Search Results (3,048)

Introduction: Neuroinflammation is a common pathological hallmark of Coronavirus Disease 2019 (COVID-19) and epilepsy; however, their shared immunogenomic mechanisms remain poorly defined. This study explores shared immune-inflammatory transcriptomic signatures and identifies potential repositioning therapeutics. Methods: We integrated single-cell RNA-seq data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and healthy donors (GSE149689), and bulk RNA-seq data from hippocampal tissue of patients with Temporal Lobe Epilepsy with Hippocampal Sclerosis (TLE-HS) and healthy controls (GSE256068). Common Differentially Expressed Genes (DEGs) were identified and subjected to GO/KEGG enrichment, a PPI network, hub gene detection (cytoHubba), and transcriptional regulation analysis (ENCODE-based TF/miRNA networks). Drug repositioning was performed using the LINCS L1000 database. Results: We identified 25 DEGs shared across datasets, including 22 upregulated genes enriched in cytokine–cytokine receptor interaction, NF-κB, and Toll-like receptor pathways. PPI analysis revealed a CX3CR1–TLR4-centered immune module. Gabapentin emerged as a promising repositioning candidate with potential to downregulate CX3CR1, TLR4, and selectin P ligand (SELPLG). Receiver Operating Characteristic (ROC) analysis confirmed the diagnostic value of these targets (AUC > 0.90 in epilepsy). A mechanistic model was proposed to illustrate Gabapentin’s dual action on microglial polarization and cytokine suppression. Conclusions: Our results reveal a shared CX3CR1–TLR4–NF-κB inflammatory axis in COVID-19 and epilepsy, supporting Gabapentin as a potential dual-action immunomodulator. These findings reveal a previously underappreciated immunomodulatory role for Gabapentin, providing mechanistic rationale for its repositioning in neuroinflammatory conditions beyond seizure control. Full article

Lithium-ion batteries (LIBs) have garnered extensive application across various domains. However, frequent safety incidents associated with these LIBs have emerged as a significant impediment to their further advancement. Consequently, there is an urgent necessity to develop a novel fire extinguishing agent that possesses both rapid fire suppression and efficient cooling capabilities, thereby effectively mitigating the occurrence and propagation of fires in LIBs. This study pioneers the development of an adaptive thermosensitive microcapsule (TM) fire extinguishing agent synthesized via in situ polymerization. The TM encapsulates a ternary composite core—perfluorohexanone (C₆F₁₂O), heptafluorocyclopentane (C₅H₃F₇), and 2-bromo-3,3,3-trifluoropropene (2-BTP)—within a melamine–urea–formaldehyde (MUF) resin shell. The TM was prepared via in situ polymerization, combined with FE-SEM, FTIR, TG–DSC, and laser particle size analysis to verify that the TM had a uniform particle size and complete coating structure. The results demonstrate that the TM can effectively suppress the thermal runaway (TR) of LIBs through the synergistic effects of physical cooling, chemical suppression, and gas isolation. Specifically, the peak TR temperature of a single-cell LIB is reduced by 14.0 °C, and the heating rate is decreased by 0.17 °C/s. Additionally, TM successfully blocked the propagation of TR thereby preventing its spread in the dual-LIB module test. Limitations of single-component agents are overcome by this innovative system by leveraging the ternary core’s complementary functionalities, enabling autonomous TR suppression without external systems. Furthermore, the TM design integrates precise thermal responsiveness, environmental friendliness, and cost-effectiveness, offering a transformative safety solution for next-generation LIBs. Full article

Time-lapse microscopy of mesenchymal stem cell (MSC) cultures allows for the quantitative observation of their self-renewal, proliferation, and differentiation. However, the rigorous comparison of two conditions, baseline (A) versus perturbation (B) (the addition of molecular factors, environmental shifts, genetic modification, etc.), remains difficult because morphology, division timing, and migratory behavior are highly heterogeneous at the single-cell scale. MSCs can be used as an in vitro model to study cell morphology and kinetics in order to assess the effect of, for example, gene therapy and prime editing in the near future. By combining static, frame-wise morphology with dynamic descriptors, we can obtain weight profiles that highlight which morphological and behavioral dimensions drive divergence. In this study, we present A/B Cells Policy: a modular, open-source Python package implementing a robust cell tracking pipeline. It integrates a YOLO-based architecture as a two-stage assignment framework with fallback and recovery passes, re-identification of lost tracks, and lineage reconstruction. The framework links descriptive statistics to a transferable system, opening up avenues for regenerative medicine, pharmacology, and early translational pipelines. It does this by providing an interpretable, measurement-based bridge between in vitro imaging and in silico intervention strategy planning. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis. T cell exhaustion (TEX) is a key factor in tumor immune evasion and therapeutic resistance. In this study, we integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to characterize TEX-related transcriptional features in HCC. Methods: We first computed TEX scores for each sample using a curated 65-gene signature and classified them into high-TEX and low-TEX groups by the median score. Differentially expressed genes were identified separately in scRNA-seq and bulk RNA-seq data, then intersected to retain shared candidates. A 26-gene prognostic signature was derived from these candidates via univariate Cox and LASSO regression analysis. Results: The high-TEX group exhibited increased expression of immune checkpoint molecules and antigen presentation molecules, suggesting a tumor microenvironment that is more immunosuppressive but potentially more responsive to immunotherapy. Functional enrichment analysis and protein–protein interaction (PPI) network construction further validated the roles of these genes in immune regulation and tumor progression. Conclusions: This study provides a comprehensive characterization of the TEX landscape in HCC and identifies a robust gene signature associated with prognosis and immune infiltration. These findings highlight the potential of targeting TEX-related genes for personalized immunotherapeutic strategies in HCC. Full article

Background: Ovarian cancer is an immunologically cold tumor that is treated with surgery and a chemotherapy regimen of platinum agents with taxanes. Paradoxically, elevated levels of several immune markers are effective at predicting prognosis for patients with ovarian cancer, though it is not clear how chemotherapy might influence this. Chemotherapy elicits immunogenic cell death, yet tumor-controlling doses of chemotherapy are also immunotoxic. Objectives: To evaluate interactions of chemotherapy with the immune system, we studied the impact of chemotherapy in an aggressive mouse model of ovarian cancer developed within our lab. Methods: Using a single-cell transcriptomics sequencing approach, supported by flow cytometry, we evaluated the influence of a first-line therapy, cisplatin and docetaxel, and a second-line therapy, pegylated liposomal doxorubicin (PLD), on control of tumor growth and on tumor-associated immune populations of cells. Results: Both chemotherapy approaches were effective at controlling tumor growth and selectively depleted tumor cells from distinct transcriptional clusters. Both chemotherapies also resulted in relative increases in immune populations compared to untreated tumor-bearing mice, but immune populations from PLD-treated mice were more abundant and expressed a greater fraction of maturity-associated transcripts and increased proportions of tumor resident macrophage populations. PLD treatment selectively upregulated MHC class II on tumor cells, and this could be replicated in vitro across ovarian cancer cell lines and in patient tumor cells ex vivo. Conclusions: Altogether, the results support the notion that PLD has a greater capacity for immunopotentiation, which may be important to consider if immunotherapy approaches are adapted for ovarian tumors in the future. Full article

Red blood cell (RBC), accounting for approximately 45% of total blood volume, are essential for oxygen delivery and carbon dioxide removal. Their unique biconcave morphology, high surface area-to-volume ratio, and remarkable deformability enable them to navigate microvessels narrower than their resting diameter, ensuring efficient microcirculation. RBC deformability is primarily determined by membrane viscoelasticity, cytoplasmic viscosity, and cell geometry, all of which can be altered under various physiological and pathological conditions. Reduced deformability is a hallmark of numerous diseases, including sickle cell disease, malaria, diabetes mellitus, sepsis, ischemia–reperfusion injury, and storage lesions in transfused blood. As these mechanical changes often precede overt clinical symptoms, RBC deformability is increasingly recognized as a sensitive biomarker for disease diagnosis, prognosis, and treatment monitoring. Over the past decades, diverse techniques have been developed to measure RBC deformability. These include single-cell methods such as micropipette aspiration, optical tweezers, atomic force microscopy, magnetic twisting cytometry, and quantitative phase imaging; bulk approaches like blood viscometry, ektacytometry, filtration assays, and erythrocyte sedimentation rate; and emerging microfluidic platforms capable of high-throughput, physiologically relevant measurements. Each method captures distinct aspects of RBC mechanics, offering unique advantages and limitations. This review synthesizes current knowledge on the pathophysiological significance of RBC deformability and the methods for its measurement. We discuss disease contexts in which deformability is altered, outline mechanical models describing RBC viscoelasticity, and provide a comparative analysis of measurement techniques. Our aim is to guide the selection of appropriate approaches for research and clinical applications, and to highlight opportunities for developing robust, clinically translatable diagnostic tools. Full article

APOBEC3B (A3B) has been implicated in host–virus interactions, but its role in SARS-CoV-2 infection is unclear. Here, we demonstrate that A3B is overexpressed in bronchoalveolar lavage fluid (BALF) cells from severe COVID-19 patients compared to those with mild disease. A3B knockdown in Caco-2 cells significantly reduces SARS-CoV-2 infectivity, likely through attenuation of the PKR-mediated integrated stress response, a pathway proposed to promote SARS-CoV-2. Single-cell RNA sequencing (scRNA-seq) data suggest that BALF cells from severe COVID-19 patients exhibit a repressed state for cellular translation, potentially mediated by eIF2α phosphorylation. However, in A549-ACE2 cells, SARS-CoV-2 does not activate PKR, but A3B knockdown still reduces SARS-CoV-2 infectivity, suggesting an alternative mechanism of action in different cellular contexts. To further investigate A3B’s role in severe COVID-19, we employed Geneformer, a transformer-based machine learning model, which predicted that A3B knockout would perturb AMPD2 (adenosine monophosphate deaminase 2), a key enzyme in purine metabolism and immune regulation. We validated this prediction using bulk RNA-seq and clinical scRNA-seq data, confirming that AMPD2 expression is downregulated in severe COVID-19 but restored upon A3B knockdown. Together, these findings suggest that A3B plays a proviral role in SARS-CoV-2 infection by modulating translational control and immune regulatory networks, warranting further studies to elucidate the underlying mechanistic details. Full article

Background: Ovarian cancer ranks as the fifth leading cause of cancer-related mortality among women worldwide. Owing to its insidious onset and lack of early symptoms, over 70% of patients are diagnosed at advanced stages. Methods: This study provides a comprehensive transcriptomic analysis of tumor-infiltrating CD4⁺ T cells in ovarian cancer, highlighting regulatory T cells (Tregs) as the dominant subset. By integrating seven multicenter ovarian cancer single-cell RNA-seq datasets, a robust metadata resource was created for detailed Treg investigation. Using the BayesPrism algorithm, Treg scores from TCGA bulk RNA-seq data enabled patient stratification into high and low Treg groups. These findings were further validated through survival analyses across five independent bulk RNA-seq cohorts. We experimentally validated the inhibitory role of Tregs in modulating CD8⁺ T-cell activity in ovarian cancer. Results: We conducted an in-depth investigation into the clustering patterns, differentiation trajectories, intercellular interactions, and enrichment profiles of tumor-infiltrating T cells in ovarian cancer. Among the seven functionally defined subclusters (C1–C7), we delineated two distinct “terminal states” of CD4⁺ T-cell differentiation: FOXP3⁺ regulatory T cells and STMN1⁺ proliferative T cells. The OCSCDs dataset comprises seven datasets totaling 137,648 single cells. Using the TCGA dataset, we quantified the proportion of tumor-infiltrating regulatory T cells (Tregs) in OCSCDs through the BayesPrism algorithm and performed survival analyses across five independent bulk RNA-seq datasets from different platforms. Conclusions: Our results establish a framework for studying Treg biology in ovarian cancer and these cells may be become an important point in the field of immunotherapy. Full article

Hepatocellular carcinoma (HCC) is a malignancy that is notorious for its dismal prognosis. Dysregulation of the tumor microenvironment (TME) in HCC has emerged as a key hallmark in determining disease progression and the response to immunotherapy. The aim of this study was to identify novel TME regulators that contribute to therapeutic resistance, thus providing mechanistic insights for targeted interventions. The expression of SMIM25 was evaluated in the the Cancer Genome Atlas-Liver Hepatocellular Carcinoma(TCGA-LIHC) and Guangxi HCC cohorts, and its clinicopathological significance was assessed. RNA sequencing and bioinformatics analyses were performed to elucidate the potential impact of elevated SMIM25 levels. Immunohistochemistry (IHC) and single-cell mass cytometry (CyTOF) were employed to examine the cellular composition of the tumor microenvironment. The biological effects of SMIM25 on cell proliferation and migration were studied in vitro using 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium Bromide(MTT) and wound healing assays, while its impact on tumor growth was evaluated in vivo in a nude mouse model. Transcriptomic and single-cell proteomic analyses were integrated to explore the mechanism by which SMIM25 affects the progression of HCC. The expression of SMIM25 was significantly up-regulated in both HCC tissues and cell lines (p < 0.05). RNA sequencing analyses revealed a significant positive correlation between SMIM25 expression and immunosuppression, and between SMIM25 expression and extracellular matrix(ECM)-related molecular features. Single-cell mass cytometry revealed two immunosuppressive cell clusters that were enriched in HCC patients with high SMIM25 expression. Moreover, SMIM25 was associated with immune exclusion and ECM remodeling signals in the TME of HCC. SMIM25 overexpression was associated with the expression of the tumor inflammatory marker cyclooxygenase-2(COX-2), and a COX-2 inhibitor could partially reverse the biological phenotype associated with SMIM25 expression in HCC cells (p < 0.05). Further transcriptome analysis in immunotherapy cohorts suggested the SMIM25-COX-2 axis might have predictive value for the response to immunotherapy. Our results suggest that SMIM25 may serve as a biomarker for the prognosis of HCC patients and may also be a predictive biomarker for the response to immunotherapy, enabling more precise and personalized HCC treatment. Full article

Chronic pain is a complex and persistent condition involving sustained nociceptive input, maladaptive neuroplastic changes, and neuroimmune interactions. Central to its pathophysiology is the dysregulation of neuromodulatory signaling pathways, including neurotransmitters (e.g., dopamine, serotonin, norepinephrine), neuropeptides (e.g., substance P, CGRP), and neurotrophic factors (e.g., BDNF), which modulate both central and peripheral sensitization mechanisms. In disorders such as fibromyalgia, altered monoaminergic transmission has been implicated in the attenuation of descending inhibitory control, thereby enhancing pain perception and reducing responsiveness to conventional therapies. Concurrently, neuroinflammation, driven by glial cell activation and cytokine release, further exacerbates neuronal excitability and reinforces maladaptive signaling loops. Recent technological advances, including transcriptomic profiling, functional neuroimaging, and single-cell RNA sequencing, have provided new insights into patient-specific patterns of neuromodulatory dysfunction, highlighting potential biomarkers for disease stratification and therapeutic targeting. These developments support the hypothesis that dysregulated neuromodulatory circuits not only underlie diverse chronic pain phenotypes but may also serve as intervention points for precision medicine. This narrative review synthesizes current evidence on the roles of neuromodulatory systems in chronic pain, focusing on synaptic plasticity, nociceptor sensitization, and neuroimmune crosstalk. By integrating preclinical findings with clinical observations, we propose a mechanistic framework for understanding pain chronification and guiding future therapeutic strategies. Harnessing neuromodulatory targets, whether pharmacologically or via neuromodulation technologies, could offer more personalized and effective approaches to chronic pain management. Full article

Exosomes are extracellular vesicles secreted by all cell types, transporting nucleic acids, proteins, lipids, and metabolites. They are known to influence tumor biology by modulating cellular proliferation, invasion, and apoptosis. In acute myeloid leukemia (AML), the precise functions of exosomes remain incompletely characterized. Here, we present an integrated multi-omics study combining single-cell RNA sequencing (scRNA-seq) of bone marrow aspirates from AML patients and healthy donors with transcriptomic profiling of purified exosomes. This approach uniquely allowed us to link cellular transcriptional states with exosome content and function. We discovered a significant upregulation of exosome-related transcriptional activity in AML cells. Purified AML exosomes showed enhanced translational, transcriptional, and metabolic activity compared to those from healthy donors. Notably, these exosomes were highly enriched in transforming growth factor-β (TGF-β), a key regulator of tumor progression. Functional assays confirmed that AML-derived exosomes promote leukemic cell proliferation and migration. Mechanistically, these effects are mediated via activation of the Smad2/3–MMP2 and ERK1/2 signaling pathways. Furthermore, cell–cell interaction analysis revealed that AML exosomes reshape the bone marrow immune microenvironment by upregulating multiple immunoregulatory genes and pathways, revealing a novel immunomodulatory role. This study provides the first integrative demonstration that TGF-β–enriched exosomes actively drive AML progression through combined enhancement of leukemic aggressiveness and immune microenvironment remodeling. Our findings highlight exosomes and their signaling cascades as promising therapeutic targets, offering new avenues for innovative AML treatments. Full article

The production of microbial lipids through single-cell oil (SCO) technologies has gained increasing attention as a sustainable alternative source of lipids for industrial applications. This development is driven by the limitations of plant-based oils, particularly their competition with food production and demand for arable land. Cutaneotrichosporon oleaginosus has been recognized as one of the most promising oleaginous microorganisms for efficient SCO production. To improve sustainability and economic viability, it is vital to understand the underlying metabolic mechanism of SCO production as well as needs and limitations in bioprocess engineering for the efficient utilization of carbon sources derived from diverse agricultural and industrial side streams. This review focuses on recent studies exploring the potential of SCO production through C. oleaginosus in a bioprocess context through the application of low-cost agro-industrial by-products as alternative carbon sources aiming to supply lipid raw materials for various industrial applications. C. oleaginosus can grow on different agro-industrial waste-derived substrates, including lignocellulosic biomass hydrolysates, biodiesel production process side streams, chitin-based by-products, cheese whey permeates, fungal biomass hydrolysates and algal biomass hydrolysates. These substrates contain various carbon sources, such as glucose, galactose, mannose, xylose, lactose, N-acetyl-glucosamine and glycerol, facilitating efficient SCO production. Additionally, the specific composition of SCO sourced from C. oleaginosus, including the presence of functional compounds like squalene and prevalent long-chain unsaturated fatty acids in its fatty acid profile, make it an ideal option to be used as a raw material in cosmetics, biofuel and food products. This comprehensive overview aims to shed light on the potential of C. oleaginosus in leveraging carbon source alternatives for sustainable SCO production for multifaceted, industrial applications of SCO. Full article

Transcriptomic profiling is a powerful tool for dissecting the cellular and molecular complexity of ocular tissues, providing insights into retinal development, corneal disease, macular degeneration, and glaucoma. With the expansion of microarray, bulk RNA sequencing (RNA-seq), and single-cell RNA-seq technologies, artificial intelligence (AI) has emerged as a key strategy for analyzing high-dimensional gene expression data. This review synthesizes AI-enabled transcriptomic studies in ophthalmology from 2019 to 2025, highlighting how supervised and unsupervised machine learning (ML) methods have advanced biomarker discovery, cell type classification, and eye development and ocular disease modeling. Here, we discuss unsupervised techniques, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and weighted gene co-expression network analysis (WGCNA), now the standard in single-cell workflows. Supervised approaches are also discussed, including the least absolute shrinkage and selection operator (LASSO), support vector machines (SVMs), and random forests (RFs), and their utility in identifying diagnostic and prognostic markers in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, keratoconus, thyroid eye disease, and posterior capsule opacification (PCO), as well as deep learning frameworks, such as variational autoencoders and neural networks that support multi-omics integration. Despite challenges in interpretability and standardization, explainable AI and multimodal approaches offer promising avenues for advancing precision ophthalmology. Full article

Background/Objectives: Breast cancer (BC) is the most prevalent cancer worldwide, with approximately 40% of early-stage BC patients developing recurrence despite initial treatments. Current diagnostic methods, such as mammography and solid tissue biopsies, face limitations in sensitivity, accessibility, and the ability to characterize tumor heterogeneity or monitor systemic disease progression. Methods: To address these gaps, this study investigates a fully automated analysis workflow using data derived from fluorescent Whole-Slide Imaging (fWSI) for detecting and classifying rare cells (circulating tumor and tumor microenvironment cells) in peripheral blood samples. Our methodology integrates supervised machine learning algorithms for rare event detection, immunofluorescence-based classification, and statistical quantification of cellular features. Results: Using a fWSI dataset of 534 cancer and non-cancer peripheral blood samples, the automated model demonstrated high concordance with manual annotation, achieving up to 98.9% accuracy and a precision-sensitivity AUC of 83.2%. Morphometric analysis of rare cells identified significant differences between normal donors, early-stage BC, and late-stage BC cohorts, with distinct clusters emerging in late-stage BC. Conclusions: These findings highlight the potential of liquid biopsy and algorithmic approaches for improving BC diagnostics and staging, offering a scalable, minimally invasive solution to enhance clinical decision-making. Future work aims to refine the automated framework to minimize errors and improve the robustness across diverse cohorts. Full article

Background and objective: Periodontitis, a chronic inflammatory disease driven by host immune dysregulation, leads to progressive destruction of periodontal tissues. This study employed an integrative approach combining single-cell transcriptomics, hierarchical weighted gene co-expression network analysis (hdWGCNA), and deep learning algorithms to identify key biomarkers associated with neutrophil degranulation in periodontitis, aiming to establish diagnostic models for early detection and precision interventions. Methods: We integrated single-cell RNA sequencing (scRNA-seq) data from human gingival tissues with bulk transcriptomic datasets. Pathogenic neutrophil subsets were characterized via pseudotime trajectory and cell–cell communication analyses. Hierarchical weighted gene co-expression network analysis (hdWGCNA) identified functional modules linked to degranulation. Machine learning and a convolutional neural network (CNN) model combining gene expression and immune cell profiles were developed for diagnosis. Results: scRNA-seq revealed a neutrophil subpopulation significantly increased infiltration in periodontitis, with cell–cell communication and pseudotime trajectory analyses demonstrating amplified inflammatory crosstalk. hdWGCNA identified the turquoise module enriched in PD-KEY-Neutrophils, containing hub genes linked to neutrophil degranulation and complement activation. Immune infiltration and non-negative matrix factorization linked high-degranulation neutrophil signatures to the periodontal immunity microenvironment. Machine learning demonstrated that the neutrophil degranulation-associated genes effectively distinguish diseased gingival tissue, suggesting their potential to predict periodontitis. Finally, integrating transcriptomic and immunological data, we developed a gene-immune CNN deep learning model accurately diagnosed periodontitis in diverse cohorts (AUC = 0.922). Conclusions: Our study identified a pathogenic neutrophil subpopulation driving periodontitis through degranulation and inflammation. The neutrophil degranulation genes serve as critical biomarkers, offering new insights for clinical diagnosis and treatment of periodontitis. Full article