98%
921
2 minutes
20
Williams Syndrome (WS) is a rare neurodevelopmental disorder characterized by dissonance in cognition and language. The purpose of this study was to investigate the characteristics of early language development in WS children. Seventeen children, aged 2-5 years, diagnosed with WS in the outpatient department of child healthcare in our hospital from December 2020 to June 2023, were included in this study. In the same period, 39 children with global developmental delay (GDD) diagnosed in the outpatient department of child healthcare in our hospital were included as the control group. All children underwent cognitive and language development assessments. The language development characteristics of WS children and the differences between WS children and children with total developmental delay were observed and analyzed. WS children had weaker language comprehension ability but significantly stronger expression ability than GDD children. Intra-group comparison found that most children in the WS group expressed better than they understood; the level of expression is relatively higher than the level of comprehension. While most children in the GDD group understood better than they expressed, the level of comprehension is relatively higher than the level of expression. In addition, the language imitation ability of WS children is significantly better than that of GDD children. Our findings suggest the outstanding feature of verbal ability is expression ability and language imitation ability of WS children; the comprehension ability is still weak. These findings can help us explore intervention methods that enable WS children to reach their full potential, so as to provide guidance for the education and rehabilitation strategies for WS patients.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/ajmg.a.64103 | DOI Listing |
Neotrop Entomol
September 2025
Dept of Entomology, Federal Univ of Viçosa, Viçosa, MG, Brazil.
The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. Thus, the objective of this work was to determine a prediction model for the seasonal dynamics of A.
View Article and Find Full Text PDFMamm Genome
September 2025
Department of Animal Health and Anatomy, Center for Animal Biotechnology and Gene Therapy, Universitat Autònoma de Barcelona, Travessera Dels Turons, 08193, Cerdanyola del Vallès, Barcelona, Spain.
The mouse remains the principal animal model for investigating human diseases due, among other reasons, to its anatomical similarities to humans. Despite its widespread use, the assumption that mouse anatomy is a fully established field with standardized and universally accepted terminology is misleading. Many phenotypic anatomical annotations do not refer to the authority or origin of the terminology used, while others inappropriately adopt outdated or human-centric nomenclature.
View Article and Find Full Text PDFEndocr J
September 2025
Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Japan.
GPT-4o, a general-purpose large language model, has a Retrieval-Augmented Variant (GPT-4o-RAG) that can assist in dietary counseling. However, research on its application in this field remains lacking. To bridge this gap, we used the Japanese National Examination for Registered Dietitians as a standardized benchmark for evaluation.
View Article and Find Full Text PDFBMJ Open
September 2025
Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada
Objective: This study validates the previously tested Screening for Poverty And Related social determinants to improve Knowledge of and access to resources ('SPARK Tool') against comparison questions from well-established national surveys (Post Survey Questionnaire (PSQ)) to inform the development of a standardised tool to collect patients' demographic and social needs data in healthcare.
Design: Cross-sectional study.
Setting: Pan-Canadian study of participants from four Canadian provinces (SK, MB, ON and NL).
Cardiol Rev
September 2025
Department of Medicine, New York Medical College, Valhalla, NY.
Atrial fibrillation (AF) is a prevalent and complex cardiac arrhythmia requiring multifaceted management strategies. This review explores the integration of large language models (LLMs) and machine learning into AF care, with a focus on clinical utility, privacy preservation, and ethical deployment. Federated and transfer learning methods have enabled high-performance predictive modeling across distributed datasets without compromising data security.
View Article and Find Full Text PDF