Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

Background: Type 2 Diabetes Mellitus (T2DM) and Alzheimer’s Disease (AD) are increasingly linked through shared pathophysiological mechanisms, giving rise to the concept of Type 3 Diabetes Mellitus (T3DM). Brain insulin resistance, oxidative stress, and neuroinflammation are central to both conditions, contributing to cognitive decline and AD progression. Aim: This review aims to explore this emerging relationship and its implications for prevention and management. Methods: Using an integrative review, 21 studies were systematically analyzed. The review focused on identifying demographic, genetic, and lifestyle factors contributing to T2DM and AD and examined shared molecular pathways such as insulin dysregulation and amyloid-beta accumulation. Results: The findings reveal that T3DM shares key features with T2DM and AD, including insulin resistance and chronic inflammation. Lifestyle interventions, such as diet and exercise, alongside routine cognitive and metabolic screenings, are critical in mitigating progression. Conclusions: Further research into diagnostic biomarkers and targeted therapies is essential to manage T3DM and its impact on AD. The role of nursing professionals in early detection, education, and holistic management is emphasized as vital in addressing this dual disease burden. This review offers actionable insights into integrated strategies for addressing these interconnected conditions.

With an increasing diversity of learners in today’s educational set-ups, there is an insurmountable need to cater for individual differences including the cultural variations among learners. It is therefore necessary for educators to develop culturally responsive teaching that enhances intercultural competencies of learners. As educators strive to provide inclusive learning environments in which learners from diverse cultural backgrounds learn equitably, differentiated instruction becomes a practical tool. This paper explores how differentiated instruction can support and enhance culturally responsive teaching by examining how tailored instructional approaches can bridge cultural gaps and enhance educational outcomes. The aim is to provide a comprehensive understanding of how educators can effectively integrate differentiated instructional methodologies to achieve the goals of Culturally Responsive Teaching. The study used a descriptive survey design to determine the use of differentiated instruction by junior school teachers in Kenya and a systematic review of literature, practical examples, and studies on teachers’ practices in culturally responsive teaching. The study outcomes indicated that teachers used various differentiated instructional strategies with flexible grouping being the most commonly used strategy. However, there arises a concern, that teachers were not very familiar with cultural variations of learners in their classrooms even as they developed their differentiated instructional strategies. Literature provided the principles and practices of culturally responsive teaching. The combination of these results were used to formulate a conceptual framework for Culturally Responsive Differentiated Instruction (CRDI) that provides insights for practitioners to develop and implement culturally responsive differentiated instructional strategies. The study recommends that a framework to support teachers in the implementation of inclusive and equitable curriculum through CRDI be developed, CRDI be integrated into the teaching processes and the teachers be trained on providing for learner differences through CRDI.

Neurodegeneration and aging are pressing issues with significant personal, economic, ethical, and social consequences. However, the underlying biological mechanisms of these conditions remain largely unknown, making the development of effective treatments challenging. The difficulty in early detection and diagnosis of neurodegenerative diseases further compounds the issue. Recent advancements in genetics, genomics, and brain imaging technology hold great promise for improving our understanding of neurodegeneration and aging, as well as the development of personalized medicine and new drugs and therapies. Addressing these challenges will require a multi-disciplinary and collaborative approach from researchers in various fields. This Special Issue offers valuable insights and perspectives on this critical area of research, which can help advance our understanding and improve the health and well-being of our aging population.