Search Results (219)

Sulforaphane, a natural isothiocyanate predominantly found in cruciferous vegetables, has shown potential in preventing and treating Helicobacter pylori infection. However, the underlying metabolic mechanisms remain largely unclear. This study employed high-coverage metabolomics and lipidomics methods to comprehensively investigate the effects of sulforaphane on the serum and liver metabolic profiles of H. pylori-infected mice. Metabolomics and lipidomics analysis revealed that H. pylori infection disrupted multiple metabolic pathways, leading to perturbations in amino acids, fatty acids, bile acids, and various lipid species. Sulforaphane treatment can ameliorate these disruptions, notably reversing alterations in serum glycerophospholipids and restoring hepatic levels of amino acids, bile acids, glycerophospholipids, ceramides, and peptides. Key metabolic pathways implicated included glutathione metabolism and glycine and serine metabolism, which are associated with antioxidant defense and host resistance to pathogenic infections. These findings offer a comprehensive metabolic basis for understanding the therapeutic effects of sulforaphane against H. pylori infection. Full article

As an intensive eco-agricultural model, the rice–crayfish co-culture (RCC) system has been widely adopted in recent years due to its remarkable advantages in resource use, efficiency, and economic benefits. However, the long-term mechanisms by which this system affects the quantity and stability of soil aggregate, as well as the vertical distribution of soil organic carbon (SOC) within aggregate across soil profiles, remain unclear. This study investigated the effects of varying duration (4 and 8 years) of RCC in Qianjiang City, Hubei Province. Soil samples were collected from six depth layers (0–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, 40–80 cm, and 80–120 cm) to analyze the distribution characteristics of soil aggregate and SOC. The results demonstrated that, compared to the field which used RCC for a duration of 4 years, the field which used RCC for a duration of 8 years significantly reduced bulk density (BD) by 16.3% in the 40–80 cm layer. However, prolonged flooding has led to a 9.6% increase in the BD of the plow pan layer (10–20 cm) due to hydrostatic pressure and mechanical disturbances. Furthermore, the use of RCC for a duration of 8 years significantly enhanced the mass fractions of water-stable aggregates > 2 mm in the 0–80 cm soil layer at 0–10 cm (25.9%), 10–20 cm (30.2%), 20–30 cm (141.8%), 30–40 cm (172.4%), and 40–80 cm (112.9%), and improved aggregate stability throughout the entire soil profile. In terms of SOC distribution, the SOC concentration increased significantly with prolonged RCC usage across all soil layers, particularly in the 0–20 cm layer. The SOC was primarily derived from >2 mm (Large aggregate). Notably, although < 0.053 mm (Silt and clay) constituted a small proportion of the 0–20 cm layer, their SOC concentration reached 15.3–20.55 g kg⁻¹. Overall, extended RCC duration reduced BD in nearly all soil layers, promoted the formation of macro-aggregate, enhanced aggregate stability, and increased the SOC concentration within macro-aggregate, while strengthening the SOC stocks capacity of the 80–120 cm soil layer from 2.58 kg C m⁻² to 4.35 kg C m⁻², an increase of 68.6%. Full article

The global antimicrobial resistance crisis demands innovative strategies to combat bacterial infections, including those caused by drug-sensitive pathogens that evade treatment through biofilm formation or metabolic adaptations. Here, we demonstrate that Squama Manitis extract (SME)—a traditional Chinese medicine component—exhibits broad-spectrum bactericidal activity against clinically significant pathogens, including both Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) species (MIC = 31.25 mg/mL), achieving significant reduction in bacterial viability within 24 h. Through integrated multi-omics analysis combining scanning electron microscopy and RNA sequencing, we reveal SME’s unprecedented tripartite mechanism of action: (1) direct membrane disruption causing cell envelope collapse, (2) metabolic paralysis through coordinated suppression of TCA cycle and fatty acid degradation pathways, and (3) inhibition of DNA repair systems (SOS response and recombination downregulation). Despite its potent activity, SME shows low cytotoxicity toward mammalian cells (>90% viability) and can penetrate Gram-negative outer membranes. These features highlight SME’s potential to address drug-resistant infections through synthetic lethality across stress response, energy metabolism, and DNA integrity pathways. While advocating for synthetic alternatives to endangered animal products, this study establishes SME as a polypharmacological template for resistance-resilient antimicrobial design, demonstrating how traditional knowledge and modern systems biology can converge to guide sustainable anti-infective development. Full article

Petroleum oily sludge (OLS), a hazardous by-product of the petroleum industry, and high-alkali lignite (HAL), an underutilized low-rank coal, pose significant challenges to sustainable waste management and resource efficiency. This study systematically investigated the combustion behavior, reaction pathways, and gaseous-pollutant-release mechanisms across varying blend ratios, utilizing integrated thermogravimetric-mass spectrometry analysis (TG-MS), interaction analysis, and kinetic modeling. The key findings reveal that co-combustion significantly enhances the combustion performance compared to individual fuels. This is evidenced by reduced ignition and burnout temperatures, as well as an improved comprehensive combustion index. Notably, an interaction analysis revealed coexisting synergistic and antagonistic effects, with the synergistic effect peaking at a blending ratio of 50% OLS due to the complementary properties of the fuels. The activation energy was found to be at its minimum value of 32.5 kJ/mol at this ratio, indicating lower reaction barriers. Regarding gas emissions, co-combustion at a 50% OLS blending ratio reduces incomplete combustion products while increasing CO₂, indicating a more complete reaction. Crucially, sulfur-containing pollutants (SO₂, H₂S) are suppressed, whereas nitrogen-containing emissions (NH₃, NO₂) increase but remain controllable. This study provides novel insights into the synergistic mechanisms between OLS and HAL during co-combustion, offering foundational insights for the optimization of OLS-HAL combustion systems toward efficient energy recovery and sustainable industrial waste management. Full article

The spontaneous combustion disaster of coal not only causes a waste of resources but also affects the safe production of coal mines. In order to accurately detect the range and location of the spontaneous combustion source of coal, this paper studies and summarizes previous research results, and based on the principles and research and development progress of existing detection technologies such as the surface temperature measurement method, ground temperature measurement method, wellbore temperature measurement method, and infrared remote sensing detection method, it briefly reviews the application of various detection technologies in engineering practice at this stage and briefly explains the advantages and disadvantages of each application. Research shows that the existing technologies are generally limited by the interference of complex environmental conditions (such as temperature measurement deviations caused by atmospheric turbulence and the influence of rock layer structure on ground temperature conduction) and the implementation difficulties of geophysical methods in mining applications (such as the interference of stray currents in the ground by electromagnetic methods and the fast attenuation speed of waves detected by geological radar methods), resulting in the insufficient accuracy of fire source location and difficulties in identifying concealed fire sources. In response to the above bottlenecks, the ”air–ground integrated” fire source location determination technology that breaks through environmental constraints and the location determination method of a CSC fire source based on a multi-physics coupling mechanism are proposed. By significantly weakening the deficiency in obtaining parameters through a single detection method, a new direction is provided for the detection of coal spontaneous combustion fire sources in the future. Full article

Background: Huang Qin Decoction (HQD) is a well-established Traditional Chinese Medicine (TCM) formulation recognized for its application in the treatment of colorectal cancer (CRC). However, the precise therapeutic mechanisms remain inadequately defined. Methods: This study integrates metabolomics from a mouse model and network pharmacology to screen potential targets and bio-active ingredients of HQD. The pharmacological activity of HQD for CRC was evidenced via single-cell RNA sequencing (scRNA-seq), molecular docking, and molecular dynamics simulations. Atomic force microscopy (AFM) assays and cellular experimental validation were used to confirm the relative mechanisms. Results: The metabolite profile undergoes significant alterations, with metabolic reprogramming evident during the malignant progression of CRC liver metastasis. Network pharmacology analysis identified that HQD regulates several metabolic pathways, including arginine biosynthesis, alanine, aspartate, and glutamate metabolism, nitrogen metabolism, phenylalanine metabolism, and linoleic acid metabolism, by targeting key proteins such as aspartate aminotransferase (GOT1), cytochrome P450 1A2 (CYP1A2), and carbonic anhydrase 2 (CA2). ScRNA-seq analysis indicated that HQD may enhance the functionality of cytotoxic T cells, thereby reversing the immunosuppressive microenvironment. Virtual verification revealed a strong binding affinity between the identified hub targets and active constituents of HQD, a finding subsequently corroborated by AFM assays. Cellular experiments confirmed that naringenin treatment inhibits the proliferation, migration, and invasion of CRC cells by downregulating GOT1 expression and disrupting glutamine metabolism. Conclusions: Computational prediction and in vitro validation reveal the active ingredients, potential targets, and molecular mechanisms of HQD against CRC liver metastasis, thereby providing a scientific foundation for the application of TCM in CRC treatment. Full article

Anesthesia is a cornerstone of modern medicine, enabling surgery, pain management, and critical care. Despite its widespread use, the precise molecular mechanisms of anesthetic action remain incompletely understood. Recent advancements in structural biology, including cryo-electron microscopy (Cryo-EM), X-ray crystallography, and computational modeling, have provided high-resolution insights into anesthetic–target interactions. This review examines key molecular targets, including GABA_A receptors, NMDA receptors, two-pore-domain potassium (K2P) channels (e.g., TREK-1), and voltage-gated sodium (Nav) channels. General anesthetics modulate GABA_A and NMDA receptors, affecting inhibitory and excitatory neurotransmission, while local anesthetics primarily block Nav channels, preventing action potential propagation. Structural studies have elucidated anesthetic binding sites and gating mechanisms, providing a foundation for drug optimization. Advances in computational drug design and AI-assisted modeling have accelerated the development of safer, more selective anesthetics, paving the way for precision anesthesia. Future research aims to develop receptor-subtype-specific anesthetics, Nav1.7-selective local anesthetics, and investigate the neural mechanisms of anesthesia-induced unconsciousness and postoperative cognitive dysfunction (POCD). By integrating structural biology, AI-driven drug discovery, and neuroscience, anesthesia research is evolving toward safer, more effective, and personalized strategies, enhancing clinical outcomes and patient safety. Full article

Suaeda salsa, a halophytic plant species, exhibits a remarkable salt tolerance and demonstrates a significant phytoremediation potential through its capacity to absorb and accumulate saline ions and heavy metals from soil substrates, thereby contributing to soil quality amelioration. Furthermore, this species serves as a critical habitat component for avifauna populations and represents a keystone species in maintaining ecological stability within estuarine and coastal wetland ecosystems. With the development and maturity of UAV remote sensing technology in recent years, the advantages of using UAV imagery to extract weak targets are becoming more and more obvious. In this paper, for Suaeda salsa, which is a weak target with a sparse distribution and inconspicuous features, relying on the high-resolution and spatial information-rich features of UAV imagery, we establish an adaptive contextual information extraction deep learning semantic segment model (ACI-Unet), which can solve the problem of recognizing Suaeda salsa from high-precision UAV imagery. The precise extraction of Suaeda salsa was completed in the coastal wetland area of Dongying City, Shandong Province, China. This paper achieves the following research results: (1) An Adaptive Context Information Extraction module based on large kernel convolution and an attention mechanism is designed; this module functions as a multi-scale feature extractor without altering the spatial resolution, enabling a seamless integration into diverse network architectures to enhance the context-aware feature representation. (2) The proposed ACI-Unet (Adaptive Context Information U-Net) model achieves a high-precision identification of Suaeda salsa in UAV imagery, demonstrating a robust performance across heterogeneous morphologies, densities, and scales of Suaeda salsa populations. Evaluation metrics including the accuracy, recall, F1 score, and mIou all exceed 90%. (3) Comparative experiments with state-of-the-art semantic segmentation models reveal that our framework significantly improves the extraction accuracy, particularly for low-contrast and diminutive Suaeda salsa targets. The model accurately delineates fine-grained spatial distribution patterns of Suaeda salsa, outperforming existing approaches in capturing ecologically critical structural details. Full article

Innovation-driven development is the main driving strategy for promoting high-quality economic development. Technological innovation is the core of innovation-driven development. Financial innovation is an important aspect of promoting financial development. As such, the coupling and coordination of the technological innovation and financial development in developing countries, such as China, is an important issue. The topic has been extensively studied over the last decade in the context of China, and a dominating method has emerged on how to model the technological innovation subsystem and the financial development subsystem, and how to quantitatively determine the degree of coupling and coordination of the two subsystems. A variety of predictors have been proposed to model each subsystem. The coupling degree and the coordination degree are then calculated, and then they are used to analyze the current development status for potential issues. However, we make an effort to validate the calculated degree of coupling and coordination before the results are used for the analysis.Without validation, the outcomes of the analysis not only might not be useful but also could lead to inappropriate governmental policies. That said, it is tremendously challenging to validate the results due to the lack of the ground truth. The goal of this study is to work towards objectively determining the reliability of the degree of coupling and coordination from an engineering perspective. Specifically, we accomplish this task by evaluating the regression performance and projection performance. We demonstrate that the use of a carefully crafted set of predictors for each subsystem is the foundation for deriving the reliable coordination degree of the two subsystems. Full article

This study was conducted to investigate the effects of replacing fish meal with either whole-fat or defatted krill powder on the growth, body color, immunity, and related gene expression of red–white koi carp. A total of 630 red–white koi carp with an initial body mass of 13.5 ± 0.05 g were randomly divided into seven groups with three replicates per group and 30 fish per replicate. The control group was fed a basic diet (C0). The other six diets were supplemented with different levels of whole krill meal or defatted krill meal as replacements (10% whole fat, 20% whole fat, 30% whole fat, 10% defatted, 20% defatted, and 30% defatted) in the experimental groups, named W10, W20, W30, D10, D20, and D30, respectively, for a total duration of 60 days. The growth, body color, immunity and gene expression indexes were measured in the koi after completion. The results indicate the following. (1) Compared with C0, the experimental groups of koi showed a significant increase in the specific growth rate (SGR) (p < 0.05), while the hepatosomatic index (HSI) and viscerosomatic ratio (VSI) decreased. Additionally, there was a significant increase in the relative expression level of insulin-like growth factor (IGF-1) in both the liver and muscle (p < 0.05). (2) The experimental groups of koi carp exhibited a significant increase in the carotenoid content in the scales and skin, as well as an elevated relative expression level of the tyrosinase (TYR) gene in the muscle (p < 0.05). (3) The lysozyme (LZM) and superoxide dismutase (SOD) activities were significantly increased in the experimental groups compared to C0 (p < 0.05). Additionally, the SOD activity was significantly higher in the defatted groups than in the whole-fat groups (p < 0.05). Furthermore, the liver alkaline phosphatase (AKP) activity was significantly lower in the D20 and D30 groups compared to the other five groups. (4) The expression level of LPL was significantly lower in the liver and muscle of the whole-fat group compared to the defatted group (p < 0.05). Tissue section observation revealed that the hepatocytes in the W20 and W30 groups exhibited a reduced size and an increased lipid droplet count, while the vacuolar degeneration of the hepatocytes increased in the D30 group. In conclusion, replacing fish meal with whole-fat or defatted krill powder or defatted Antarctic krill meal significantly improves the growth performance, body color, and immunity of red–white koi carp. However, excessive addition of krill meal can easily cause liver damage. The recommended replacement level for whole krill powder is 20% to 30%, while defatted krill powder should be replaced at a level of 10% to 20%. Full article

Azvudine (FNC) is a novel cytidine analogue that is widely used in the treatment of infectious diseases such as AIDS and COVID-19. Previous studies have demonstrated its anticancer activity in various cancer cell lines, including non-Hodgkin’s lymphomas and lung adenocarcinoma cell lines. However, its effects on hepatocellular carcinoma (HCC) and the underlying mechanisms remain unclear. This study aimed to investigate the anti-epithelial–mesenchymal transition (anti-EMT) activity of FNC and evaluate its potential application in HCC treatment. We found that FNC significantly inhibits the migration of the liver cancer cell line Huh7 by downregulating key EMT markers, such as matrix metalloproteinases (MMPs) and E-cadherin, at both the transcriptional and protein expression levels. Notably, we found that FNC inhibits HEY proteins, particularly HEY1, a transcriptional regulator of the Notch signalling pathway that is overexpressed in approximately 50% of HCC patients. To identify the primary target of FNC, microscale thermophoresis (MST) and molecular dynamics (MD) simulations were performed, revealing that FNC directly binds to Jagged1. This study provides valuable insights into the therapeutic potential of FNC in HCC treatment and elucidates its underlying mechanisms. Full article

Epoxy resins (ERs) are esteemed for their mechanical robustness and adhesive qualities, particularly in steel bridge deck applications. Nonetheless, their intrinsic brittleness limits broader utility. This study addresses this limitation by modulating ER crosslink density through adjustments in curing agent concentration, incorporation of hyperbranched polymers (HBPs), and optimization of curing conditions. Employing a multi-objective optimization strategy, this research aims to enhance toughness while minimizing strength degradation. Non-isothermal curing kinetics, realized using the differential scanning calorimetry (DSC) method, attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), tensile testing, and thermogravimetric analysis (TGA), were employed to investigate the effects of curing agent and HBP content on the curing reaction, mechanical properties, and thermal stability, respectively. Response surface methodology facilitated comprehensive optimization. Findings indicate that both curing agent and HBP contents significantly influence curing dynamics and mechanical performance. Curing agent content below 40% or above 50% can induce side reactions, adversely affecting properties. While a curing agent content exceeding 45% or an HBP content exceeding 5% improves the toughness of ER, these increases concurrently reduce mechanical strength and thermal stability. The study identifies an optimal formulation comprising 45.21% curing agent, a curing temperature of 60.45 °C, and 5.77% HBP content. Full article

In 5G-and-beyond (B5G) Internet of Things (IoT) networks, the integration of intelligent reflecting surfaces (IRSs) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) techniques can significantly improve signal quality and increase network capacity. However, a single fixed IRS lacks the dynamic adjustment capability to flexibly adapt to complex environmental changes and diverse user demands, while mmWave MIMO is constrained by limited coverage. Motivated by these challenges, we investigate the application of drone-mounted IRS-assisted MIMO communications in B5G IoT networks, where multiple IRS-equipped drones are deployed to provide real-time communication support. To fully exploit the advantages of the proposed MIMO-enabled air-to-ground integrated information transmission framework, we formulate a joint optimization problem involving beamforming, phase shift design, and drone deployment, with the objective of maximizing the sum of achievable weighted data rates (AWDRs). Given the NP-hard nature of the problem, we develop an iterative optimization algorithm to solve it, where the optimization variables are tackled in turn. By employing the quadratic transformation technique and the Lagrangian multiplier method, we derive closed-form solutions for the optimal beamforming and phase shift design strategies. Additionally, we optimize drone deployment by using a distributed discrete-time convex optimization approach. Finally, the simulation results show that the proposed scheme can improve the sum of AWDRs in comparison with the state-of-the-art schemes. Full article

Fault diagnosis in large-scale systems presents significant challenges due to the complexity and high dimensionality of data, as well as the scarcity of labeled fault data, which are hard to obtain during the practical operation process. This paper proposes a novel approach, called Multi-Variable Meta-Transformer (MVMT), to tackle these challenges. In order to deal with the multi-variable time series data, we modify the Transformer model, which is the currently most popular model on feature extraction of time series. To enable the Transformer model to simultaneously receive continuous and state inputs, we introduced feature layers before the encoder to better integrate the characteristics of both continuous and state variables. Then, we adopt the modified model as the base model for meta-learning—more specifically, the Model-Agnostic Meta-Learning (MAML) strategy. The proposed method leverages the power of Transformers for handling multi-variable time series data and employs meta-learning to enable few-shot learning capabilities. The case studies conducted on the Tennessee Eastman Process database and a Power-Supply System database demonstrate the exceptional performance of fault diagnosis in few-shot scenarios, whether based on continuous-only data or a combination of continuous and state variables. Full article

The peripartum period is critical for breeding female donkeys (i.e., jennies) and ensuring the delivery of healthy neonatal foals. The gut microbiota deeply influences the host metabolism. This study aimed to investigate the dynamic changes in the gut microbiome during the peripartum period in jennies. Fresh fecal samples of eight adult jennies were collected at the following seven sampling time points: 21, 7, and 3 days prepartum (G21, G7, and G3) and 1, 3, 7, and 14 days postpartum (L1, L3, L7, and L14). Sequencing of the V4 hypervariable regions of the 16S rRNA genes was carried out using fecal samples to identify the differences in the microbiome across the peripartum period. Bacteroidota and Firmicutes were the most abundant bacterial phyla in the feces. Treponema and Lachnospiraceae XPB1014 group significantly increased in the L3 group compared to the G7 group (q < 0.05), and a decline trend was observed in L1 group around parturition. The genus Clostridium sensu stricto 1, family Clostridiaceae, and order Clostridiales were considered to be biomarkers of the L3 group. Among the 25 functional pathways detected by Kyoto Encyclopedia of Genes and Genomes pathway analysis, beta lactam resistance, insulin resistance, and peptidases were the top three important pathways observed in the gut microbiota during the peripartum period in jennies. The gut microbial structure changed significantly at different time points during the peripartum period in jennies. These results contribute to a better understanding of the gut microbiota to ensure health care during important phases from late pregnancy to early lactation in jennies. Full article