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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 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 study measured testosterone and pre-androgens in blood, finding changes with age but not with menopause. This indicates that menopause itself is not a reason for women to need testosterone supplementation.