASD and all DD were poor but better in the 21 to <27 months and 27-36-month age cohorts (54.5-64.3%). Although positive predictive value (PPV) was poor for ASD, especially the younger cohort, positive M-CHAT helped in detecting all DD (PPV = 81.6%). This suggested M-CHAT for screening ASD was accurate for older cohorts (>21 months) and a useful screening tool for all DD.
Neurodevelopmental disorders are defined as a set of abnormal brain developmental conditions marked by the early childhood onset of cognitive, behavioral, and functional deficits leading to memory and learning problems, emotional instability, and impulsivity. Autism spectrum disorder, attention-deficit/hyperactivity disorder, Tourette syndrome, fragile X syndrome, and Down's syndrome are a few known examples of neurodevelopmental disorders. Although they are relatively common in both developed and developing countries, very little is currently known about their underlying molecular mechanisms. Both genetic and environmental factors are known to increase the risk of neurodevelopmental disorders. Current diagnostic and screening tests for neurodevelopmental disorders are not reliable; hence, individuals with neurodevelopmental disorders are often diagnosed in the later stages. This negatively affects their prognosis and quality of life, prompting the need for a better diagnostic biomarker. Recent studies on microRNAs and their altered regulation in diseases have shed some light on the possible role they could play in the development of the central nervous system. This review attempts to elucidate our current understanding of the role that microRNAs play in neurodevelopmental disorders with the hope of utilizing them as potential biomarkers in the future.