The psychosocial consequences of growing up with Heritable Connective Tissue Disorders (HCTD) are largely unknown. We aimed to assess Health-Related Quality of Life (HRQoL) and mental health of children and adolescents with HCTD. This observational multicenter study included 126 children, aged 4–18 years, with Marfan syndrome (MFS, n = 74), Loeys–Dietz syndrome (n = 8), molecular confirmed Ehlers–Danlos syndromes (n = 15), and hypermobile Ehlers–Danlos syndrome (hEDS, n = 29). HRQoL and mental health were assessed through the parent and child-reported Child Health Questionnaires (CHQ-PF50 and CHQ-CF45, respectively) and the parent-reported Strengths and Difficulties Questionnaire. Compared with a representative general population sample, parent-reported HRQoL of the HCTD-group showed significantly decreased Physical sum scores (p < 0.001, d = 0.9) and Psychosocial sum scores (p = 0.024, d = 0.2), indicating decreased HRQoL. Similar findings were obtained for child-reported HRQoL. The parent-reported mental health of the HCTD-group showed significantly increased Total difficulties sum scores (p = 0.01, d = 0.3), indicating decreased mental health. While the male and female MFS- and hEDS-subgroups both reported decreased HRQoL, only the hEDS-subgroup reported decreased mental health. In conclusion, children and adolescents with HCTD report decreased HRQoL and mental health, with most adverse outcomes reported in children with hEDS and least in those with MFS. These findings call for systematic monitoring and tailored interventions.
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Background: Research into termination of long-term psychosocial treatment of mental disorders is scarce. Yearly 25% of people in Dutch mental health services receive long-term treatment. They account for many people, contacts, and costs. Although relevant in different health care systems, (dis)continuation is particularly problematic under universal health care coverage when secondary services lack a fixed (financially determined) endpoint. Substantial, unaccounted, differences in treatment duration exist between services. Understanding of underlying decisional processes may result in improved decision making, efficient allocation of scarce resources, and more personalized treatment.
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OBJECTIVES: Children diagnosed with auditory processing disorders (APD) experience difficulties in auditory functioning and with memory, attention, language, and reading tasks. However, it is not clear whether the behavioral characteristics of these children are distinctive from the behavioral characteristics of children diagnosed with a different developmental disorder, such as specific language impairment (SLI), dyslexia, attention-deficit hyperactivity disorder (ADHD), learning disorder (LD), or autism spectrum disorder. This study describes the performance of children diagnosed with APD, SLI, dyslexia, ADHD, and LD to different outcome measurements. The aim of this study was to determine (1) which characteristics of APD overlap with the characteristics of children with SLI, dyslexia, ADHD, LD, or autism spectrum disorder; and (2) if there are characteristics that distinguish children diagnosed with APD from children diagnosed with other developmental disorders.DESIGN: A systematic review. Six electronic databases (Pubmed, CINAHL, Eric, PsychINFO, Communication & Mass Media Complete, and EMBASE) were searched to find peer-reviewed studies from 1954 to May 2015. The authors included studies reporting behaviors and performance of children with (suspected) APD and children diagnosed with a different developmental disorder (SLI, Dyslexia, ADHD, and LD). Two researchers identified and screened the studies independently. Methodological quality of the included studies was assessed with the American Speech-Language-Hearing Association's levels-of-evidence scheme.RESULTS: In total, 13 studies of which the methodological quality was moderate were included in this systematic review. In five studies, the performance of children diagnosed with APD was compared with the performance of children diagnosed with SLI: in two with children diagnosed with dyslexia, one with children diagnosed with ADHD, and in another one with children diagnosed with LD. Ten of the studies included children who met the criteria for more than one diagnosis. In four studies, there was a comparison made between the performances of children with comorbid disorders. There were no studies found in which the performance of children diagnosed with APD was compared with the performance of children diagnosed with autism spectrum disorder. Children diagnosed with APD broadly share the same characteristics as children diagnosed with other developmental disorders, with only minor differences between them. Differences were determined with the auditory and visual Duration Pattern Test, the Children's Auditory Processing Performance Scale questionnaire, and the subtests of the Listening in Spatialized Noise-Sentences test, in which noise is spatially separated from target sentences. However, these differences are not consistent between studies and are not found in comparison to all groups of children with other developmental disorders.CONCLUSIONS: Children diagnosed with APD perform equally to children diagnosed with SLI, dyslexia, ADHD, and LD on tests of intelligence, memory or attention, and language tests. Only small differences between groups were found for sensory and perceptual functioning tasks (auditory and visual). In addition, children diagnosed with dyslexia performed poorer in reading tasks compared with children diagnosed with APD. The result is possibly confounded by poor quality of the research studies and the low quality of the used outcome measures. More research with higher scientific rigor is required to better understand the differences and similarities in children with various neurodevelopmental disorders.
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Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
