Hélder Cerqueira Alves

Abstract
Objectives: Social isolation and loneliness among older people are widespread, with an impact on physical and mental health. Cycling Without Age (CWA) is an international cycling programme developed to minimise social isolation and loneliness in older people. It involves trishaw (electric bicycle) rides in the open air, led by volunteer riders. This study aimed to analyse the effects of CWA intervention on loneliness and social isolation among older people living in Porto, Portugal. Methods: Older adults (aged 55 years or older) living in the community or a nursing home were included. The intervention comprised at least four bicycle rides, with a duration between 30 and 60 min. A research protocol was applied before and after the intervention, which included the UCLA Loneliness Scale and the Abbreviated Lubben Social Network Scale. Results: A total of 47 participants (median age = 85 years) completed the intervention. Participants were mostly female (81%), widowed (66%) and living in nursing homes (72%). A statistically significant decrease in loneliness was found after the intervention (Median [IQR]_after = 24.0 [16.0] vs. before = 17.0 [6.0]; p < 0.05). Discussion: This preliminary work highlights the positive effect the CWA intervention may have on loneliness among older adults, which is consistent with other CWA programme studies. However, future research is required to evaluate whether these effects persist over time.

Abstract
The demographic aging presents significant intervention challenges to institutions within the elderly care management system, such as the National Republican Guard (GNR) with its program “65 years—elderly in safety,” which aims to minimize the effects of social isolation and the experience of loneliness. This exploratory study had as its main goal to understand how elderly individuals aged 65 and over who participate in this GNR program experience loneliness and to identify the social and emotional challenges that arise from it, particularly regarding mental health. Twenty elderly people were surveyed, out of a total of 40, and identified and monitored by the GNR. This is a non-probabilistic quota sample, selected according to gender and age group. In addition to a sociodemographic characterization survey, the UCLA Loneliness Scale, the SELSA-S (Social and Emotional Loneliness Scale for Adults), and the Geriatric Depression Scale (GDS 15) were used. The surveyed elderly present a moderate level of loneliness, with most having family but not finding in it the necessary social support to meet their needs and promote their mental health and well-being. The fact that they live in rural areas, far from large population centers, creates barriers to fulfilling their various needs and often, as evidenced by the data collected, leads them to develop depressive symptoms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
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
In this paper we introduce and develop the concept of interval-weighted networks (IWN), a novel approach in Social Network Analysis, where the edge weights are represented by closed intervals composed with precise information, comprehending intrinsic variability. We extend IWN for both Newman's modularity and modularity gain and the Louvain algorithm, considering a tabular representation of networks by contingency tables. We apply our methodology to two real-world IWN. The first is a commuter network in mainland Portugal, between the twenty three NUTS 3 Regions (IWCN). The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015 (IWTN). The optimal partition of geographic locations (regions or countries) is developed and compared using two new different approaches, designated as Classic Louvain and Hybrid Louvain , which allow taking into account the variability observed in the original network, thereby minimizing the loss of information present in the raw data. Our findings suggest the division of the twenty three Portuguese regions in three main communities for the IWCN and between two to three country communities for the IWTN. However, we find different geographical partitions according to the community detection methodology used. This analysis can be useful in many real-world applications, since it takes into account that the weights may vary within the ranges, rather than being constant.

Abstract
In this paper we introduce and develop the concept of interval-weighted networks (IWN), a novel approach in Social Network Analysis, where the edge weights are represented by closed intervals composed with precise information, comprehending intrinsic variability. We extend IWN for both Newman's modularity and modularity gain and the Louvain algorithm, considering a tabular representation of networks by contingency tables. We apply our methodology to two real-world IWN. The first is a commuter network in mainland Portugal, between the twenty three NUTS 3 Regions (IWCN). The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015 (IWTN). The optimal partition of geographic locations (regions or countries) is developed and compared using two new different approaches, designated as Classic Louvain and Hybrid Louvain , which allow taking into account the variability observed in the original network, thereby minimizing the loss of information present in the raw data. Our findings suggest the division of the twenty three Portuguese regions in three main communities for the IWCN and between two to three country communities for the IWTN. However, we find different geographical partitions according to the community detection methodology used. This analysis can be useful in many real-world applications, since it takes into account that the weights may vary within the ranges, rather than being constant.