Background: Parkinson's disease (PD) often presents with subtle early signs, making diagnosis difficult. F-DOPA PET imaging provides a reliable measure of dopaminergic function and is a primary tool for early PD diagnosis. This study aims to evaluate the ability of machine-learning (ML) extracted EEG features to predict F-DOPA results and distinguish between PD and non-PD patients.
View Article and Find Full Text PDFPersonalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we use a computational approach termed Disruption Networks to generate a data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. This data type extends beyond the "feature level space", to the "relations space", by quantifying individual-level breaking or rewiring of cross-feature relations.
View Article and Find Full Text PDFBackground: With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools.
Methods: This prospective pilot study was conducted between November 2020 and September 2021.
Proteins and enzymes in the cell nucleus require physical access to their DNA target sites in order to perform genomic tasks such as gene activation and transcription. Hence, chromatin accessibility is a central regulator of gene expression, and its genomic profile holds essential information on the cell type and state. We utilized the Dam methyltransferase in combination with a fluorescent cofactor analogue to generate fluorescent tags in accessible DNA regions within the cell nucleus.
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