Elsevier,
Acta Pharmaceutica Sinica B, Volume , 2025
The article presents PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks to prioritize therapeutic targets for Alzheimer's disease (AD). PI4AD recovers clinically validated targets like APP and ESR1, confirming its prioritization efficacy. The framework identifies Ras signaling as a central therapeutic hub, complementing traditional amyloid/tau-focused approaches. Crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1) and drug repurposing opportunities, bridging genetic insights with pathway-level biology.
Elsevier,
Acta Pharmaceutica Sinica B, Volume , 2025
The article discusses the potential of artificial intelligence (AI) in revolutionizing drug discovery for Alzheimer's disease (AD) and delirium. It explores how AI can facilitate target identification, small molecule and protein-based drug design, and optimization of pharmacokinetic properties to address the challenges in developing effective treatments for these two brain diseases.
Elsevier,
Neurologia Argentina, Volume , 2025
The article suggest that beta-amyloid protein (Aβ) has a significant indirect effect on neurogranin (Ng) through key synaptic mediators such as SYT1 and GAP43 during the preclinical stages of Alzheimer’s disease (AD). These findings highlight the crucial role of SYT1 and GAP43 in mediating beta-amyloid-induced synaptic dysfunction, offering potential early biomarkers and therapeutic targets for AD progression.
Elsevier,
Geriatric Nursing, Volume 61, 1 January 2025
This review highlights how nurse practitioners can strengthen Alzheimer’s disease care by supporting earlier diagnosis and access to treatment, which is essential for maintaining quality of life. Expanding their role helps reduce barriers in the healthcare system, ensuring people with Alzheimer’s receive timely care that promotes better health and well-being.
Elsevier,
International Journal of Educational Research Open, Volume 8, June 2025, 100437
This study evaluates Child Aid’s innovative teacher training program in rural Guatemalan indigenous schools, which combines workshops, instructional coaching, and provision of children’s literature to enhance reading comprehension and critical thinking.
Elsevier, International Journal of Educational Research, Volume 132, January 2025
This qualitative study examines the role of outdoor science activities in improving students engagement with science subjects, finding that both teachers and students view them positively for enhancing motivation, collaboration, and long term learning. However, barriers such as limited teacher training, time constraints, and negative stakeholder perceptions highlight the need for targeted support and greater awareness to effectively integrate these approaches into science education.
Elsevier, International Journal of Educational Research, Volume 134, January 2025
This study examines whether conducting lessons in natural outdoor environments, rather than traditional classrooms, influences students conceptual learning. Results show that younger students benefited cognitively from outdoor lessons, while older students performed better indoors, although both groups perceived natural settings as more restorative, indicating that the physical environment can shape learning outcomes.
Elsevier,
Sustainable Urban Environments for Human Health, Volume , 1 January 2025
When considering urban energy transition, including renewable energy sources (RES) development in an urban space, we must be aware of the complexity of this issue. As usual, attention is first paid to infrastructural conditions, but the energy transition is a multifaceted process. In addition to technological and financial factors, spatial, social, cultural, and historical variables are pivotal because lasting change requires a participatory perspective and contextualization of actions. The study aims to explore the epistemological role of RES installations in urban spaces, focusing on their capacity to domesticate and normalize renewable energy practices in cities. We explored the theoretical side of the issue and provided some background on implementing RES in selected urban spaces in Poland. We pay special attention to their composition in the urban landscape and their potential role in the domestication of renewables in cities. RES installations have a chance to become iconic objects in urban spaces and, therefore, directly influence its inhabitants' social and sustainable practices.
Elsevier,
Nanostructured Carbon Materials from Plant Extracts: Synthesis, Characterization, and Applications, Volume , 1 January 2025
Carbon-based nanomaterials derived from plant extracts have emerged as promising candidates for various environmental applications due to their unique properties and eco-friendly synthesis routes. These nanomaterials including carbon dots, graphene, nanodiamonds, and carbon nanotubes, possess unique physicochemical properties such as biocompatibility, low toxicity, and facile functionalization, making them suitable for environmental applications such as water purifications, chemical sensing, etc. Additionally, these green carbon nanomaterials are used in wastewater treatment to break down complex pollutants and act as catalysts in environmental reactions, accelerating pollutant degradation and reducing environmental impact.
Elsevier,
Fuelling the Future: Intelligent Approaches for Harnessing Hydrogen Energy, Volume , 1 January 2025
This chapter explores the integration of artificial intelligence (AI) in biohydrogen production, a promising renewable energy technology. Biohydrogen is regarded as a potential renewable bioenergy resource. There are many processes through which it can be produced, for example, thermochemical and biological processes like pyrolysis, electrolysis, dark fermentation, and photo-fermentation. It is more economically viable when it is produced from waste materials such as waste biomass via microbial fermentation or light-driven chemical reactions. In the last decade, AI or intelligent systems have revolutionized scientific research. Prospectively, classical AI, machine learning (ML), and deep learning algorithms can be applied to optimize biohydrogen production processes. These techniques including reinforcement learning, artificial neural networks, and genetic algorithms can help optimize crucial influential parameters affecting biohydrogen production efficiency and yield. Random forest and support vector machine are two specific ML algorithms that can improve process monitoring, yield prediction, and address challenges for biohydrogen production by managing complex data, accurately predicting outcomes with improved scalability for industrial production processes. The chapter also highlights AI applications in biohydrogen production employing various AI tools like jellyfish optimizer and adaptive neuro-fuzzy inference system that optimize operational conditions in microbial electrolysis cells, enhancing hydrogen yield from wastewater. However, there are many challenges to implement AI-based systems in practice at large that include data limitations, real-world variability, scalability, and supportive technology to AI. Moreover, intelligent systems’ limited adaptability, to date proven credibility and human oversight importance were also discussed with associated ethical concerns. It also needs continuous monitoring and improvement for economically viable and sustainable production processes. Emerging technological trends in biohydrogen production focus on autonomous AI-based production systems, predictive modeling, appropriate management of supply chain, and sustainability valuation. Future AI developments aim to make biohydrogen production more cost-effective, efficient, and scalable.
