Search

BackgroundLoneliness, social isolation, and feeling disconnected from society are commonly experienced by parents of children with rare diseases and are, among others, important reasons for special supportive care needs. Social networking platforms are increasingly used for health communication, information exchange, and support. In the field of rare pediatric diseases, qualitative studies have shown that Facebook online support groups are utilized by and beneficial for persons affected by rare pediatric diseases. Nonetheless, the extent of this usage has not been investigated. ObjectiveThis study aims to provide a comprehensive quantitative analysis of the extent of Facebook usage as a tool for rare pediatric disease support groups and to explore factors that influence a disease’s representation on Facebook. These results potentially offer important insights for future public health initiatives and give direction to further research that can give much needed support to parents of children with rare diseases. MethodsWe determined rare pediatric diseases using the inventory of the online portal Orphanet. Facebook support groups were identified by searching 5 synonymous disease descriptions using the group category search bar. Disease- and group-describing parameters were statistically analyzed using standard descriptive statistical methods. Results6398 Facebook support groups, representing 826 diseases (19.5% of all searched diseases), were found. 69% are private groups. Group type, size, activity (sum of posts, comments, and reactions calculated by Facebook), new memberships, and language varied largely between groups (member count: minimum 1, maximum 23,414; activity last 30 days: minimum 0, maximum 3606). The highest percentage of awareness and information groups was found for teratogenic diseases (18/68, 26%). The odds of finding a Facebook group increased according to the level of information available about the disease: known prevalence (odds ratio [OR] 3.98, 95% CI 3.39-4.66, P

Hao‐Fountain syndrome (HAFOUS, OMIM: #616863) is a neurodevelopmental disorder caused by pathogenic variants in the gene USP7 coding for USP7, a protein involved in several crucial cellular homeostatic mechanisms and the recently described MUST complex. The phenotype of HAFOUS is insufficiently understood, yet there is a great need to better understand the spectrum of disease, genotype–phenotype correlations, and disease trajectories. We now present a larger cohort of 32 additional individuals and provide further clinical information about six previously reported individuals. A questionnaire‐based study was performed to characterize the phenotype of Hao‐Fountain syndrome more clearly, to highlight new traits, and to better distinguish the disease from related neurodevelopmental disorders. In addition to confirming previously described features, we report hyperphagia and increased body weight in a subset of individuals. HAFOUS patients present an increased rate of birth complications, congenital anomalies, and abnormal pain thresholds. Speech impairment emerges as a potential hallmark of Hao‐Fountain syndrome. Cognitive testing reports reveal borderline intellectual functioning on average, although some individuals score in the range of intellectual disability. Finally, we created a syndrome‐specific severity score. This score neither indicates a sex‐ nor age‐specific difference of clinical severity, yet highlights a more severe outcome when amino acid changes colocalize to the catalytic domain of the USP7 protein. [ABSTRACT FROM AUTHOR]

Among a multitude of hormonal and metabolic complications, individuals with Prader‐Willi syndrome (PWS) exhibit significant bone abnormalities, including decreased BMD, osteoporosis, and subsequent increased fracture risk. Here we show in mice that loss of Magel2, a maternally imprinted gene in the PWS critical region, results in reduced bone mass, density, and strength, corresponding to that observed in humans with PWS, as well as in individuals suffering from Schaaf‐Yang syndrome (SYS), a genetic disorder caused by a disruption of the MAGEL2 gene. The low bone mass phenotype in Magel2‐/‐ mice was attributed to reduced bone formation rate, increased osteoclastogenesis and osteoclast activity, and enhanced trans‐differentiation of osteoblasts to adipocytes. The absence of Magel2 in humans and mice resulted in reduction in the fatty acid amide bone homeostasis regulator, N‐oleoyl serine (OS), whose levels were positively linked with BMD in humans and mice as well as osteoblast activity. Attenuating the skeletal abnormalities in Magel2‐/‐ mice was achieved with chronic administration of a novel synthetic derivative of OS. Taken together, Magel2 plays a key role in modulating bone remodeling and mass in PWS by affecting OS levels and activity. The use of potent synthetic analogs of OS should be further tested clinically as bone therapeutics for treating bone loss. © 2018 American Society for Bone and Mineral Research. [ABSTRACT FROM AUTHOR]

Rodents employ a broad spectrum of ultrasonic vocalizations (USVs) for social communication. As these vocalizations offer valuable insights into affective states, social interactions, and developmental stages of animals, various deep learning approaches have aimed at automating both the quantitative (detection) and qualitative (classification) analysis of USVs. So far, no notable efforts have been made to determine the most suitable architecture. We present the first systematic evaluation of different types of neural networks for USV classification. We assessed various feedforward networks, including a custom-built, fully-connected network, a custom-built convolutional neural network, several residual neural networks, an EfficientNet, and a Vision Transformer. Our analysis concluded that convolutional networks with residual connections specifically adapted to USV data, are the most suitable architecture for analyzing USVs. Paired with a refined, entropy-based detection algorithm (achieving recall of 94.9 % and precision of 99.3 %), the best architecture (achieving 86.79 % accuracy) was integrated into a fully automated pipeline capable of analyzing extensive USV datasets with high reliability. In ongoing projects, our pipeline has proven to be a valuable tool in studying neonatal USVs. By comparing these distinct deep learning architectures side by side, we have established a solid foundation for future research., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).)