Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterized by difficulties in social interaction, language development delays, repeated body movements, and markedly deteriorated activities and interests. Environmental factors, such as viral infection, parental age, and zinc deficiency, can be plausible contributors to ASD susceptibility. As ASD is highly heritable, genetic risk factors involved in neurodevelopment, neural communication, and social interaction provide important clues in explaining the etiology of ASD. Accumulated evidence also shows an important role of epigenetic factors, such as DNA methylation, histone modification, and noncoding RNA, in ASD etiology. In this review, we compiled the research published to date and described the genetic and epigenetic epidemiology together with environmental risk factors underlying the etiology of the different phenotypes of ASD.

In an era when immunohistochemistry (IHC) is increasingly depended on for histological subtyping, and IHC-determined biomarker informing rapid treatment choices is on the horizon; reproducible, quantifiable techniques are required. This study aimed to compare automated IHC scoring to quantify 6 DNA damage response protein markers using a tissue microarray of 66 ovarian cancer samples. Accuracy of quantification was compared between manual H-score and computer-aided quantification using Aperio ImageScope with and without a tissue classification algorithm. High levels of interobserver variation was seen with manual scoring. With automated methods, inclusion of the tissue classifier mask resulted in greater accuracy within carcinomatous areas and an overall increase in H-score of a median of 11.5% (0%–18%). Without the classifier, the score was underestimated by a median of 10.5 (5.2–25.6). Automated methods are reliable and superior to manual scoring. Fixed algorithms offer the reproducibility needed for high-throughout clinical applications.