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Active Support is a support model designed to enhance quality of life through activity engagement in people with intellectual disabilities. The aim of the current study was to investigate whether implementation of Active Support affected quality of life, well-being, and activity engagement of residents with mild to moderate intellectual disabilities, using a cluster randomised controlled design. Fourteen services were recruited, and Active Support was implemented after conducting baseline assessments.

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Background: There is limited and conflicting evidence on the comparative cardiometabolic safety and effectiveness of aripiprazole in the management of severe mental illness. We investigated the hypothesis that aripiprazole has a favourable cardiometabolic profile, but similar effectiveness when compared to olanzapine, quetiapine, and risperidone.

Methods And Findings: We conducted an observational emulation of a head-to-head trial of aripiprazole versus olanzapine, quetiapine, and risperidone in UK primary care using data from the Clinical Practice Research Datalink.

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Purpose Of Review: This review aims to summarise recent evidence on the effects of dietary patterns on the risk of bone fractures and sarcopenia.

Recent Findings: Several dietary patterns have been investigated in relation to musculoskeletal health, including Mediterranean Dietary Patterns (MDP), Dietary Inflammatory Indices, vegetarian and vegan diets. Adherence to 'healthier' dietary patterns appears to be protective against fractures and sarcopenia, with the strongest protective associations found between the MDP and fractures.

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Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS.

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Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.

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