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| http://dx.doi.org/10.1001/jamanetworkopen.2023.24100 | DOI Listing |
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Integration of Microarray Data and Single-Cell Sequencing Analysis to Explore Key Genes Associated with Macrophage Infiltration in Heart Failure.
J Inflamm Res
December 2024
Department of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, People's Republic of China.
Background: Cardiac macrophages are a heterogeneous population with high plasticity and adaptability, and their mechanisms in heart failure (HF) remain poorly elucidated.
Methods: We used single-cell and bulk RNA sequencing data to reveal the heterogeneity of non-cardiomyocytes and assess the immunoreactivity of each subpopulation. Additionally, we employed four integrated machine learning algorithms to identify macrophage-related genes with diagnostic value, and in vivo validation was performed.
Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow.
J Chem Inf Model
December 2024
Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
Machine learning (ML) methods provide a pathway to accurately predict molecular properties, leveraging patterns derived from structure-property relationships within materials databases. This approach holds significant importance in drug discovery and materials design, where the rapid, efficient screening of molecules can accelerate the development of new pharmaceuticals and chemical materials for highly specialized target application. Unsupervised and self-supervised learning methods applied to graph-based or geometric models have garnered considerable traction.
View Article and Find Full Text PDFDeep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?
Algorithms
December 2023
Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain.
View Article and Find Full Text PDFPhenotypic clustering of repaired Tetralogy of Fallot using unsupervised machine learning.
Int J Cardiol Congenit Heart Dis
September 2024
The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, 1389 Blalock, Baltimore, 21287, MD, USA.
Objective: Repaired Tetralogy of Fallot (rTOF), a complex congenital heart disease, exhibits substantial clinical heterogeneity. Accurate prediction of disease progression and tailored patient management remain elusive. We aimed to categorize rTOF patients into distinct phenotypes based on clinical variables and variables obtained from cardiac magnetic resonance (CMR) imaging.
View Article and Find Full Text PDFAlthough evidence-based treatments for Prolonged Grief Disorder (PGD) exist, pretreatment characteristics associated with differential improvement trajectories have not been identified. To identify clinical factors relevant to optimizing PGD treatment outcomes, we used unsupervised and supervised machine learning to study treatment effects from a double-blinded, placebo-controlled, randomized clinical trial. Participants were randomized into four treatment groups for 20 weeks: citalopram with grief-informed clinical management, citalopram with prolonged grief disorder therapy (PGDT), pill placebo with PGDT, or pill placebo with clinical management.
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