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IntroductionWilson's disease (WD) is associated with a variety of movement disorders and progressive neurological dysfunction. The aim of this study was to correlate baseline brain magnetic resonance imaging (MRI) features with clinical phenotype and long-term outcomes in chronically treated WD patients.MethodsPatients were retrospectively selected from an institutional database. Two experienced neuroradiologists reviewed baseline brain MRI. Functional assessment was performed using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) scale, and disease severity was classified using the Global Assessment Scale for Wilson's Disease (GASWD).ResultsOf 27 patients selected, 14 were female (51.9%), with a mean (standard deviation [SD]) age at onset of 19.5 (7.1) years. Neurological symptoms developed in 22 patients (81.5%), with hyperkinetic symptoms being the most common (70.4%). Baseline brain MRI showed abnormal findings in 18 cases (66.7%), including T2 hyperintensities in 59.3% and atrophy in 29.6%. After a mean (SD) follow-up of 20.9 (11.0) years, WD patients had a mean score of 19.2 (10.2) on WHODAS 2.0 and 6.4 (5.7) on GASWD. The presence of hyperkinetic symptoms correlated with putaminal T2 hyperintensities (p = 0.003), putaminal T2 hypointensities (p = 0.009), and mesencephalic T2 hyperintensities (p = 0.009). Increased functional disability was associated with brain atrophy (p = 0.007), diffusion abnormalities (p = 0.013), and burden of T2 hyperintensities (p = 0.002). A stepwise regression model identified atrophy as a predictor of increased WHODAS 2.0 (p = 0.023) and GASWD (p = 0.007) scores.ConclusionsAtrophy and, to a lesser extent, deep T2 hyperintensity are associated with functional disability and disease severity in long-term follow-up of WD patients.

Abstract
Nowadays, there has been a growing interest in the use of mobile robots for various applications, where the analysis of the operational environment is a crucial component to conduct our special tasks or missions. The main aim of this work was to implement artificial intelligence (AI) for object detection and distance estimation navigating the developed unmanned platform in unknown environments. Conventional approaches are based on vision systems analysis using neural networks for object detection, classification, and distance estimation. Unfortunately, in the case of precise operation, the used algorithms do not provide accurate data required by platforms operators as well as autonomy subsystems. To overcome this limitation, the authors propose a novel approach using the spatial data from laser scanners supplementing the acquisition of precise information about the detected object distance in the operational environment. In this article, we introduced the application of pretrained neural network models, typically used for vision systems, in analysing flat distributions of LiDAR point cloud surfaces. To achieve our goal, we have developed software that fuses detection algorithm (based on YOLO network) to detect objects and estimate their distances using the MiDaS depth model. Initially, the accuracy of distance estimation was evaluated through video stream testing in various scenarios. Furthermore, we have incorporated data from a laser scanner into the software, enabling precise distance measurements of the detected objects. The paper provides discussion on conducted experiments, obtained results, and implementation to improve performance of the described modular mobile platform.

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Bipolar Disorder (BD) is a chronic and severe psychiatric illness presenting with mood alterations, including manic, hypomanic, and depressive episodes. Due to the high clinical heterogeneity and lack of biological validation, both treatment and diagnosis of BD remain problematic and challenging. In this context, this paper proposes a novel intelligent system applied to the diagnosis of BD. First, each patient’s single nucleotide polymorphism (SNP) data is represented by QR codes, which reduces the high dimensionality of the problem and homogenizes the data representation. For the initial tests of the system, the Wellcome Trust Case Control Consortium (WTCCC) dataset was used. The preliminary results are encouraging, with an AUC value of 0.82 and an accuracy of 82%, correctly classifying all cases and most controls. This approach reduces the dimensionality of large amounts of data and can help improve diagnosis and deliver the right treatment to the patient. Furthermore, the architecture of the system is versatile and could be adapted and used to diagnose other diseases where there is also high dimensionality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
In this work, we assess the impact of self-supervised learning (SSL) approaches on the detection of gastritis atrophy (GA) and intestinal metaplasia (IM) conditions. GA and IM are precancerous gastric lesions. Detecting these lesions is crucial to intervene early and prevent their progression to cancer. A set of experiments is conducted over the Chengdu dataset, by considering different amounts of annotated data in the training phase. Our results reveal that, when all available data is used for training, SSL approaches achieve a classification accuracy on par with a supervised learning baseline, (81.52% vs 81.76%). Interestingly, we observe that in low-data regimes (here represented as retaining only 12.5% of annotated data for training), the SSL model guarantees an accuracy gain with respect to the supervised learning baseline of approximately 1.5% (73.00% vs 71.52%). This observation hints at the potential of SSL models in leveraging unlabeled data, thus showcasing more robust performance improvements and generalization. Experimental results also show that SSL performance is significantly dependent on the specific data augmentation techniques and parameters adopted for contrastive learning, thus advocating for further investigations into the definition of optimal data augmentation frameworks specifically tailored for gastric lesion detection applications.