Search Results (642)

This paper introduces a theoretical large-scale radio channel model for the downlink in cellular systems, aimed at estimating variations in received signal power at the user terminal as a function of device mobility. This enables applications such as direction-of-arrival (DoA) estimation, estimating power at subsequent points based on received power, and detection of coverage anomalies. The model is validated using real-world measurements from urban and suburban environments, achieving a maximum estimation error of 7.6%. In contrast to conventional models like Okumura–Hata, COST-231, Third Generation Partnership Project (3GPP) stochastic models, or ray-tracing techniques, which estimate average power under static conditions, the proposed model captures power fluctuations induced by terminal movement, a factor often neglected. Although advanced techniques such as wave-domain processing with intelligent metasurfaces can also estimate DoA, this model provides a simpler, geometry-driven approach based on empirical traces. While it does not incorporate infrastructure-specific characteristics or inter-cell interference, it remains a practical solution for scenarios with limited information or computational resources. Full article

Miller–Dieker Syndrome (MDS) is a rare neurodevelopmental disorder caused by a heterozygous deletion of approximately 26 genes within the MDS locus of human chromosome 17. MDS, which affects 1 in 100,000 babies, can lead to a range of phenotypes, including lissencephaly, severe neurological defects, distinctive facial abnormalities, cognitive impairments, seizures, growth retardation, and congenital heart and liver abnormalities. One hallmark feature of MDS is an unusually smooth brain surface due to abnormal neuronal migration during early brain development. Several genes located within the MDS locus have been implicated in the pathogenesis of MDS, including PAFAH1B1, YWHAE, CRK, and METTL16. These genes play a role in the molecular and cellular pathways that are vital for neuronal migration, the proper development of the cerebral cortex, and protein translation in MDS. Improved model systems, such as MDS patient-derived organoids and multi-omics analyses indicate that WNT/β-catenin signaling, calcium signaling, S-adenosyl methionine (SAM) homeostasis, mammalian target of rapamycin (mTOR) signaling, Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling, and others are dysfunctional in MDS. This review of MDS integrates details at the clinical level alongside newly emerging details at the molecular and cellular levels, which may inform the development of novel therapeutic strategies for MDS. Full article

Neurodegenerative diseases (NDDs) such as Alzheimer’s, Parkinson’s, ALS, and Huntington’s pose a growing global challenge due to their complex pathobiology and aging demographics. Once considered as cellular debris, small extracellular vesicles (sEVs) are now recognized as active mediators of intercellular signaling in NDD progression. These nanovesicles (~30–150 nm), capable of crossing the blood–brain barrier, carry pathological proteins, RNAs, and lipids, facilitating the spread of toxic species like Aβ, tau, TDP-43, and α-synuclein. sEVs are increasingly recognized as valuable diagnostic tools, outperforming traditional CSF biomarkers in early detection and disease monitoring. On the therapeutic front, engineered sEVs offer a promising platform for CNS-targeted delivery of siRNAs, CRISPR tools, and neuroprotective agents, demonstrating efficacy in preclinical models. However, translational hurdles persist, including standardization, scalability, and regulatory alignment. Promising solutions are emerging, such as CRISPR-based barcoding, which enables high-resolution tracking of vesicle biodistribution; AI-guided analytics to enhance quality control; and coordinated regulatory efforts by the FDA, EMA, and ISEV aimed at unifying identity and purity criteria under forthcoming Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines. This review critically examines the mechanistic roles, diagnostic potential, and therapeutic applications of sEVs in NDDs, and outlines key strategies for clinical translation. Full article

Background and Objectives: Nicotine is the most well-studied toxic substance in cigarette smoke and e-cigarette vape. However, smoke and vape are composed of other components that have a negative impact on health. The objective of this study is to investigate whether nicotine has a distinctive impact on molecular mechanisms in stem cells. Methods: The cellular impact of nicotine on the regenerative capacity of human dental pulp stem cells (DPSCs) and the microRNA (miRNA) profile was examined. Bioinformatic analysis was performed to identify miRNA-regulated cellular pathways associated with nicotine exposure. These pathways were then compared to those induced by cigarette smoke condensate (CSC). Results: Prolonged exposure to nicotine significantly impaired the regeneration of DPSCs and changed the expression of miRNAs. Nicotine upregulated the expression of hsa-miR-7977, hsa-miR-3178, and hsa-miR-10400-5p compared to vehicle control. Interestingly, nicotine did not change the expression of hsa-miR-29b-3p, hsa-miR-199b-5p, hsa-miR-26b-5p, or hsa-miR-26a-5p compared to the control. However, the expressions of these miRNAs were significantly altered when compared to CSC treatment. Further analysis revealed that nicotine was distinctively associated with certain miRNA-targeted pathways including apoptosis, ErbB, MAPK signaling, PI3K-Akt, TGF-b signaling, and Wnt signaling. Conclusions: Our work provides evidence on the distinctive miRNA signature induced by nicotine. The information will be important for identifying the unique molecular pathways downstream of nicotine from smoking and vaping in different individuals, providing a new direction for personalized disease prevention, prognosis, and treatment. Full article

Urinary extracellular vesicles (U-EVs) are gaining increasing interest as non-invasive liquid biopsy tools for clinical use. Prostate cancer (PCa) is amongst the highest cancer-related cause of death in men, and therefore, the identification of non-invasive robust biomarkers is of high importance. This study assessed U-EV profiles from individuals affected by PCa at Gleason scores 6–9, compared with healthy controls. U-EVs were characterised and assessed for proteomic cargo content by LC-MS/MS analysis. The U-EV proteomes were compared for enrichment of gene ontology (GO), KEGG, and Reactome pathways, as well as disease–gene associations. U-EVs ranged in size from 50 to 350 nm, with the majority falling within the 100–200 nm size range for all groups. U-EV protein cargoes from the PCa groups differed significantly from healthy controls, with 16 protein hits unique to the GS 6–7 and 88 hits to the GS 8–9 U-EVs. Pathway analysis showed increased enrichment in the PCa U-EVs of biological process GO (5 and 37 unique to GS 6–7 and GS 8–9, respectively), molecular function GO (3 and 6 unique to GS 6–7 and GS 8–9, respectively), and cellular component GO (10 and 22 unique to GS 6–7 and GS 8–9, respectively) pathways. A similar increase was seen for KEGG pathways (11 unique to GS 8–9) and Reactome pathways (102 unique to GS 8–9). Enrichment of disease–gene associations was also increased in the PCa U-EVs, with highest differences for the GS 8–9 U-EVs (26 unique terms). The pathway enrichment in the PCa U-EVs was related to several key inflammatory, cell differentiation, cell adhesion, oestrogen signalling, and infection pathways. Unique GO and KEGG pathways enriched for the GS 8–9 U-EVs were associated with cell–cell communication, immune and stress responses, apoptosis, peptidase activity, antioxidant activity, platelet aggregation, mitosis, proteasome, mRNA stability oxytocin signalling, cardiomyopathy, and several neurodegenerative diseases. Our findings highlight U-EVs as biomarkers to inform disease pathways in prostate cancer patients and offer a non-invasive biomarker tool for clinical use. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Background: Human induced pluripotent stem cell cardiomyocytes (hiPSC-CMs) allow for high-throughput evaluation of cardiomyocyte (CM) physiology in health and disease. While multimodality testing provides a large breadth of information related to electrophysiology, contractility, and intracellular signaling in small populations of iPSC-CMs, current technologies for analyzing these parameters are expensive and resource-intensive. Methods: We have designed a novel 2D/3D hybrid organoid system that can harness optical imaging techniques to assess electromechanical properties and calcium dynamics across CMs in a high-throughput manner. We validated our methods using a doxorubicin-based system, as the drug has well-characterized cardiotoxic, pro-arrhythmic effects. Results: This novel hybrid system provides the functional benefit of 3D organoids while minimizing optical interference from multilayered cellular systems through our cell-culture techniques that propagate organoids outwards into 2D iPSC-CM sheets. The organoids recapitulate contractile forces that are more robust in 3D structures and connectivity, while 2D CMs facilitate analysis at an individual cellular level, which recreated numerous doxorubicin-induced electrophysiologic and propagation abnormalities. Conclusions: Thus, we have developed a novel 2D/3D hybrid organoid model that employs an integrated optical analysis platform to provide a reliable high-throughput method for studying cardiotoxicity, providing valuable data on calcium, contractility, and signal propagation. Full article

In a multi-cell network, interference management between adjacent cells is a key factor that determines the performance of the entire cellular network. In particular, in order to control inter-cell interference while providing a high data rate to users, it is very important for the base station (BS) of each cell to appropriately control the transmit power in the downlink. However, as the number of cells increases, controlling the downlink transmit power at the BS becomes increasingly difficult. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based transmit power control scheme to maximize the sum rate in multi-cell networks. In particular, the proposed scheme incorporates a long short-term memory (LSTM) architecture into the MADRL scheme to retain state information across time slots and to use that information for subsequent action decisions, thereby improving the sum rate performance. In the proposed scheme, the agent of each BS uses only its local channel state information; consequently, it does not need to receive signal messages from adjacent agents. The simulation results show that the proposed scheme outperforms the existing MADRL scheme by reducing the amount of signal messages exchanged between links and improving the sum rate. Full article

Liver diseases encompass a wide range of etiologies and involve highly heterogeneous cellular environments, yet the specific cellular states through which genetic risk contributes to disease remain incompletely understood. In this study, we integrated genome-wide association study (GWAS) data from six liver diseases and two metabolic traits with transcriptomic profiles of approximately 168,000 human liver cells at single-cell resolution, using the single-cell disease relevance score (scDRS) approach. Our results revealed that disease-associated genetic signals are predominantly localized to non-parenchymal cells—particularly liver sinusoidal endothelial cells (LSECs), cholangiocytes, and specific subsets of lymphocytes. Notably, we identified marked intra-cell-type heterogeneity, with disease associations confined to specific subpopulations exhibiting immune activation or stress-responsive transcriptional programs. For example, autoimmune and viral liver diseases were linked to immunologically active LSECs and cholangiocytes, whereas their metabolically active counterparts showed no enrichment. These findings highlight the necessity of resolving liver cell complexity to uncover the functional basis of genetic risk and suggest that susceptibility to liver disease is driven by specialized cell states within broader cellular categories. Our study provides a refined cellular map of liver disease susceptibility, offering new perspectives for understanding pathogenic mechanisms and informing targeted therapeutic strategies. Full article

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) technology enables single-cell resolution analysis of chromatin accessibility, offering critical insights into gene regulation, epigenetic heterogeneity, and cellular differentiation across various biological contexts. However, existing cell annotation methods face notable limitations. Cross-omics approaches, which rely on single-cell RNA sequencing (scRNA-seq) as a reference, often struggle with data alignment due to fundamental differences between transcriptional and chromatin accessibility modalities. Meanwhile, intra-omics methods, which rely solely on scATAC-seq data, are frequently affected by batch effects and fail to fully utilize genomic sequence information for accurate annotation. To address these challenges, we propose scAttG, a novel deep learning framework that integrates graph attention networks (GATs) and convolutional neural networks (CNNs) to capture both chromatin accessibility signals and genomic sequence features. By utilizing the nucleotide sequences corresponding to scATAC-seq peaks, scAttG enhances both the robustness and accuracy of cell-type annotation. Experimental results across multiple scATAC-seq datasets suggest that scAttG generally performs favorably compared to existing methods, showing competitive performance in single-cell chromatin accessibility-based cell-type annotation. Full article

Stem-cell behavior is governed not solely by intrinsic genetic programs but by highly specialized microenvironments—or niches—that integrate structural, biochemical, and mechanical cues to regulate quiescence, self-renewal, and differentiation. This review traces the evolution of stem-cell niche biology from foundational embryological discoveries to its current role as a central determinant in tissue regeneration and disease. We describe the cellular and extracellular matrix architectures that define adult stem-cell niches across diverse organs and dissect conserved signaling axes—including Wnt, BMP, and Notch—that orchestrate lineage commitment. Emphasis is placed on how aging, inflammation, fibrosis, and metabolic stress disrupt niche function, converting supportive environments into autonomous drivers of pathology. We then examine emerging therapeutic strategies that shift the regenerative paradigm from a stem-cell-centric to a niche-centric model. These include stromal targeting (e.g., FAP inhibition), which are engineered scaffolds that replicate native niche mechanics, extracellular vesicles that deliver paracrine cues, and composite constructs that preserve endogenous cell–matrix interactions. Particular attention is given to cardiac, hematopoietic, reproductive, and neurogenic niches, where clinical failures often reflect niche misalignment rather than intrinsic stem-cell deficits. We argue that successful regenerative interventions must treat stem cells and their microenvironment as an inseparable therapeutic unit. Future advances will depend on high-resolution niche mapping, mechanobiologically informed scaffold design, and niche-targeted clinical trials. Re-programming pathological niches may unlock regenerative outcomes that surpass classical cell therapies, marking a new era of microenvironmentally integrated medicine. Full article

Sepsis remains a critical global health challenge characterized by life-threatening organ dysfunction arising from a dysregulated host response to infection. Immunothrombosis refers to the intersection of immune activation and coagulation pathways, particularly relevant in the context of sepsis. A growing body of evidence identifies immunothrombosis, a tightly interwoven process between innate immunity and coagulation. While immunothrombosis serves as a host defense mechanism under physiological conditions, its aberrant activation in sepsis precipitates microvascular thrombosis, organ ischemia, and progression toward disseminated intravascular coagulation (DIC). This review provides a comprehensive overview of the cellular contributors to immunothrombosis, including neutrophils, monocytes, platelets, and endothelial cells, and elucidates the signaling cascades, such as nuclear factor kappa B (NF-κB), mitogen-activated protein kinase (MAPK), and inflammasome activation, that govern their interplay. We further highlight emerging molecular mediators, including extracellular traps, tissue factor expression, and cytokine amplification loops, that collectively promote pathological thromboinflammation. A deeper understanding of these interconnected pathways offers critical insights into the pathogenesis of sepsis and unveils potential targets for timely intervention. Ultimately, this review aims to bridge immunological and hematological perspectives to inform the development of novel therapeutic strategies against sepsis-induced coagulopathy. Full article

It is well established that different fasting strategies offer a range of benefits and may even serve as potential therapeutic approaches for metabolic diseases. The biological effects of intermittent fasting (IF) are multidimensional, involving the induction of metabolic switching from glucose to fatty acid and ketone utilization, thereby enhancing fat metabolism and improving glucose tolerance and insulin sensitivity. In addition, IF modulates the growth hormone/insulin-like growth factor 1 (GH/IGF-1) axis by lowering IGF-1 levels, a change associated with enhanced cellular protection, reduced tumorigenesis, and delayed aging. Moreover, IF modulates key signaling pathways, including mitogen-activated protein kinases, Notch, and nuclear factor kappa B, which collectively contribute to reduced oxidative stress, attenuated inflammation, and hepatoprotection. Although fasting may present certain challenges, it is essential to be adequately informed about its potential benefits and appropriate preparatory strategies before undertaking various fasting protocols. This review summarizes the current knowledge on various IF protocols and periodic short-term fasting (PSTF) lasting more than 24 h and up to 72 h, highlighting the signaling pathways through which these interventions affect metabolic processes. Additionally, it aims to provide a practical guide for the safe preparation for PSTF lasting more than 24 h and up to 72 h. Full article

Stress granules (SG), dynamic cytoplasmic condensates formed via liquid-liquid phase separation (LLPS), serve as a critical hub for cellular stress adaptation and antiviral defense. By halting non-essential translation and sequestering viral RNA, SG restrict viral replication through multiple mechanisms, including PKR-eIF2α signaling, recruitment of antiviral proteins, and spatial isolation of viral components. However, viruses have evolved sophisticated strategies to subvert SG-mediated defenses, including proteolytic cleavage of SG nucleators, sequestration of core proteins into viral replication complexes, and modulation of stress-responsive pathways. This review highlights the dual roles of SG as both antiviral sentinels and targets of viral manipulation, emphasizing their interplay with innate immunity, autophagy, and apoptosis. Furthermore, viruses exploit SG heterogeneity and crosstalk with RNA granules like processing bodies (P-bodies, PB) to evade host defenses, while viral inclusion bodies (IBs) recruit SG components to create proviral microenvironments. Future research directions include elucidating spatiotemporal SG dynamics in vivo, dissecting compositional heterogeneity, and leveraging advanced technologies to unravel context-specific host-pathogen conflicts. This review about viruses and SG formation helps better understand the virus-host interaction and game process to develop new drug targets. Understanding these mechanisms not only advances virology but also informs innovative strategies to address immune escape mechanisms in viral infections. Full article