Search Results (456)

This study systematically analyzed the multidimensional effects of Lactobacillus fermentation on Pueraria lobata (PL) and investigated the potential mechanisms underlying its hypolipidemic activity. Results indicated that fermentation significantly increased the total acid content from 1.02 to 3.48 g·L⁻¹, representing a 2.41-fold increase. Although slight reductions were observed in total flavonoids (8.67%) and total phenolics (6.72%), the majority of bioactive components were well preserved. Other antioxidant capacities were retained at >74.71% of baseline, except hydroxyl radical scavenging. Flavor profiling showed increased sourness and astringency, accompanied by reduced bitterness, with volatile compounds such as β-pinene and trans-2-hexenyl butyrate contributing to a distinct aromatic profile. Untargeted metabolomics analysis revealed that fermentation specifically enhanced the abundance of low-concentration isoflavone aglycones, including daidzein and genistein, suggesting a compositional shift that may improve hypolipidemic efficacy. Integrated network pharmacology and computational modeling predicted that eight key components, including genistein, could stably bind to ten core targets (e.g., AKT1 and MMP9) primarily through hydrogen bonding and hydrophobic interactions, potentially regulating lipid metabolism via the PI3K-AKT, PPAR, and estrogen signaling pathways. This study reveals the role of Lactobacillus fermentation in promoting the conversion of isoflavone glycosides to aglycones in PL and constructs a multi-dimensional “components-targets-pathways-disease” network, providing both experimental evidence and a theoretical foundation for further research on the lipid-lowering mechanisms of fermented PL and the development of related functional products. Full article

Background/Objectives: Hypertension is a global health challenge. Zhengan Xifeng Decoction (ZXD), a classical traditional Chinese medicine, has shown clinical efficacy against hypertension. This study aimed to identify the bioactive constituents of ZXD and elucidate its antihypertensive mechanisms by integrating plasma UPLC–MS (ultra-performance liquid chromatography–mass spectrometry) analysis, network pharmacology, and molecular dynamics (MD) simulations. Methods: ZXD constituents and plasma-absorbed compounds were characterized by UPLC–MS. Putative targets (TCMSP, SwissTargetPrediction) were cross-referenced with hypertension targets (GeneCards, OMIM) and analyzed in a STRING protein–protein interaction network (Cytoscape) to define hub targets, followed by GO/KEGG enrichment. Selected protein–ligand complexes underwent docking, Prime MM-GBSA calculation, and MD validation. Results: A total of 72 absorbed components were identified, including 14 prototype compounds and 58 metabolites. Network pharmacology identified ten key bioactive compounds (e.g., liquiritigenin, isoliquiritigenin, and caffeic acid), 149 hypertension-related targets, and ten core targets such as SRC, PIK3CA, PIK3CB, EGFR, and IGF1R. Functional enrichment implicated cardiovascular, metabolic, and stress-response pathways in the antihypertensive effects of ZXD. Molecular docking demonstrated strong interactions between key compounds, including liquiritigenin, caffeic acid, and isoliquiritigenin, and core targets, supported by the MM-GBSA binding free energy estimation. Subsequent MD simulations confirmed the docking poses and validated the stability of the protein–ligand complexes over time. Conclusions: These findings provide mechanistic insights into the multi-component, multi-target, and multi-pathway therapeutic effects of ZXD, offering a scientific basis for its clinical use and potential guidance for future drug development in hypertension management. Full article

Dendrobium officinale (Orchidaceae) is a commonly used medicinal and edible herb. Although its anti-ageing properties have been demonstrated, the underlying mechanisms remain unclear. We employed network pharmacology and molecular biology techniques to systematically explore its anti-ageing mechanisms. An ageing model was established using D-galactose-induced Kunming mice. D. officinale significantly ameliorated ageing-related symptoms, including behavioural impairment and organ index reduction. It enhanced antioxidant capacity by increasing serum T-AOC levels and restoring renal activities of key antioxidant enzymes (SOD, GSH-Px, CAT) while reducing MDA; it suppressed serum TNF-α levels, indicating anti-inflammatory effects. Histopathological examination revealed that D. officinale alleviated D-galactose-induced renal damage, including tubular cell swelling and glomerular capsule widening. Network pharmacology identified 8 core active compounds (e.g., 5,7-dihydroxyflavone, naringenin) and 10 key targets (e.g., HSP90AA1, EGFR, MAPK3). KEGG analysis highlighted pathways including neuroactive ligand–receptor interaction, cAMP signalling, and calcium signalling. Molecular docking confirmed strong binding affinities between core compounds and key targets. Western blotting and immunohistochemistry validated that D. officinale upregulated EGFR, HSP90AA1, ERK, and GAPDH expression in renal tissues. In summary, D. officinale exerts anti-ageing effects by modulating oxidative stress, suppressing inflammation, and regulating multiple signalling pathways. Our findings provide a scientific rationale for its application in anti-ageing interventions. Full article

This study investigated the mechanism of hydroxysafflor yellow A (HSYA) on senescent human umbilical cord mesenchymal stem cells (hUC-MSCs) through transcriptome sequencing. HSYA treatment identified 2377 differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that these DEGs were primarily enriched in cell adhesion regulation and the extracellular matrix (ECM)–receptor interaction pathway. Gene Set Enrichment Analysis (GSEA) and protein–protein interaction (PPI) network analysis corroborated the central role of ECM–receptor interaction signaling, and Key Driver Analysis (KDA) revealed 10 core regulatory genes (e.g., ID1, SMAD3, TGFB3). SA-β-gal staining showed that HSYA significantly reduced senescence-associated β-galactosidase activity. Flow cytometry showed no significant changes in cell cycle distribution. Western blot analysis indicated that HSYA treatment reduced the protein expression level of p16 without significantly altering p53 levels. Furthermore, HSYA significantly attenuated intracellular reactive oxygen species (ROS) accumulation. qPCR validation demonstrated that HSYA significantly upregulated ID1, GDF5, SMAD3, and TGFB3 while downregulating BMP4, TGFB2, and CCN2. These findings indicate that HSYA modulates genes associated with the ECM–receptor interaction pathway, potentially contributing to improved ECM homeostasis in senescent hUC-MSCs. Full article

This paper presents TwinP2G, a software application for optimal planning of investments in power-to-gas (PtG) systems. TwinP2G provides simulation and optimization services for the techno-economic analysis of user-customized energy networks. The core of TwinP2G is based on power flow simulation; however it supports energy sector coupling, including electricity, green hydrogen, natural gas, and synthetic methane. The framework provides a user-friendly user interface (UI) suitable for various user roles, including data scientists and energy experts, using visualizations and metrics on the assessed investments. An identity and access management mechanism also serves the security and authorization needs of the framework. Finally, TwinP2G revolutionizes the concept of data availability and data sharing by granting its users access to distributed energy datasets available in the EnerShare Data Space. These data are available to TwinP2G users for conducting their experiments and extracting useful insights on optimal PtG investments for the energy grid. Full article

Background/Objectives: Allergic diseases (e.g., asthma, chronic urticaria) are increasing globally, but current anti-allergic drugs exhibit limitations in efficacy and safety. Traditional Chinese Medicine (TCM) emphasizes constitutional regulation for allergic diseases management. The allergic constitution prescription (ACP), a TCM formulation, lacks clear mechanistic insights. Methods: This study employs a novel network pharmacology approach integrating ultra-high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS) to identify ACP’s chemical components and compare its mechanisms with anti-allergic drugs. Chemical components of ACP were analyzed via UHPLC-Q-TOF-MS/MS, and allergic disease-related targets were collected from public databases. Anti-allergic drug targets were intersected with ACP-disease targets to identify unique and common pathways. Molecular docking and dynamics simulations assessed binding affinity between key compounds and core targets. Results: We identified 126 compounds in ACP. Compared to anti-allergic drugs, ACP targeted 10 unique and five common key pathways (e.g., MAPK signaling), 10 unique and nine common core targets (e.g., Tumor Necrosis Factor (TNF), IL-6), and 14 unique and 15 common key compounds. Simulations confirmed high binding affinity of ACP compounds to core targets. Conclusions: These findings highlight ACP’s potential multi-target mechanisms for allergic diseases treatment, identifying unique and shared pathways, targets, and compounds compared to anti-allergic drugs, offering new insights for further mechanistic studies. However, it is crucial to note that these mechanistic predictions and compound-target interactions are primarily derived from computational analyses, and experimental validation (e.g., in vitro or in vivo assays) is essential to confirm these computational findings. Full article

Objective: To systematically evaluate the efficacy and explore the mechanisms of Yiqi and blood-activating traditional Chinese medicine (TCM) combined with chemotherapy in treating non-small cell lung cancer (NSCLC). Methods: Randomized controlled trials (RCTs) on chemotherapy combined with Yiqi and blood-activating TCM for NSCLC were retrieved from CNKI, VIP, Wanfang, and PubMed databases. The search period covered from the inception of each database to January 2025. Study quality was assessed via the Cochrane Risk of Bias tool. Meta-analysis evaluated clinical efficacy, data mining identified core herbs and active compounds, and network pharmacology analyzed targets and pathways. Immune infiltration analysis explored immunomodulatory mechanisms. Results: A total of 57 RCTs with 4865 patients were analyzed. Combined therapy significantly improved short-term efficacy, relieved symptoms (e.g., cough, fatigue), reduced adverse effects, and enhanced quality of life versus chemotherapy alone. Data mining identified Astragalus membranaceus, Atractylodes macrocephala, and Poria cocos as core herbs. Network pharmacology revealed 40 active compounds (including quercetin and kaempferol) that targeted 137 key proteins (e.g., TP53, AKT1), with these targets mainly involved in immune regulation. Immune infiltration analysis showed increased CD4+ T cells and balanced T cell subsets, indicating enhanced antitumor immunity. Conclusions: Yiqi and blood-activating TCM combined with chemotherapy improves NSCLC outcomes by modulating the tumor immune microenvironment. This supports the integration of TCM and highlights immune regulation as a key mechanism. Full article

Facing global pressures such as population growth, shrinking arable land, and climate change, intelligent agriculture has emerged as a critical pathway toward sustainable and efficient agricultural production. Control algorithms serve as the core enabler of this transition, finding applications in crop production, pest management, agricultural machinery, and resource optimization. This review systematically examines the performance and applications of both traditional (e.g., PID, fuzzy logic) and advanced control algorithms (e.g., neural networks, model predictive control, adaptive control, active disturbance rejection control, and sliding mode control) in agriculture. While traditional methods are valued for simplicity and robustness, advanced algorithms better handle nonlinearity, uncertainty, and multi-objective optimization, enhancing both precision and resource efficiency. However, challenges such as environmental heterogeneity, hardware limitations, data scarcity, real-time requirements, and multi-objective conflicts hinder widespread adoption. This review contributes a structured, critical synthesis of these algorithms, highlighting their comparative strengths and limitations, and identifies key research gaps that distinguish it from prior reviews. Future directions include lightweight algorithms, digital twins, multi-sensor integration, and edge computing, which together promise to enhance the scalability and sustainability of intelligent agricultural systems. Full article

Conventional fracturing fluids, while essential for large-volume stimulation of unconventional reservoirs, often induce significant reservoir damage through water retention and capillary trapping. To address this problem, this study developed a novel superhydrophobic nano-viscous drag reducer (SN-DR), synthesized through a multi-monomer copolymerization and silane modification strategy, which enhances structural stability and minimizes reservoir damage. The structure and thermal stability of SN-DR were characterized by FT-IR, ¹H NMR, and TGA. Rheological evaluations demonstrated that the gel fracturing fluid exhibits a highly stable three-dimensional network structure, with a G′ maintained at approximately 3000 Pa and excellent shear recovery under cyclic stress. Performance tests showed that a 0.15% SN-DR achieved a drag reduction rate of 78.1% at 40 L/min, reduced oil–water interfacial tension to 0.91 mN·m⁻¹, and yielded a water contact angle of 152.07°, confirming strong hydrophobicity. Core flooding tests revealed a flowback rate exceeding 50% and an average permeability recovery of 86%. SEM and EDS indicated that the gel formed nanoscale, tightly packed papillary structures on core surfaces, enhancing roughness and reducing water intrusion. The study demonstrates that gel fracturing fluid enhances structural stability, alters wettability, and mitigates water-blocking damage. These findings offer a new strategy for designing high-performance fracturing fluids with integrated drag reduction and reservoir protection properties, providing significant theoretical insights for improving hydraulic fracturing efficiency. Full article

The emergence of 6G wireless systems marks a paradigm shift toward intelligent, context-aware networks that can adapt in real-time to their environment. Within this landscape, Sensing-Assisted Communication (SAC) emerges as a key enabler, integrating perception into the communication control loop to enhance reliability, beamforming accuracy, and system responsiveness. Unlike prior surveys that treat SAC as a subfunction of Integrated Sensing and Communication (ISAC), this work offers the first dedicated review of SAC in Millimeter-Wave (mmWave) and Sub-Terahertz (Sub-THz) systems, where directional links and channel variability present core challenges. SAC encompasses a diverse set of methods that enable wireless systems to dynamically adapt to environmental changes and channel conditions in real time. Recent studies demonstrate up to 80% reduction in beam training overhead and significant gains in latency and mobility resilience. Applications include predictive beamforming, blockage mitigation, and low-latency Unmanned Aerial Vehicle (UAV) and vehicular communication. This review unifies the SAC landscape and outlines future directions in standardization, Artificial Intelligence (AI) integration, and cooperative sensing for next-generation wireless networks. Full article

With the advancement of technologies such as 5G, digital twins, and edge computing, the Internet of Things (IoT) as a critical component of intelligent systems is profoundly driving the transformation of various industries toward digitalization and intelligence. However, the exponential growth of network connection nodes has expanded the attack exposure surface of IoT devices. The IoT devices with limited storage and computing resources struggle to cope with new types of attacks, and IoT devices lack mature authorization and authentication mechanisms. It is difficult for traditional data-sharing solutions to meet the security requirements of cloud-based shared data. Therefore, this paper proposes a blockchain-based multi-authority IoT data-sharing scheme with attribute-based searchable encryption for intelligent system (BM-ABSE), aiming to address the security, efficiency, and verifiability issues of data sharing in an IoT environment. Our scheme decentralizes management responsibilities through a multi-authority mechanism to avoid the risk of single-point failure. By utilizing the immutability and smart contract function of blockchain, this scheme can ensure data integrity and the reliability of search results. Meanwhile, some decryption computing tasks are outsourced to the cloud to reduce the computing burden on IoT devices. Our scheme meets the static security and IND-CKA security requirements of the standard model, as demonstrated by theoretical analysis, which effectively defends against the stealing or tampering of ciphertexts and keywords by attackers. Experimental simulation results indicate that the scheme has excellent computational efficiency on resource-constrained IoT devices, with core algorithm execution time maintained in milliseconds, and as the number of attributes increases, it has a controllable performance overhead. Full article

Underwater target detection is a critical technology for marine resource management and ecological protection, but its performance is often limited by complex underwater environments, including optical attenuation, scattering, and dense distributions of small targets. Existing methods have significant limitations in feature extraction efficiency, robustness in class-imbalanced scenarios, and computational complexity. To address these challenges, this study proposes a lightweight adaptive detection model, YOLOv11-MSE, which optimizes underwater detection performance through three core innovations. First, a multi-scale dilated attention (MSDA) mechanism is embedded into the backbone network to dynamically capture multi-scale contextual features while suppressing background noise. Second, a Slim-Neck architecture based on GSConv and VoV-GSCSPC modules is designed to achieve efficient feature fusion via hybrid convolution strategies, significantly reducing model complexity. Finally, an efficient multi-scale attention (EMA) module is introduced in the detection head to reinforce key feature representations and suppress environmental noise through cross-dimensional interactions. Experiments on the underwater detection dataset (UDD) demonstrate that YOLOv11-MSE outperforms the baseline model YOLOv11, achieving a 9.67% improvement in detection precision and a 3.45% increase in mean average precision (mAP50) while reducing computational complexity by 6.57%. Ablation studies further validate the synergistic optimization effects of each module, particularly in class-imbalanced scenarios where detection precision for rare categories (e.g., scallops) is significantly enhanced, with precision and mAP50 improving by 60.62% and 10.16%, respectively. This model provides an efficient solution for edge computing scenarios, such as underwater robots and ecological monitoring, through its lightweight design and high underwater target detection capability. Full article

Background: As a globally significant aquaculture species, elucidating the molecular mechanisms underlying the regulation of the Pacific White Shrimp (Litopenaeus vannamei) growth holds substantial scientific and industrial value. This study systematically investigates the role of the LvChia2 gene in governing growth and development through a cross-tissue metabolic network approach. Methods: RNA knockdown (RNAi)-mediated knockdown of LvChia2 significantly impaired growth performance and triggered a tissue-specific metabolic compensation mechanism. Results: This mechanism was characterized by reduced crude lipid content in muscle and adaptive modulation of lipase (LPS) activities in hepatopancreatic and intestinal tissues, suggesting inter-tissue metabolic coordination. Transcriptomic profiling identified 610 differentially expressed genes (DEGs), forming a three-dimensional regulatory network encompassing “energy metabolism, molt regulation, and nutrient utilization.” Key mechanistic insights revealed the following: (1) Enhanced mitochondrial energy transduction through the upregulation of ATP synthase subunits and NADH dehydrogenase (ND-SGDH). (2) The disruption of ecdysteroid signaling pathways via suppression of Krueppel homolog 1 (Kr-h1). (3) The coordinated regulation of nitrogen metabolism through the downregulation of glutamine synthetase and secretory phospholipase A2. These molecular adaptations, coupled with tissue-specific oxidative stress responses, reflect an integrated physiological strategy for environmental adaptation. Conclusions: Notably, this study provides the first evidence in crustaceans of chitinase-mediated growth regulation through cross-tissue metabolic interactions and identifies six core functional genes (ATP5L, ATP5G, ND-SGDH, Kr-h1, GS, sPLA2) as potential targets for molecular breeding. A novel “gut-hepatopancreas axis” energy compensation mechanism is proposed, offering insights into resource allocation during metabolic stress. These findings advance our understanding of crustacean growth regulation and establish a theoretical foundation for precision aquaculture strategies, including genome editing and multi-trait genomic selection. Full article

Accurate wind field modeling over hilly terrain is critical for wind energy, infrastructure safety, and environmental assessment, yet its inherent complexity poses significant simulation challenges. This paper systematically reviews this field’s major advances by analyzing 610 key publications from 2015 to 2024, selected from core databases (e.g., Web of Science and Scopus) through targeted keyword searches (e.g., ‘wind flow’, ‘complex terrain’, ‘CFD’, ‘hilly’) and subsequent rigorous relevance screening. We critique four primary modeling paradigms—field measurements, wind tunnel experiments, Computational Fluid Dynamics (CFD), and data-driven methods—across three key application areas, filling a gap left by previous single-focus reviews. The analysis confirms CFD’s dominance (75% of studies), with a clear shift from idealized 2D to real 3D terrain. Key findings indicate that high-fidelity coupled models (e.g., LES), validated against benchmark field experiments such as Perdigão, can reduce mean wind speed prediction bias to below 0.1 m/s; and optimized engineering designs for mountainous infrastructure can mitigate local wind speed amplification effects by 15–20%. Data-driven surrogate models, represented by FuXi-CFD, show revolutionary potential, reducing the inference time for high-resolution wind fields from hours to seconds, though they currently lack standardized validation. Finally, this review summarizes persistent challenges and outlines future directions, advocating for physics-informed neural networks, high-fidelity multi-scale models, and the establishment of open-access benchmark datasets. Full article

In recent years, the rapid expansion of urban rail transit has significantly improved travel efficiency, yet it has also exacerbated spatial inequality in service coverage. Accessibility, as a fundamental metric for evaluating the equity of service distribution, remains limited by three major shortcomings in current assessment methods: the neglect of actual road network characteristics, reliance on a single static scale, and the absence of quantitative mechanisms to assess accessibility equity. These deficiencies hinder a comprehensive understanding of how equity evolves with the spatiotemporal dynamics of rail systems. To address the aforementioned issues, this study proposes an innovative spatiotemporally dynamic and multi-scale analytical framework for evaluating urban rail accessibility and its equity implications. Specifically, we develop a network-based buffer decay model to refine service population estimation by incorporating realistic walking paths, capturing both distance decay and road network constraints. The framework integrates multiple spatial analytical techniques, including the Gini coefficient, Lorenz curve, global and local spatial autocorrelation, center-of-gravity shift, and standard deviation ellipse, to quantitatively assess the equity and evolutionary patterns of accessibility across multiple spatial scales. Taking the central urban area of Hangzhou as a case study, this research investigates the spatiotemporal patterns and equity changes in metro station accessibility in 2019 and 2023. The results indicate that the expansion of the metro network has partially improved overall accessibility equity: the Gini coefficient at the TAZ (Traffic Analysis Zone) scale decreased from 0.56 to 0.425. Nevertheless, significant inequality remains at finer spatial resolutions (grid-level Gini coefficient = 0.404). In terms of spatial pattern, the core area (e.g., Wulin Square) forms a ‘high-high’ accessibility agglomeration area, while the urban fringe area (e.g., northern Yuhang) presents a ‘low-low’ agglomeration, and the problem of local ‘accessibility depression’ still exists. Additionally, the accessibility centroid has consistently shifted northwestward, and the long axis of the standard deviation ellipse has rotated from an east–west to a northwest-southeast orientation, indicating a growing spatial polarization between core and peripheral zones. The findings suggest that improving equity in urban rail accessibility cannot rely solely on expanding network size; rather, it requires coordinated strategies involving network structure optimization, branch line development, multimodal integration, and the construction of efficient transfer systems to promote more balanced and equitable spatial distribution of rail transit resources citywide. Full article