AIMC Topic: Leukocytes, Mononuclear
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Morphological profiling using machine learning reveals emergent subpopulations of interferon-γ-stimulated mesenchymal stromal cells that predict immunosuppression.
Cytotherapy
Nov 28, 2018
BACKGROUND: Although a preponderance of pre-clinical data demonstrates the immunosuppressive potential of mesenchymal stromal cells (MSCs), significant heterogeneity and lack of critical quality attributes (CQAs) based on immunosuppressive capacity l...
Selective cytotoxic and genotoxic activities of 5-(2-bromo-5-methoxybenzylidene)-thiazolidine-2,4-dione against NCI-H292 human lung carcinoma cells.
Pharmacological reports : PR
Nov 22, 2017
BACKGROUND: Thiazolidine-2,4-dione ring system is used as a pharmacophore to build various heterocyclic compounds aimed to interact with biological targets. In the present study, benzylidene-2,4-thiazolidinedione derivatives (compounds 2-5) were synt...
GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.
Computers in biology and medicine
Jun 1, 2025
OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.
Single-cell RNA sequencing reveals immunological link between house dust mite allergy and childhood asthma.
Scientific reports
May 14, 2025
Allergic asthma in children is typically associated with house dust mites (HDM) as the key allergen. Nevertheless, the diagnostic rate remains below 60% due to the absence of specific symptoms and diagnostic markers, which hinders the implementation ...
Oxidative Phosphorylation Pathway in Ankylosing Spondylitis: Multi-Omics Analysis and Machine Learning.
International journal of rheumatic diseases
May 1, 2025
INTRODUCTION: Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the axial skeleton, characterized by immune microenvironment dysregulation and elevated cytokines like TNF-α and IL-17. Mitochondrial oxidative phosphorylation (OXP...
An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.
Genes to cells : devoted to molecular & cellular mechanisms
Mar 1, 2025
Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activi...
Unveiling the Immune Landscape of Delirium through Single-Cell RNA Sequencing and Machine Learning: Towards Precision Diagnosis and Therapy.
Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society
Jan 1, 2025
BACKGROUND: Postoperative delirium (POD) poses significant clinical challenges regarding its diagnosis and treatment. Identifying biomarkers that can predict and diagnose POD is crucial for improving patient outcomes.
Artificial Intelligence-Based Counting Algorithm Enables Accurate and Detailed Analysis of the Broad Spectrum of Spot Morphologies Observed in Antigen-Specific B-Cell ELISPOT and FluoroSpot Assays.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Antigen-specific B-cell ELISPOT and multicolor FluoroSpot assays, in which the membrane-bound antigen itself serves as the capture reagent for the antibodies that B cells secrete, inherently result in a broad range of spot sizes and intensities. The ...
BEENE: deep learning-based nonlinear embedding improves batch effect estimation.
Bioinformatics (Oxford, England)
Aug 1, 2023
MOTIVATION: Analyzing large-scale single-cell transcriptomic datasets generated using different technologies is challenging due to the presence of batch-specific systematic variations known as batch effects. Since biological and technological differe...
Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.
Briefings in bioinformatics
Sep 2, 2021
Single-cell transcriptomics technologies have vast potential in advancing our understanding of cellular heterogeneity in complex tissues. While methods to interpret single-cell transcriptomics data are developing rapidly, challenges in most analysis ...
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