Sensitive surface plasmon resonance biosensor by optimized carboxylate functionalized carbon nanotubes/chitosan for amlodipine detecting.

The adoption of biophotonic sensing technologies holds significant promise for application in health care and biomedical industries in all aspects of human life. Then, this piece of writing employs the powerful effective medium theory and FDTD simulation to anticipate the most favorable state and plasmonic attributes of a magnificent nanocomposite, comprising carboxylate functionalized carbon nanotubes and chitosan (CS). Furthermore, it thoroughly explores the exhibited surface plasmon resonance behaviors of this composite versus the quantity of CS variation. Subsequently, enlightening simulations are conducted on the nanocomposite with a delicate layer and a modified golden structure integrating as a composite. The intricate simulations eventually unveil an optimal combination to pave the way for crafting an exceptional specific biosensor that far surpasses its counterpart as a mere Au thin layer in terms of excellence. The proposed biosensor demonstrated linear behavior across a wide range from 0.01 µM to 150 µM and achieved a detection limit of 10 nM, with a sensitivity of 134◦RIU-1.

Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment Using Convolutional Neural Networks.

Background: Alzheimer's disease and mild cognitive impairment are common diseases in the elderly, affecting more than 50 million people worldwide in 2020. Early diagnosis is crucial for managing these diseases, but their complexity poses a challenge. Convolutional neural networks have shown promise in accurate diagnosis. Objective: The main objective of this research is to diagnose Alzheimer's disease and mild cognitive impairment in healthy individuals using convolutional neural networks. Methods: This study utilized three different convolutional neural network models, two of which were pre-trained models, namely AlexNet and DenseNet, while the third model was a CNN1D-LSTM neural network. Results: Among the neural network models used, the AlexNet demonstrated the highest accuracy, exceeding 98%, in diagnosing mild cognitive impairment and Alzheimer's disease in healthy individuals. Furthermore, the accuracy of the DenseNet and CNN1D-LSTM models is 88% and 91.89%, respectively. Conclusions: The research highlights the potential of convolutional neural networks in diagnosing mild cognitive impairment and Alzheimer's disease. The use of pre-trained neural networks and the integration of various patient data contribute to achieving accurate results. The high accuracy achieved by the AlexNet neural network underscores its effectiveness in disease classification. These findings pave the way for future research and improvements in the field of diagnosing these diseases using convolutional neural networks, ultimately aiding in early detection and effective management of mild cognitive impairment and Alzheimer's disease.