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This article delves into the intricate relationship between oral health, quality of life, and behavioral characteristics in individuals with autism spectrum disorder (ASD). Background/Objectives: Autism has been associated with various challenges, and this study seeks to elucidate the impact of ASD on oral health outcomes and overall well-being. The research focuses on assessing overall oral health by evaluating various parameters, such as the condition of lips, tongue, gums and tissues, natural teeth, dentures, oral hygiene, and dental pain in individuals with ASD. Additionally, the study explores the influence of age, sex, and certain variables, like basic daily living skills on oral health practices, aiming to identify potential correlations between these factors and oral health outcomes. Methods: We employed standardized instruments to quantitatively measure and analyze the impact of oral health status on the overall quality of life experienced by individuals with ASD. Results: The study found a statistically significant positive association between oral health, measured by the Oral Health Assessment Tool (OHAT), and quality of life, as indicated by EuroQol 5-Dimensions Youth version (EQ-5D-Y) total scores (β = 0.13045, p = 0.00271). This suggests that better oral health is linked to higher quality of life. When adjusting for age and sex in a multiple linear regression model, the association remained significant but with a slightly reduced effect size (β = 0.10536, p = 0.0167). Age also showed a marginally significant positive association with quality-of-life scores. ANOVA results indicated that participants with advanced oral health status reported significantly higher quality-of-life scores than those with poorer oral health (p = 0.00246). The study also found that intelligence quotient (IQ) does not substantially influence dental health status, while the "Autonomy" subscale of the EQ-5D-Y is positively related to the OHAT. Conclusions: Unhealthy oral conditions significantly impact the overall quality of life in individuals with ASD. Therefore, it is crucial to include regular dental assessments and treatments in therapeutic protocols for patients with ASD. [ABSTRACT FROM AUTHOR]

Accurate segmentation of the liver and tumors from CT volumes is crucial for hepatocellular carcinoma diagnosis and pre-operative resection planning. Despite advances in deep learning-based methods for abdominal CT images, fully-automated segmentation remains challenging due to class imbalance and structural variations, often requiring cascaded approaches that incur significant computational costs. In this paper, we present the Dual-Encoder Double Concatenation Network (DEDC-Net) for simultaneous segmentation of the liver and its tumors. DEDC-Net leverages both residual and skip connections to enhance feature reuse and optimize performance in liver and tumor segmentation tasks. Extensive qualitative and quantitative experiments on the LiTS dataset demonstrate that DEDC-Net outperforms existing state-of-the-art liver segmentation methods. An ablation study was conducted to evaluate different encoder backbones - specifically VGG19 and ResNet - and the impact of incorporating an attention mechanism. Our results indicate that DEDC-Net, without any additional attention gates, achieves a superior mean Dice Score (DS) of 0.898 for liver segmentation. Moreover, integrating residual connections into one encoder yielded the highest DS for tumor segmentation tasks. The robustness of our proposed network was further validated on two additional, unseen CT datasets: IDCARDb-01 and COMET. Our model demonstrated superior lesion segmentation capabilities, particularly on IRCADb-01, achieving a DS of 0.629. The code implementation is publicly available at this website., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)