Search Results (265)

Pediatric asthma is a multifactorial and heterogeneous disease determined by the dynamic interplay of genetic susceptibility, environmental exposures, and immune dysregulation. Recent advances have highlighted the pivotal role of epigenetic mechanisms, in particular, DNA methylation, histone modifications, and non-coding RNAs, in the regulation of inflammatory pathways contributing to asthma phenotypes and endotypes. This review examines the role of respiratory viruses such as respiratory syncytial virus (RSV), rhinovirus (RV), and other bacterial and fungal infections that are mediators of infection-induced epithelial inflammation that drive epithelial homeostatic imbalance and induce persistent epigenetic alterations. These alterations lead to immune dysregulation, remodeling of the airways, and resistance to corticosteroids. A focused analysis of T2-high and T2-low asthma endotypes highlights unique epigenetic landscapes directing cytokines and cellular recruitment and thereby supports phenotype-specific aspects of disease pathogenesis. Additionally, this review also considers the role of miRNAs in the control of post-transcriptional networks that are pivotal in asthma exacerbation and the severity of the disease. We discuss novel and emerging epigenetic therapies, such as DNA methyltransferase inhibitors, histone deacetylase inhibitors, miRNA-based treatments, and immunomodulatory probiotics, that are in preclinical or early clinical development and may support precision medicine in asthma. Collectively, the current findings highlight the translational relevance of including pathogen-related biomarkers and epigenomic data for stratifying pediatric asthma patients and for the personalization of therapeutic regimens. Epigenetic dysregulation has emerged as a novel and potentially transformative approach for mitigating chronic inflammation and long-term morbidity in children with asthma. Full article

Cell confluence and number are critical indicators for assessing cellular growth status, contributing to disease diagnosis and the development of targeted therapies. Accurate and efficient cell segmentation is essential for quantifying these indicators. However, current segmentation methodologies still encounter significant challenges in addressing multi-scale heterogeneity, poorly delineated boundaries under limited annotation, and the inherent trade-off between computational efficiency and segmentation accuracy. We propose an innovative network architecture. First, a preprocessing pipeline combining contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur is introduced to balance noise suppression and local contrast enhancement. Second, a bidirectional feature pyramid network (BiFPN) is incorporated, leveraging cross-scale feature calibration to enhance multi-scale cell recognition. Third, adaptive kernel convolution (AKConv) is developed to capture the heterogeneous spatial distribution of glioma stem cells (GSCs) through dynamic kernel deformation, improving boundary segmentation while reducing model complexity. Finally, a probability density-guided non-maximum suppression (Soft-NMS) algorithm is proposed to alleviate cell under-detection. Experimental results demonstrate that the model achieves 95.7% mAP50 (box) and 95% mAP50 (mask) on the GSCs dataset with an inference speed of 38 frames per second. Moreover, it simultaneously supports dual-modality output for cell confluence assessment and precise counting, providing a reliable automated tool for tumor microenvironment research. Full article

The hypothalamus–pituitary–ovarian (HPO) axis orchestrates reproductive functions through intricate neuroendocrine crosstalk. Here, we integrated single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics (ST) to decode the cellular heterogeneity and intercellular communication networks in the reproductive systems of pregnant Mongolian cattle. We retained a total of 6161 high-quality nuclei from the hypothalamus, 14,715 nuclei from the pituitary, and 26,072 nuclei from the ovary, providing a comprehensive cellular atlas across the HPO axis. In the hypothalamus, neurons exhibited synaptic and neuroendocrine specialization, with glutamatergic subtype Glut4 serving as a TGFβ signaling hub to regulate pituitary feedback, while GABAergic GABA1 dominated PRL signaling, likely adapting maternal behavior. Pituitary stem cells dynamically replenished endocrine populations via TGFβ, and lactotrophs formed a PRL–PRLR paracrine network with stem cells, synergizing mammary development. Ovarian luteal cells exhibited steroidogenic specialization and microenvironmental synergy: endothelial cells coregulated TGFβ-driven angiogenesis and immune tolerance, while luteal–stromal PRL–PRLR interactions amplified progesterone synthesis and nutrient support. Granulosa cells (GCs) displayed spatial-functional stratification, with steroidogenic GCs persisting across pseudotime as luteinization precursors, while atretic GCs underwent apoptosis. Spatial mapping revealed GCs’ annular follicular distribution, mediating oocyte–somatic crosstalk, and luteal–endothelial colocalization supporting vascularization. This study unveils pregnancy-specific HPO axis regulation, emphasizing multi-organ crosstalk through TGFβ/PRL pathways and stem cell-driven plasticity, offering insights into reproductive homeostasis and pathologies. Full article

Glioblastoma (GBM) remains one of the most aggressive and treatment-resistant brain tumors, largely due to its profound intratumoral heterogeneity and immunosuppressive microenvironment. Various classifications of GBM subtypes were created based on transcriptional and methylation profiles. This effort, followed by the development of new technology such as single-nuclei sequencing (snRNAseq) and spatial transcriptomics, led to a better understanding of the glioma cells’ plasticity and their ability to transition between diverse cellular states. GBM cells can mimic neurodevelopmental programs to resemble oligodendrocyte or neural progenitor behavior and hitchhike the local neuronal network to support their growth. The tumor microenvironment, especially under hypoxic conditions, drives the tumor cell clonal selection, which then reshapes the immune cells’ functions. These adaptations contribute to immune evasion by progressively disabling T cell and myeloid cell functions, ultimately establishing a highly immunosuppressive tumor milieu. This complex and metabolically constrained environment poses a major barrier to effective antitumor immunity and limits the success of conventional therapies. Understanding the dynamic interactions between glioma cells and their microenvironment is essential for the development of more effective immunotherapies and rational combination strategies aimed at overcoming resistance and improving patient outcomes. Full article

Proto-oncogenes in the RAS superfamily play dual roles in maintaining cellular homeostasis, such as regulating growth signals and contributing to cancer development through proliferation and deregulation. Activating proto-oncogenes in vitro transforms cells, underscoring their centrality in gene regulation and cellular networks. Despite decades of research, poor outcomes in advanced cancers reveal gaps in understanding Ras-driven mechanisms or therapeutic strategies. This narrative review examines RAS genes and Ras proteins in both housekeeping functions, such as cell growth, apoptosis, and protein trafficking, as well as in tumorigenesis, integrating insights from human (HRAS, KRAS, NRAS), mouse (Hras, Kras, Nras), and Drosophila melanogaster (ras) models. While RAS mutations are tightly linked to human tumors, the interplay between their standard and oncogenic functions remains complex. Even within the same tissue, distinct cancer pathways—such as the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) pathways—can drive varied disease courses, complicating treatment. Advanced-stage cancers add further challenges, including heterogeneity, protective microenvironments, drug resistance, and adaptive progression. This synthesis organizes current knowledge of RAS gene regulation and Ras protein function from genomic alterations and intracellular signaling to membrane dynamics and extracellular interactions, offering a layered perspective on the Ras pathway’s role in both housekeeping and tumorigenic contexts. Full article

Background/Objectives: Subcutaneous tumor models are widely used in colorectal cancer (CRC) research due to their experimental accessibility; however, the spatial organization and regulatory mechanisms of their tumor microenvironment remain poorly understood. Methods: Here, we applied spatial transcriptomics to systematically characterize spatial heterogeneity within MC38 subcutaneous tumors in a syngeneic mouse model. Results: We identified two spatially distinct tumor zones, partitioned by cancer-associated fibroblasts (CAFs), that differ markedly in cellular composition, oncogenic signaling, immune infiltration, and metabolic states. One zone exhibited features of TGF-β-driven extracellular matrix remodeling, immune exclusion, and hyperproliferative metabolism, while the other was enriched for immunosuppressive macrophages, metabolic reprogramming via PPAR and AMPK pathways, and high-risk cell populations. Spatially resolved cell–cell communication networks further revealed zone-specific ligand–receptor interactions—such as ANGPTL4–SDC2 and PROS1–AXL—that underpin stromal remodeling and immune evasion and are associated with patient prognosis. Conclusions: Collectively, our study uncovers how region-specific cellular ecosystems and intercellular crosstalk establish prognostically divergent niches within subcutaneous CRC tumors, offering insights into spatially guided therapeutic strategies. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring seamless mobility and efficient resource allocation. Traditional handover and load balancing techniques, primarily designed for 4G systems, are no longer sufficient to address the complexity of heterogeneous network environments that incorporate millimeter-wave communication, Internet of Things (IoT) devices, and unmanned aerial vehicles (UAVs). The review focuses on how recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are being applied to improve predictive handover decisions and enable real-time, adaptive load distribution. AI-driven solutions can significantly reduce handover failures, latency, and network congestion, while improving overall user experience and quality of service (QoS). This paper surveys state-of-the-art research on these techniques, categorizing them according to their application domains and evaluating their performance benefits and limitations. Furthermore, the paper discusses the integration of intelligent handover and load balancing methods in smart city scenarios, where ultra-dense networks must support diverse services with high reliability and low latency. Key research gaps are also identified, including the need for standardized datasets, energy-efficient AI models, and context-aware mobility strategies. Overall, this review aims to guide future research and development in designing robust, AI-assisted mobility and resource management frameworks for next-generation wireless systems. 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

Extracellular signal-regulated kinase 1/2 (ERK1/2) is involved in the regulation of the key cellular processes that are essential for the proper functioning of the cell under physiological conditions. Notably, the hyperactivation of ERK1/2 is implicated in oncogenesis and metastatic dissemination across various tumor types, making it an attractive candidate for targeted therapy (TT) through functional inhibition. In intrahepatic cholangiocarcinoma (iCCA), sustained ERK1/2 activation represents one of the major events within the complex signaling network that drives tumor development and progression. In this review, we dissect the biological role of ERK1/2 signaling in iCCA and highlight recent preclinical advances involving selective small-molecule ERK1/2 inhibitors. In vitro and in vivo studies have demonstrated how these inhibitors present effective anti-tumorigenic properties. In particular, PD901 and U0126 effectively reduce iCCA cell proliferation and invasion. Furthermore, Ulixertinib has shown a favorable therapeutic index and encouraging activity in clinical trials involving advanced solid tumors, including iCCA, paving the way for a new therapeutic approach targeting ERK1/2. Nevertheless, the heterogeneous and dynamic molecular landscape of iCCA, often accompanied by drug resistance, presents significant therapeutic challenges. We underscore how targeting the ERK1/2 pathway could represent a cornerstone within a multifaceted therapeutic strategy, fostering the development of personalized treatment approaches and improving clinical outcomes in iCCA patients. Full article

From a space-time perspective, this paper constructs a CA-SHIRS model to study the psychosocial resonance diffusion of food safety risk, using complex network and cellular automata theory. The CA-SHIRS model is a framework that combines cellular automata with SHIRS (Susceptible–Hidden–Infected–Recovered–Susceptible) epidemic modeling. This methodological integration can effectively reflect local interactions and spatial distribution among consumers. Furthermore, this paper analyzes the diffusion mechanism and spatial–temporal evolution of the psychosocial resonance of food safety risk, considering the interaction between consumer heterogeneity and media communication strategies. The primary conclusions are outlined as follows: (1) An increase in infection probability, conversion probability, and immune failure probability causes the psychosocial resonance of food safety risk to spread rapidly across different regions and populations. In contrast, an increase in immune probability helps control the psychosocial resonance of food safety risk. (2) The diffusion threshold of the psychosocial resonance of food safety risk is negatively related to the consumer risk perception level, consumer risk attention, media freedom, and media report authenticity. However, it is positively correlated with consumer sentiment, market noise, and media report tendency. (3) The consumer risk perception level, consumer risk attention, media freedom, and media report authenticity can effectively inhibit the spatial–temporal diffusion of the psychosocial resonance of food safety risk. On the other hand, increases in market noise, consumer sentiment, and media report tendency accelerate the spread of the psychosocial resonance of food safety risk across different regions. Full article

Tumor diseases are characterized by high interindividual and intratumoral heterogeneity (ITH). The development and progression of neoplasms outline complex networks of extracellular and cellular signals that have yet to be fully elucidated. This narrative review provides a comprehensive overview of the literature related to the cellular and molecular mechanisms underlying the heterogeneity of the tumor mass. Furthermore, it examines the possible role of the tumor microenvironment in the development and support of the neoplasm, in order to highlight its potential in the construction of a diagnostic–therapeutic approach to precision medicine. Many authors underline the importance of the tumor microenvironment (TME) as it actively takes part in the growth of the neoplastic mass and in the formation of metastases and in the acquisition of resistance to anticancer drugs. In specific body districts, the ideal conditions occur for the TME establishment, particularly the inflammatory state, the recruitment of cell types, the release of specific cytokines and growth factors, hypoxic conditions. These components actively intervene by enabling tumor progression and construction of physical barriers shaped by the extracellular matrix that contribute to forming peripheral tolerance by intervention of myeloid precursors and the polarization of M2 macrophages. In recent years, ITH and the TME have assumed an important position in cancer research and pharmacology as they enable understanding the dense network of communication existing between the neoplasm and the surrounding environment, and to monitor and deepen the effects of drugs with a view to develop increasingly precise and effective therapies. In the last decade, knowledge of TME has been exploited to produce targeted molecular agents (inhibitory small molecules, monoclonal antibodies, gene therapy). Nonetheless, the bibliography shows the need to study ITH through new prognostic and predictive biomarkers (e.g., ctDNA and CTCs) and to increase its basic biology knowledge. Precision medicine is a new opportunity in the treatment of oncological diseases that is transforming the development of new drug approaches and their clinical use. Biology and biotechnologies are providing the bases for this revolution. Full article

Systemic cancer progression culminating in metastatic disease is implicitly dependent on tumour cell interactions with the vascular system. Indeed, different facets of the micro- and macro-vasculature can be regarded as rate-limiting ‘vascular checkpoints’ in the process of cancer dissemination. The underlying complex communication networks drive tumour neovascularization, angiogenesis, immunoregulation, activation of the coagulation system, angiocrine interactions, and non-angiogenic vascular responses across multiple cancer types. Yet, each cancer may represent a unique vascular interaction scenario raising a prospect of targeted modulation of blood and lymphatic vessels for therapeutic purposes, beyond the traditional notion of tumour anti-angiogenesis. While the emphasis of studies aiming to understand this circuitry has traditionally been on soluble, or ‘mono-molecular’ mediators, the rise of the particulate secretome encompassing heterogeneous subpopulations of extracellular vesicles (EVs; including exosomes) and particles (EPs) brings another dimension into the tumour–vascular communication web during the process of metastasis. EVs and EPs are nanosized cellular fragments, the unique nature of which lies in their ability to encapsulate, protect and deliver to target cells a range of bioactive molecular entities (proteins, RNA, DNA) assembled in ways that enable them to exert a wide spectrum of biological activities. EVs and EPs penetrate through biological barriers and are capable of intracellular uptake. Their emerging vascular functions in metastatic or infiltrative cancers are exemplified by their roles in pre-metastatic niche formation, thrombosis, vasectasia or angiocrine regulation of cancer stem cells. Here, we survey some of the related evidence supporting the biological, diagnostic and interventional significance of EVs/EPs (EVPs) in disseminated neoplastic disease. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Single-cell RNA sequencing (scRNA-seq) has revolutionized neuroscience by enabling the analysis of cellular heterogeneity and dynamic molecular processes at the single-cell resolution. In spinal cord research, scRNA-seq provides critical insights into cell type diversity, developmental trajectories, and pathological mechanisms. This review summarizes recent progress in the application of scRNA-seq to spinal cord development, injury, and neurodegenerative diseases and discusses the current challenges and future directions. Relevant studies focusing on the key applications of scRNA-seq, including advances in spatial transcriptomics and multi-omics integration, were retrieved from PubMed and the Web of Science. scRNA-seq has enabled the identification of distinct spinal cord cell populations and revealed the gene regulatory networks driving development. Injury models have revealed the temporal dynamics of immune and glial responses, alongside potential regenerative processes. In neurodegenerative conditions, scRNA-seq highlights cell-specific vulnerabilities and molecular changes. The integration of spatial transcriptomics and computational tools, such as machine learning, has further improved the resolution of spinal cord biology. However, challenges remain in terms of data complexity, sample acquisition, and clinical translation. Single-cell transcriptomics is a powerful approach for understanding spinal cord biology. Its integration with emerging technologies will advance both basic research and clinical applications, supporting personalized and regenerative therapy. Addressing these technical and analytical barriers is essential to fully realize the potential of scRNA-seq in spinal cord science. Full article