ciTechCare - Capítulos de livros
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- Hyperbaric Oxygen Therapy Improves Glucose Homeostasis in Type 2 Diabetes Patients: A Likely Involvement of the Carotid BodiesPublication . Vera-Cruz, P.; Guerreiro, F.; Ribeiro, M.J.; Guarino M.P.; Conde, S.V.The carotid bodies (CBs) are peripheral chemoreceptors that respond to hypoxia increasing minute ventilation and activating the sympathetic nervous system. Besides its role in ventilation we recently described that CB regulate peripheral insulin sensitivity. Knowing that the CB is functionally blocked by hyperoxia and that hyperbaric oxygen therapy (HBOT) improves fasting blood glucose in diabetes patients, we have investigated the effect of HBOT on glucose tolerance in type 2 diabetes patients. Volunteers with indication for HBOT were recruited at the Subaquatic and Hyperbaric Medicine Center of Portuguese Navy and divided into two groups: type 2 diabetes patients and controls. Groups were submitted to 20 sessions of HBOT. OGTT were done before the first and after the last HBOT session. Sixteen diabetic patients and 16 control individual were included. Fasting glycemia was143.5 ± 12.62 mg/dl in diabetic patients and 92.06 ± 2.99 mg/dl in controls. In diabetic patients glycemia post-OGTT was 280.25 ± 22.29 mg/dl before the first HBOT session. After 20 sessions, fasting and 2 h post-OGTT glycemia decreased significantly. In control group HBOT did not modify fasting glycemia and post-OGTT glycemia. Our results showed that HBOT ameliorates glucose tolerance in diabetic patients and suggest that HBOT could be used as a therapeutic intervention for type 2 diabetes.
- Level up! How Gamed-Based Activities Transform Learning and Alleviate Stress in Institutionalized ElderlyPublication . Lacomba-Arnau, Elena; Ribeiro, Anaísa; Sabino, Raquel; Pinheiro, Rafael; Lopes, Susana; Gaspar, Marisa; Navarro-Mateos, Carmen; Sousa, Micael; Rosa, MarleneMental health issues are a critical concern for the elderly, as the inability to manage stress during stimulation activities can significantly impair their ability to accept and effectively learn new tasks, thereby affecting their performance in daily life activities. Serious games are increasingly recognized as valuable in the context of rehabilitation; however, there is a paucity of studies examining how elderly individuals manage stress and learn in regular practice using such games. In this study, 10 institutionalized elderly participants underwent 6 game-based stimulation sessions playing the serious games Ta!Ti! and Mexerico. Learning variables, including time and error rates, were assessed at baseline (T0), mid-point (T1), and the final session (T2), along with stress management indicators, specifically cortisol levels, at T0 and T2. The findings revealed that learning profiles improved throughout the program, with more pronounced gains observed initially. Additionally, stress levels decreased following each game-based session. The study identified significant relationships between stress management and learning profiles, suggesting that game-based activities can effectively enhance both learning outcomes and stress reduction in the elderly.
- STM-GNN: Space-Time-and-Memory Graph Neural Networks for Predicting Multi-Drug Resistance Risks in Dynamic Patient NetworksPublication . Geissbuhler, Damien; Bornet, Alban; Marques, Catarina; Anjos, André; Pereira, Sónia; Teodoro, DouglasHospital-acquired infections (HAIs), particularly those caused by multidrug-resistant (MDR) bacteria, pose significant risks to vulnerable patients. Accurate predictive models are important for assessing infection dynamics and informing infection prediction and control (IPC) programmes. Graph-based methods, including graph neural networks (GNNs), offer a powerful approach to model complex relationships between patients and environments but often struggle with data sparsity, irregularity, and heterogeneity. We propose the space-time-and-memory (STM)-GNN, a temporal GNN enhanced with recurrent connectivity designed to capture spatiotemporal infection dynamics. STM-GNN effectively integrates sparse, heterogeneous data combining network information from patient-environment interactions and internal memory from historical colonization and contact patterns. Using a unique IPC dataset containing clinical and environmental colonization information collected from a long-term healthcare unit, we show that STM-GNN effectively addresses the challenges of limited and irregular data in an MDR prediction task. Our model reaches 0.84 AUROC, and achieves the most balanced performance overall compared to classic machine learning algorithms, as well as temporal GNN approaches.