Characteristics of Parents and Their Infants with Autistic Behaviors: Parent-Infant Interaction, Parental Depression, and Parenting Stress

Parents of infants with autistic behaviors frequently face difficulties in childrearing owing to their infants’ behavioral traits. This study aimed to clarify the characteristics of parent-infant interaction, parental depressive symptoms, and parenting stress in the context of infants’ autistic behaviors. We employed a cross-sectional, case-control observational design with a case group of 52 parents and a control group of 56 parents. We measured parent- infant interaction using the Japanese version of the Nursing Child Assessment Teaching Scale, and parental depressive symptoms and parenting stress with the Japanese version of the Center for Epidemiologic Studies Depression Scale and the Japanese Parenting Stress Index Short Form, respectively. There was no significant difference between the two groups regarding parent-infant interaction. Mothers in the case group had significantly higher depressive symptoms than those in the control group (p < .05). Both fathers (p < .05) and mothers (p < .01) in the case group scored significantly higher than the control group on parenting stress. Thus, parents of infants with autistic behaviors have greater mental health problems right from infancy. We suggest that nurses should provide families with appropriate support to promote adaptive parent-infant interaction and prevent the aggravation of parental mental health problems irrespective of Autism Spectrum Disorder diagnoses

Support system using electronic medical chart for smoking cessation among inpatients

科学研究費補助金研究成果報告書研究種目: 基盤研究(C)研究期間: 2009～2011課題番号: 21592750研究代表者: 宮松 直美（滋賀医科大学・医学部・教授）研究分担者: 盛永 美保（滋賀医科大学・医学部・講師）研究分担者: 藤野 みつ子（滋賀医科大学・医学部・看護師）連携研究者: 三浦 克之（滋賀医科大学・医学部・教授）連携研究者: 林 周子（滋賀医科大学・医学部・看護師）連携研究者: 永田 啓（滋賀医科大学・医学部・教授）連携研究者: 澤井 信江（滋賀医科大学・医学部・准教授

Targeted deep sequencing analyses of long QT syndrome in a Japanese population

Long QT syndrome (LQTS) is one of the most common inherited arrhythmias and multiple genes have been reported as causative. Presently, genetic diagnosis for LQTS patients is becoming widespread and contributing to implementation of therapies. However, causative genetic mutations cannot be detected in about 20% of patients. To elucidate additional genetic mutations in LQTS, we performed deep-sequencing of previously reported 15 causative and 85 candidate genes for this disorder in 556 Japanese LQTS patients. We performed in-silico filtering of the sequencing data and found 48 novel variants in 33 genes of 53 cases. These variants were predicted to be damaging to coding proteins or to alter the binding affinity of several transcription factors. Notably, we found that most of the LQTS-related variants in the RYR2 gene were in the large cytoplasmic domain of the N-terminus side. They might be useful for screening of LQTS patients who had no known genetic factors. In addition, when the mechanisms of these variants in the development of LQTS are revealed, it will be useful for early diagnosis, risk stratification, and selection of treatment

Schematic view of RYR2 mutations in long QT syndrome.

Only those predicted to be damaging in silico are shown. The number in the parentheses indicates the reference number in the manuscript. Those with surrounding lines are the novel variants identified in this study. Gray boxes indicate the transmembrane region and “P” indicates the pore-forming region.</p

List of primer sequences used for SNP validation on exon region.

(XLSX)</p

Characteristics of newly detected variants and their functional prediction scores.

Characteristics of newly detected variants and their functional prediction scores.</p

Variants in the upstream region that could alter the binding affinity of transcription factors predicted by TRAP.

Variants in the upstream region that could alter the binding affinity of transcription factors predicted by TRAP.</p

List of primer sequences used for SNP validation on upstream region.

(XLSX)</p

The average data output in deep sequencing.

The average data output in deep sequencing.</p