This article provides a bibliometric analysis of 545 linguistics research articles on dementia published between 1994 and 2023, highlighting key contributors, influential journals, and foundational references.
This scoping review aims to identify trends in studies related to children's online safety education facilitated by digital technologies. The review is guided by the five-stage framework developed by Arksey and O'Malley.
This study explores the food and nutrition security practices among artisanal and small-scale mining communities in Nigeria, revealing both strengths, such as access to local foods and purchasing power, and significant challenges, including meal skipping, poor water quality, and limited healthcare access. The findings emphasize the need for comprehensive interventions to improve nutrition, healthcare infrastructure, and social protection, which are essential for the well-being and sustainable development of these mining communities.
This research underscores the potential of aquaponic systems to contribute to sustainable urban food production while emphasizing the importance of stringent water quality and hygiene practices to ensure food safety in global food systems.
Learn about the history of the ADAM Program and get a behind the scenes look at the new Interactive Missing Children Dashboard. This article demonstrates the power of the ADAM Program in locating missing children, relating to SDG 16, Peace, Justice and Strong Institutions. The ADAM Program is the result of a 25 year partnership between LexisNexis Risk Solutions and the National Center for Missing & Exploited Children (NCMEC).
The 2025 winners of the RELX Environmental Challenge, which supports innovative solutions to advance SDG 6 Clean water and sanitation, have been announced.
This article provides a comprehensive review of the use of graphene-based biosensing platforms for the early detection of Alzheimer's disease. It is found that graphene-based biosensors can detect Alzheimer's disease biomarkers at femtomolar concentrations, enabling early diagnosis before symptom onset. These sensors can also identify multiple biomarkers simultaneously in accessible biofluids like blood, saliva, and urine, enabling less invasive testing.
Report on a new smart delivery system designed to target and treat Alzheimer's disease more effectively, aiming to overcome the challenges of current treatments and offering a promising way to fight Alzheimer's more accurately and safely.
This study presents AlzFormer, a novel deep learning framework utilizing spatiotemporal self-attention to classify Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively normal (CN) individuals from structural MRI scans. By modeling MRI volumes as sequential slice-based inputs and fine-tuning a pre-trained TimeSformer model, AlzFormer achieved 94% accuracy and high class-wise F1-scores, while attention map analyses highlighted clinically relevant brain regions, demonstrating both robust performance and interpretability in multiclass AD diagnosis.
