The life expectancy of people with intellectual disabilities (ID) has increased more than that of the general population, demanding a deeper understanding of their health profiles to inform tailored healthy aging interventions. This study aimed to characterize persons with ID aged ≥ 45 years in terms of clinical conditions (degree of ID, comorbidities, and medication), malnutrition risk, functional capacity and frailty. To our knowledge, this is the first study to address this topic in Portugal. A cross-sectional, descriptive-correlational design was used, with a non-probability convenience sampling of 234 individuals with ID aged ≥ 45 years. Questionnaires were completed by organizational mediators, achieving a response rate of 18.5%. The mean age was 53.32 years; 56.4% were male, 97.9% were single, and 98.3% had no children. The main findings are as follows: (i) degree of ID: 52.1% moderate, 25.6% severe, 15% mild and 7.3% profound; (ii) high prevalence of comorbidities; (iii) 38.9% were on five or more medicines (polypharmacy); (iv) 44.9% were at risk of malnutrition and 4.7% were malnourished; (v) 23.1% had total or severe dependence in basic activities of daily living; (vi) 85.5% were severely and 14.5% were moderately dependent in instrumental activities of daily living; and (vii) 46.3% were classified as being frail. Older adults with ID seemed to present complex clinical conditions that required a higher intensity of support. These findings contribute to the continuous development of a model of care tailored to older people with ID, particularly healthy aging interventions.
Electroencephalography (EEG) data used in brain-computer and brain-machine interfaces (BCI/BMI) contain sensitive, user-specific information, creating privacy risks in consumer applications such as gaming, human augmentation, and virtual reality. Existing methods like encryption and anonymization either fail to protect data during analysis or still expose identifiable attributes. While differential privacy (DP) offers formal protection, it often reduces data utility. The key challenge is to generate synthetic EEG data that protects sensitive attributes while retaining temporal features for downstream use. To address this, we developed a federated spiking generative framework that integrates a spiking neural network (SNN)-based generator, temporally correlated noise, an artificial neural network (ANN)-based discriminator, and Rényi differential privacy (RDP) within a federated learning framework. The SNN-based generator captures temporal characteristics, and the ANN-based discriminator maintains classification accuracy. Federated training prevents centralization of raw client data, while privacy is preserved through controlled perturbation of spike-train and membrane-potential representations, supported by RDP-based privacy accounting. We evaluated our architecture against methods such as RDP-TimeGAN, RDP-CRNN-GAN, RDP-RCGAN, and RDP-Clare-GAN, demonstrating that our approach preserves temporal features and downstream utility while maintaining privacy in synthetic EEG generation. This solution enables privacy-preserving generation of distributed synthetic EEG data for BCI/BMI research and development.
ObjectiveMoyamoya disease (MMD) is a cerebrovascular disease whose mechanism relatively unknown and can cause vascular occlusion and hyperplasia. This study aimed to explore molecular heterogeneity associated with the expression of acetylation-related genes in MMD and to identify candidate genes potentially involved in vascular endothelial dysfunction.MethodsPublic MMD transcriptomic datasets were analyzed using a predefined acetylation-regulatory gene set to identify expression clusters and prioritize cluster-associated candidate genes. Functional enrichment, immune-infiltration, and predicted miRNA–mRNA network analyses were performed to characterize candidate-associated molecular features. Serum STK39 and MTMR12 concentrations were assessed by ELISA in a preliminary study of three patients with MMD and three healthy controls. STK39 was further investigated in human brain microvascular endothelial cells (HBMECs) using siRNA-mediated knockdown under normoxic and hypoxic conditions, with Trichostatin A (TSA) treatment included as an exploratory pharmacological intervention.ResultsConsensus clustering was used to classify MMD into two subtypes, and five key subtype-related genes were identified: STK39, NEO1, PKNOX1, GOLT1B, and MTMR12. In the validation cohort, serum STK39 and MTMR12 levels were significantly decreased in patients with MMD. In vitro, STK39 knockdown promoted HBMEC migration, inhibited proliferation, and increased the expression of HIF-1α, p-STAT3, cleaved Caspase-3, IL-6, CCL2, and VCAM-1 under hypoxic conditions, whereas TSA treatment partially reversed these changes.ConclusionAcetylation-related genes are closely associated with the molecular heterogeneity of MMD. Among them, STK39 appears to participate in hypoxia-related endothelial dysfunction and inflammatory activation. These findings provide new insight into the role of acetylation-related pathways in MMD pathogenesis and suggest potential targets for future mechanistic studies.
BackgroundBipolar disorder (BD) is a chronic, disabling psychiatric condition with a high global disease burden. Its course is marked by alternating episodes of mania and depression, with varying degrees of severity. While cyclicity is a defining feature, clinical patterns show significant inter-individual and intra-individual variability. Although naturalistic studies have described the course of BD, the impact of psychiatric comorbidities, particularly attention deficit hyperactivity disorder (ADHD) and borderline personality disorder (BPD), remains underexplored. MapCoBD is a 12-month, naturalistic observational study whose primary objective is to compare the longitudinal clinical course of BD among individuals with classic episodic BD, BD with comorbid ADHD, and BD with comorbid BPD. Secondary objectives are to characterize the clinical, neuropsychological, neuroimaging, and molecular features associated with these distinct clinical courses.ContentThis project will recruit 150 BD individuals divided into three groups: (1) classic episodic BD (epiBD, n = 50), (2) BD with comorbid ADHD (BD-ADHD, n = 50), and (3) BD with comorbid BPD (BD-BPD, n = 50). Assessments will be conducted at baseline (T0), 6 months (T1), and 12 months (T2), including: mood symptom ratings, neuropsychological testing, fasting blood sampling (for serum, plasma, and microRNA), and 3T structural brain MRI scans. Daily mood ratings will also be collected prospectively throughout the study. The primary longitudinal clinical endpoint will be illness burden, defined as the proportion of evaluable follow-up days spent with moderate-to-severe psychopathological symptoms, based on prospective NIMH Life Chart Method assessments. Daily affective ratings will additionally be used to characterize mood instability. At each time point, peripheral biomarkers implicated in BD pathophysiology, including endocannabinoid (eCB) signaling, inflammatory cascade (IC), and epigenetic regulation (microRNA), will be quantified. Clinical, neuroimaging, and molecular data will also be integrated using supervised machine-learning approaches to explore the classification of clinical group status and the prediction of longitudinal mood instability and illness burden.ConclusionsThis study will determine whether ADHD and BPD comorbidity is associated with distinct longitudinal patterns of illness burden and mood instability in BD. The integration of clinical, neuroimaging, and molecular data may identify group-specific multimodal signatures and candidate predictors of clinical course, which will require independent validation before clinical application.
