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Preliminary Evidence for Perturbation-Based tACS-EEG Biomarkers of Gamma Activity in Alzheimer's Disease.
Int J Geriatr Psychiatry
January 2025
Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Background: Alzheimer's disease (AD) is characterized by impaired inhibitory circuitry and GABAergic dysfunction, which is associated with reduced fast brain oscillations in the gamma band (γ, 30-90 Hz) in several animal models. Investigating such activity in human patients could lead to the identification of novel biomarkers of diagnostic and prognostic value. The current study aimed to test a multimodal "Perturbation-based" transcranial Alternating Current Stimulation-Electroencephalography (tACS)-EEG protocol to detect how responses to tACS in AD patients correlate with patients' clinical phenotype.
View Article and Find Full Text PDFAnalysis of Chemical Components of Penicillium chrysogenum Crude Extract Using Liquid Chromatography Coupled With Q-Exactive Orbitrap Mass Spectrometry Combined With GNPS Molecular Networking.
Rapid Commun Mass Spectrom
April 2025
School of Pharmaceutical Science and Technology, Faculty of Medicine, Tianjin University, Tianjin, Tianjin, China.
A Chinese isolate of the fungus Penicillium chrysogenum was analyzed using liquid chromatography coupled with Q-Exactive Orbitrap mass spectrometry combined with Global Natural Products Social Networking (GNPS) on culture condition leading to the rapid identification of 20 secondary metabolites. Among them are eight polyketones, two phthalides, six diketopiperazine alkaloids, and others. A meleagrine network was examined and proposed as a promising candidate for new natural products.
View Article and Find Full Text PDFA prediction study on the occurrence risk of heart disease in older hypertensive patients based on machine learning.
BMC Geriatr
January 2025
Department of Cardiology, The Second Hospital & Clinical Medical School, Lanzhou University, No. 82 Cuiyingmen, Lanzhou, 730000, China.
Objective: Constructing a predictive model for the occurrence of heart disease in elderly hypertensive individuals, aiming to provide early risk identification.
Methods: A total of 934 participants aged 60 and above from the China Health and Retirement Longitudinal Study with a 7-year follow-up (2011-2018) were included. Machine learning methods (logistic regression, XGBoost, DNN) were employed to build a model predicting heart disease risk in hypertensive patients.
Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models.
Sci Rep
January 2025
Department of Orthopaedics, Traditional Chinese Medical Hospital of Gansu Province, Qilihe District, Guazhou Street 418, Lanzhou, 730050,, Gansu, China.
Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio.
View Article and Find Full Text PDFReal-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models.
Sensors (Basel)
December 2024
Department of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, Lebanon.
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication.
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