Publications by authors named "M Vai"

Neurodegenerative diseases are among the major challenges in modern medicine, due to the progressive aging of the world population. Among these, Parkinson's disease (PD) affects 10 million people worldwide and is associated with the aggregation of the presynaptic protein α-synuclein (α-syn). Here we use two different PD models, yeast cells and neuroblastoma cells overexpressing α-syn, to investigate the protective effect of an extract from the cocoa shell, which is a by-product of the roasting process of cocoa beans.

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Article Synopsis
  • Mental health issues are becoming a major global concern, requiring an improved and objective early diagnosis method due to the subjective nature of traditional diagnoses.
  • The paper introduces a lightweight detection method using EEG signals and various entropy measures to create an entropy-based matrix for identifying multiple mental disorders.
  • The proposed method demonstrates impressive classification accuracies for schizophrenia, epilepsy, and depression through conventional machine learning classifiers, validating its effectiveness with minimal data input.
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Optogenetics-based integrated photoelectrodes with high spatiotemporal resolution play an important role in studying complex neural activities. However, the photostimulation artifacts caused by the high level of integration and the high impedance of metal recording electrodes still hinder the application of photoelectrodes for optogenetic studies of neural circuits. In this study, a neural optrode fabricated on sapphire GaN material was proposed, and 4 μLEDs and 14 recording microelectrodes were monolithically integrated on a shank.

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Intracardiac wireless communication is crucial for the development of multi-chamber leadless cardiac pacemakers (LCP). However, the time-varying characteristics of intracardiac channel pose major challenges. As such, mastering the dynamic conduction properties of the intracardiac channel and modeling the equivalent time-varying channel are imperative for realizing LCP multi-chamber pacing.

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Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens.

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