Oxytocin receptor gene variations predict neural and behavioral response to oxytocin in autism

Oxytocin appears beneficial for autism spectrum disorder (ASD), and more than 20 single-nucleotide polymorphisms (SNPs) in oxytocin receptor (OXTR) are relevant to ASD. However, neither biological functions of OXTR SNPs in ASD nor critical OXTR SNPs that determine oxytocin's effects on ASD remain unknown. Here, using a machine-learning algorithm that was designed to evaluate collective effects of multiple SNPs and automatically identify most informative SNPs, we examined relationships between 27 representative OXTR SNPs and six types of behavioral/neural response to oxytocin in ASD individuals. The oxytocin effects were extracted from our previous placebo-controlled within-participant clinical trial administering single-dose intranasal oxytocin to 38 high-functioning adult Japanese ASD males. Consequently, we identified six different SNP sets that could accurately predict the six different oxytocin efficacies, and confirmed the robustness of these SNP selections against variations of the datasets and analysis parameters. Moreover, major alleles of several prominent OXTR SNPs-including rs53576 and rs2254298-were found to have dissociable effects on the oxytocin efficacies. These findings suggest biological functions of the OXTR SNP variants on autistic oxytocin responses, and implied that clinical oxytocin efficacy may be genetically predicted before its actual administration, which would contribute to establishment of future precision medicines for ASD

Artificial Intelligence in Healthcare : Directions of Standardization

Artificial intelligence (AI) can have a significant positive impact on health and healthcare. AI can be used to improve the quality, efficiency and equity of health care. However, AI has the potential to have significant negative impacts. Therefore, AI medical applications should be designed and deployed in accordance with established guidelines and legislation. There may be gaps or questions in the current regulatory framework related to the interpretation and application of the existing regulatory framework to healthcare applications that include artificial intelligence solutions. Global standardization maintains a consistent approach and can reduce the burden on stakeholders when it comes to establishing regulatory frameworks, interpreting and complying with regulatory requirements. While AI is far from new, it has only recently become mainstream. This chapter outlines the research of the authors who are members of the Hoc Group on Application of AI Technologies in Health Informatics (ISO AHG2 TC215), which was formed by ISO Technical Committee 215 to define goals and directions for standardization in the field of AI in health care.acceptedVersionPeer reviewe