EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.

Background/Objectives: Digital twins are emerging as a transformative technology in diabetes management, promising a shift from standardized protocols to highly personalized care. This scoping review aims to systematically map the current landscape of digital twin applications in diabetes, synthesizing evidence on their implementation architectures, analytical models, performance metrics, and clinical integration strategies to identify key trends and critical gaps. Methods: A systematic search was conducted across five electronic databases in accordance with PRISMA-ScR guidelines to identify empirical studies on digital twins for diabetes. Data from the selected articles were extracted to analyze bibliographic characteristics, population data, technological components, performance outcomes, and integration levels. A narrative synthesis was performed to map the evidence. Results: Seventeen studies were included, revealing a rapid increase in publications since 2022, with a notable concentration of research in India. The technological architecture shows a convergence toward machine learning models (e.g., LSTM) powered by data from IoT devices and wearables. Certain interventional studies have reported significant clinical impacts, including HbA1c reductions of up to 1.9% and T2DM remission rates as high as 76.5% in one trial. However, major implementation barriers were identified, including fragmented interoperability standards and low rates of full integration into clinical workflows (35.3%). Conclusions: Digital twins are emerging as powerful tools that show the potential to drive significant clinical outcomes in diabetes care. However, to translate this promise into widespread practice, future efforts must focus on overcoming the critical challenges of standardized interoperability and deep clinical integration. Rigorous, independently validated, long-term trials in diverse populations are essential to confirm these promising findings.

Background: Thoracentesis is pivotal in managing pleural effusion (PE), particularly in invasive mechanical ventilation (IMV), with documented improvements in respiratory mechanics, oxygenation, and hemodynamic parameters. However, its efficacy may vary based on effusion type and drained volume. Methods: A retrospective longitudinal study was conducted at a high-complexity care center in Cali, Colombia (2019–2024), including 93 (IMV) patients who underwent therapeutic thoracentesis (TT). Respiratory and hemodynamic parameters were assessed before and up to 24 h post-procedure. Stratified analysis was performed by drained volume, fluid type, and left ventricular ejection fraction (LVEF). Results: TT yielded significant improvements in fraction of inspired oxygen (FiO₂) (−4%), positive end expiratory pressure (PEEP) (−0.5 cmH₂O), and Oxygen arterial Pressure Index/Inspired Oxygen Fraction (PaO₂/FiO₂-ratio) (+27.1), with greater impact for volumes ≥500 mL and transudative PE. Patients with LVEF ≤ 40% showed increased mean arterial pressure (MAP) and PaO₂. Complication rates were low (<4%). Conclusions: TT is safe and effective in critically ill IMV patients, particularly for transudative PE and drained volumes ≥500 mL, as well as in subjects with LVEF ≤ 40%. Its positive impact on oxygenation and ventilation supports its therapeutic utility in critical care.