Search Results (651)

Background: The candidate therapeutic peptide TnP demonstrates broad, system-level regulatory capacity, revealed through integrated network analysis from transcriptomic data in zebrafish. Our study primarily identifies TnP as a multifaceted modulator of drug metabolism, wound healing, proteolytic activity, and pigmentation pathways. Results: Transcriptomic profiling of TnP-treated larvae following tail fin amputation revealed 558 differentially expressed genes (DEGs), categorized into four functional networks: (1) drug-metabolizing enzymes (cyp3a65, cyp1a) and transporters (SLC/ABC families), where TnP alters xenobiotic processing through Phase I/II modulation; (2) cellular trafficking and immune regulation, with upregulated myosin genes (myhb/mylz3) enhancing wound repair and tlr5-cdc42 signaling fine-tuning inflammation; (3) proteolytic cascades (c6ast4, prss1) coupled to autophagy (ulk1a, atg2a) and metabolic rewiring (g6pca.1-tg axis); and (4) melanogenesis-circadian networks (pmela/dct-fbxl3l) linked to ubiquitin-mediated protein turnover. Key findings highlight TnP’s unique coordination of rapid (protease activation) and sustained (metabolic adaptation) responses, enabled by short network path lengths (1.6–2.1 edges). Hub genes, such as nr1i2 (pxr), ppara, and bcl6aa/b, mediate crosstalk between these systems, while potential risks—including muscle hypercontractility (myhb overexpression) or cardiovascular effects (ace2-ppp3ccb)—underscore the need for targeted delivery. The zebrafish model validated TnP-conserved mechanisms with human relevance, particularly in drug metabolism and tissue repair. TnP’s ability to synchronize extracellular matrix remodeling, immune resolution, and metabolic homeostasis supports its development for the treatment of fibrosis, metabolic disorders, and inflammatory conditions. Conclusions: Future work should focus on optimizing tissue-specific delivery and assessing genetic variability to advance clinical translation. This system-level analysis positions TnP as a model example for next-generation multi-pathway therapeutics. Full article

5G addresses user privacy concerns in cellular networking by encrypting a subscriber identifier with elliptic-curve-based encryption and then transmitting it as ciphertext known as a Subscriber Concealed Identifier (SUCI). However, an adversary equipped with a quantum computer can break a discrete-logarithm-based elliptic curve algorithm. Consequently, the user privacy in 5G is at stake against quantum attacks. In this paper, we study the incorporation of the post-quantum ciphers in the SUCI calculation both at the user equipment and at the core network, which involves the shared-key exchange and then using the resulting key for the ID encryption. We experiment on different hardware platforms to analyze the PQC key exchange and encryption using NIST-standardized CRYSTALS-Kyber (which is now called an ML-KEM after the standardization selection by NIST). Our analyses focus on the performances and compare the Kyber-based key exchange and encryption with the current (pre-quantum) elliptic curve Diffie–Hellman (ECDH). The performance analyses are critical because mobile networking involves resource-limited and battery-operating mobile devices. We measure and analyze not only the time and CPU-processing performances but also the energy and power performances. Our analyses show that Kyber-512 is the most efficient and even has better performance (i.e., faster computations and lower energy consumption) than ECDH. Full article

Quercus acutissima seeds exhibit high desiccation sensitivity, posing significant challenges for long-term preservation. This study investigates the physiological and metabolic responses of soluble osmoprotectants—particularly soluble proteins and proline—during the desiccation process. Seeds were sampled at three critical moisture content levels: 38.8%, 26.8%, and 14.8%, corresponding to approximately 99%, 52%, and 0% germination, respectively. We measured germination ability, soluble protein content, and proline accumulation, and we performed untargeted metabolomic profiling using LC-MS. Soluble protein levels increased early but declined later during desiccation, while proline levels continuously increased for sustained osmotic adjustment. Metabolomics analysis identified a total of 2802 metabolites, with phenylpropanoids and polyketides (31.12%) and lipids and lipid-like molecules (29.05%) being the most abundant. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that differentially expressed metabolites were mainly enriched in key pathways such as amino acid metabolism, energy metabolism, and nitrogen metabolism. Notably, most amino acids decreased in content, except for proline, which showed an increasing trend. Tricarboxylic acid cycle intermediates, especially citric acid and isocitric acid, showed significantly decreased levels, indicating energy metabolism imbalance due to uncoordinated consumption without effective replenishment. The reductions in key amino acids such as glutamic acid and aspartic acid further reflected metabolic network disruption. In summary, Q. acutissima seeds fail to establish an effective desiccation tolerance mechanism. The loss of soluble protein-based protection, limited capacity for proline-mediated osmotic regulation, and widespread metabolic disruption collectively lead to irreversible cellular damage. These findings highlight the inherent metabolic vulnerabilities of recalcitrant seeds and suggest potential preservation strategies, such as supplementing critical metabolites (e.g., TCA intermediates) during storage to delay metabolic collapse and mitigate desiccation-induced damage. Full article

Cell-free massive multiple-input multiple-output (CF M-MIMO) is one of the most promising technologies for future wireless communication such as 5G and beyond fifth-generation (B5G) networks. It is a type of network technology that uses a massive number of distributed antennas to serve a large number of users at the same time. It has the ability to provide high spectral efficiency (SE) as well as improved coverage and interference management, compared to traditional cellular networks. However, estimating the channel with high-performance, low-cost computational methods is still a problem. Different algorithms have been developed to address these challenges in channel estimation. One of the high-performance channel estimators is a phase-aware minimum mean square error (MMSE) estimator. This channel estimator has high computational complexity. To address the shortcomings of the existing estimator, this paper proposed an efficient phase-aware element-wise minimum mean square error (PA-EW-MMSE) channel estimator with QR decomposition and a precoding matrix at the user side. The closed form uplink (UL) SE with the phase MMSE and proposed estimators are evaluated using MMSE combining. The energy efficiency and area throughput are also calculated from the SE. The simulation results show that the proposed estimator achieved the best SE, EE, and area throughput performance with a substantial reduction in the complexity of the computation. Full article

The Industrial Internet of Things (IIoT) has revolutionized industry through interconnected devices and intelligent applications. Leveraging the advancements in sixth-generation cellular networks (6G), the 6G-aided IIoT has demonstrated a superior performance across applications requiring low latency and high reliability, with image recognition being among the most pivotal. However, the existing algorithms often neglect the explainability of image recognition processes and fail to address the collaborative potential between edge computing servers. This paper proposes a novel method, IRCE (Intelligent Recognition with Collaborative Edges), designed to enhance the explainability and transferability in 6G-aided IIoT image recognition. By incorporating an explainable layer into the feature extraction network, IRCE provides visual prototypes that elucidate decision-making processes, fostering greater transparency and trust in the system. Furthermore, the integration of the local maximum mean discrepancy (LMMD) loss facilitates seamless transfer learning across geographically distributed edge servers, enabling effective domain adaptation and collaborative intelligence. IRCE leverages edge intelligence to optimize real-time performance while reducing computational costs and enhancing scalability. Extensive simulations demonstrate the superior accuracy, explainability, and adaptability of IRCE compared to those of the traditional methods. Moreover, its ability to operate efficiently in diverse environments highlights its potential for critical industrial applications such as smart manufacturing, remote diagnostics, and intelligent transportation systems. The proposed approach represents a significant step forward in achieving scalable, explainable, and transferable AI solutions for IIoT ecosystems. Full article

According to our research, the flowers from Citrus grandis ‘Tomentosa’ contain rich biologically active essential oil components, but the chemical components and relative pharmacological properties have not been systematically studied. Therefore, the study aimed to identify the essential oil components by GC-MS/MS and explore the pharmacological activity and mechanism of these essential oil components by a network pharmacology approach. Finally, GC-MS/MS analysis identified 43 essential oil components, which corresponded to 739 potential targets. GO analysis results showed that 12, 18, and 12 entries were related to biological processes, cellular components, and molecular functions, respectively. A total of 120 pathways were obtained based on KEGG analysis, of which the most important was the adenylate cyclase-inhibiting G protein-coupled acetylcholine receptor signaling pathway. The “active component–target–disease” network further demonstrated these essential oil components’ potential efficacy against pain, tumors, neuropsychiatric diseases, eye diseases, and respiratory diseases, which were highly related to PPARA, GABRA1, PTGS2, and SLC6A2. Experimental validation confirmed that β-caryophyllene, a major constituent, dose-dependently inhibited the proliferation of HT29 and MCF-7 cells (0–320 μM). This study provides a reliable basis for elucidating the pharmacological activity of the essential oil components and related mechanisms, which is beneficial to the comprehensive utilization and development of Citrus grandis ‘Tomentosa’. Full article

Epigenetic modifications act as crucial regulators of gene activity and are influenced by both internal and external environmental factors, with diet being the most impactful external factor. On the other hand, cellular metabolism encompasses a complex network of biochemical reactions essential for maintaining cellular function, and it impacts every cellular process. Many metabolic cofactors are critical for the activity of chromatin-modifying enzymes, influencing methylation and the global acetylation status of the epigenome. For instance, dietary nutrients, particularly those involved in one-carbon metabolism (e.g., folate, vitamins B12 and B6, riboflavin, methionine, choline, and betaine), take part in the generation of S-adenosylmethionine (SAM), which represents the main methyl donor for DNA and histone methylation; α-ketoglutarate and ascorbic acid (vitamin C) act, respectively, as a co-substrate and cofactor for Ten-eleven Translocation (TET), which is responsible for DNA demethylation; and metabolites such as Acetyl-CoA directly impact histone acetylation, linking metabolism of the TCA cycle to epigenetic regulation. Further, bioactive compounds, such as polyphenols, modulate epigenetic patterns by affecting methylation processes or targeting epigenetic enzymes. Since diet and nutrition play a critical role in shaping epigenome functions and supporting human health, this review offers a comprehensive update on recent advancements in metabolism, epigenetics, and nutrition, providing insights into how nutrients contribute to metabolic balance, epigenome integrity maintenance and, consequently, disease prevention. Full article

Electrically conductive hydrogels are gaining attention owing to their applications in biosensing, cellular interfaces, and tissue engineering. However, conventional hydrogels often lack adequate electrical conductivities. Here, we present two novel conductive alginate-based hydrogels designed for extrusion-based 3D bioprinting: (i) covalently synthesized alginate–polypyrrole (alginate–PPy) via EDC/NHS-mediated conjugation with 3-aminopropyl pyrrole, and (ii) nanoparticle-reinforced alginate blended with polypyrrole nanoparticles (alginate@PPy-NP). Both systems exhibited shear-thinning behavior, tunable viscoelasticity, and excellent printability. Alginate@PPy-NP demonstrated superior compressive strength and shape fidelity, whereas alginate–PPy showed enhanced elastic moduli (G′/G″), reflecting a more uniform gel network. Electrical conductivity increased with increasing pyrrole content in both formulations. Optimization of the composition and printing conditions enabled the fabrication of fibroblast-laden constructs with high structural integrity. This work highlights the potential of alginate–polypyrrole hydrogels as customizable, conductive bioinks for 3D bioprinting in regenerative medicine. 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

Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. The need for enhanced radio resource allocation schemes, improved user mobility and increased throughput, driven by a rising demand for data, has necessitated the development of diverse algorithms that optimize output values based on varied input parameters. In this context, we identify the main topics related to cellular networks and machine learning algorithms in order to pinpoint areas where the optimization of parameters is crucial. Furthermore, the wide range of available algorithms often leads to confusion and disorder during classification processes. It is crucial to note that next-generation networks are expected to require reduced latency times, especially for sensitive applications such as Industry 4.0. Research Question: An analysis of the existing literature on mobile network load balancing methods was conducted to identify systems that operate using semi-automatic, automatic and hybrid algorithms. Our research question is as follows: What are the automatic, semi-automatic and hybrid load balancing algorithms that can be applied to next-generation mobile networks? Contribution: This paper aims to present a comprehensive analysis and classification of the algorithms used in this area of study; in order to identify the most suitable for load balancing optimization in next-generation mobile networks, we have organized the classification into three categories, automatic, semi-automatic and hybrid, which will allow for a clear and concise idea of both theoretical and field studies that relate these three types of algorithms with next-generation networks. Figures and tables illustrate the number of algorithms classified by type. In addition, the most important articles related to this topic from five different scientific databases are summarized. Methodology: For this research, we employed the PRISMA method to conduct a systematic literature review of the aforementioned study areas. Findings: The results show that, despite the scarce literature on the subject, the use of load balancing algorithms significantly influences the deployment and performance of next-generation mobile networks. This study highlights the critical role that algorithm selection should play in 5G network optimization, in particular to address latency reduction, dynamic resource allocation and scalability in dense user environments, key challenges for applications such as industrial automation and real-time communications. Our classification framework provides a basis for operators to evaluate algorithmic trade-offs in scenarios such as network fragmentation or edge computing. To fill existing gaps, we propose further research on AI-driven hybrid models that integrate real-time data analytics with predictive algorithms, enabling proactive load management in ultra-reliable 5G/6G architectures. Given this background, it is crucial to conduct further research on the effects of technologies used for load balancing optimization. This line of research is worthy of consideration. Full article

Odontogenesis, the development of teeth, is a complex, multistage process that unfolds from early embryogenesis through tooth eruption and maturation. It serves as a classical model of organogenesis due to the intricate reciprocal interactions between cranial neural crest-derived mesenchyme and oral epithelium. This narrative review synthesizes current scientific knowledge on human tooth development, tracing the journey from the embryological origins in the first branchial arch to the formation of a fully functional tooth and its supporting structures. Key morphogenetic stages—bud, cap, bell, apposition, and root formation—are described in detail, highlighting the cellular events and histological features characterizing each stage. We discuss the molecular and cellular regulatory networks that orchestrate odontogenesis, including the conserved signaling pathways (Wnt, BMP, FGF, SHH, EDA) and transcription factors (e.g., PAX9, MSX1/2, PITX2) that drive tissue patterning and cell differentiation. The coordinated development of supporting periodontal tissues (cementum, periodontal ligament, alveolar bone, gingiva) is also examined as an integral part of tooth organogenesis. Finally, developmental anomalies (such as variations in tooth number, size, and form) and the fate of residual embryonic epithelial cells are reviewed to underscore the clinical significance of developmental processes. Understanding the normal course of odontogenesis provides crucial insight into congenital dental disorders and lays a foundation for advances in regenerative dental medicine. 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

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

The advancement of modern lifestyles has precipitated excessive consumption of energy-dense foods, driving the escalating global burden of lipid metabolism dysregulation-related pathologies—including obesity, type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and cardiovascular disorders—which collectively pose a formidable challenge to global public health systems. The almond hull, as a by-product of almond processing, is rich in polyphenolic compounds with demonstrated antioxidant, anti-inflammatory, and lipid-lowering potential, though its precise hypo-lipidemic mechanisms remain elusive. In this study, polyphenols were extracted from almond hulls using 50% ethanol with ultrasound-assisted extraction, followed by preliminary purification via solvent partitioning. The ethyl acetate fraction was analyzed by liquid chromatography–mass spectrometry (LC-MS). Network pharmacology and molecular docking were employed to investigate the interactions between key bioactive constituents (e.g., quercetin, baicalein, and kaempferol) and targets in lipid metabolism-related pathways. Molecular dynamics (MD) simulations further evaluated the stability of the lowest-energy complexes. Results revealed that the ethyl acetate fraction exhibited potent pancreatic lipase inhibitory activity (IC50 = 204.2 µg/mL). At 0.1 mg/mL after 24 h treatment, it significantly reduced free fatty acids (FFAs)-induced intracellular triglyceride accumulation (p < 0.01) and enhanced cellular antioxidant capacity. Network pharmacology and in vitro studies suggest almond hull extract modulates PI3K-AKT signaling and improves insulin resistance, demonstrating lipid-lowering effects. These findings support its potential in functional foods and pharmaceuticals, though further in vivo validation and mechanistic investigations are required. Full article

In this study, we demonstrate an application for 5G networks in mobile and remote GPR scanning situations to detect buried objects by experts while the operator is performing the scans. Using a GSSI SIR-30 system in conjunction with the RealSense camera for visual mapping of the surveyed area, subsurface GPR scans were created and transmitted for remote processing. Using mobile networks, the raw B-scan files were transmitted at a sufficient rate, a maximum of 0.034 ms mean latency, to enable near real-time edge processing. The performance of 5G networks in handling the data transmission for the GPR scans and edge computing was compared to the performance of 4G networks. In addition, long-range low-power devices, namely Wi-Fi HaLow and Wi-Fi hotspots, were compared as local alternatives to cellular networks. Augmented reality headset representation of the F-scans is proposed as a method of assisting the operator in using the edge-processed scans. These promising results bode well for the potential of remote processing of GPR data in augmented reality applications. Full article