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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.
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.
This viewpoint offers insights on policy to improve diet quality, that resonate not only in Bangladesh but also across other countries navigating similar transitions.
