Search Results (10,475)

The emergence of SARS-CoV-2 variants highlights the urgent need for novel therapeutic strategies, particularly entry inhibitors that could efficiently prevent viral infection. Medicinal herbs and herbal combination formulas have long been recognized for their effects in treating infectious diseases and their antiviral properties, thus providing abundant resources for the discovery of antiviral candidates. While many candidates have been suggested to have antiviral activity against SARS-CoV-2 infection, few have been validated for their mechanisms, including possible effects on viral entry. This study aimed to identify SARS-CoV-2 entry inhibitors from medicinal herbs and herbal formulas that are known for heat-clearing and detoxifying properties and/or antiviral activities. A SARS-CoV-2 pseudoparticle (SARS-CoV-2pp) system was used to assess mechanism-specific entry inhibition. Our results showed that the methanol extract of Anemarrhena asphodeloides rhizome, as well as the water extracts of Cimicifuga foetida rhizome, Xiao Chai Hu Tang (XCHT), and Sheng Ma Ge Gen Tang (SMGGT), have substantial inhibitory effects on the entry of SARS-CoV-2pps into host cells. Given the observation that Cimicifuga foetida exhibited the most potent inhibition and is a constituent of SMGGT, we further investigated the major compounds of the herb and identified caffeic acid as a bioactive component for blocking SARS-CoV-2pp entry. Entry inhibition of Cimicifuga foetida and caffeic acid was validated on both wild-type and the currently dominant JN.1 strain SARS-CoV-2pp systems. Moreover, caffeic acid was able to both inactivate the pseudoparticles and prevent their entry into pretreated host cells. The results support the traditional use of these herbal medicines and underscore their potential as valuable resources for identifying active compounds and developing therapeutic entry inhibitors for the management of COVID-19. Full article

Reactions of the angular 4,4′-oxybis[N-(pyridin-3-ylmethyl)benzamide] (L) with dicarboxylic acids and transition metal salts afforded non-entangled {[Cd(L)(1,3-BDC)(H₂O)]∙2H₂O}_n (1,3-BDC = 1,3-benzenedicarboxylic acid), 1; {[Cd(L)(1,4-HBDC)(1,4-BDC)_0.5]∙2H₂O}_n (1,4-BDC = 1,4-benzenedicarboxylic acid), 2; {[Cu₂(L)₂(1,3-BDC)₂]∙1.5H₂O}_n, 3; {[Ni(L)(1,3-BDC)(H₂O)]∙2H₂O}_n, 4; {[Zn(L)(1,3-BDC)]∙4H₂O}_n, 5; {[Zn(L)(1,4-BDC)]∙2H₂O}_n, 6; and [Cd₃(L)₂(1,4-BDC)₃]_n, 7, which have been structurally characterized by using single-crystal X-ray diffraction. Complexes 1–5 and 7 are 2D layers, giving (6⁴·8·10)(6)-2,4L3, (4²·8²·10²)(4²·8⁴)₂(4)₂, (4·5·6)(4·5⁵·6³·7)-3,5L66, (6⁴·8·10)(6)-2,4L3, interdigitated (8⁴·12²)(8)₂-2,4L2 and (3⁶·4⁶·5³)-hxl topologies, respectively, and 6 is a 1D chain with the (4³·6²·8)(4)-2,4C3 topology. The factors that govern the structures of 1–7 are discussed and the thermal properties of 1–7 and the luminescent properties of complexes 1, 2, 5 and 6 are investigated. The stabilities of complexes 1 and 5 toward the detection of Fe³⁺ ions are also evaluated. Full article

Fat embolism is a critical medical emergency often resulting from long bone fractures or amputations, leading to acute respiratory distress syndrome (ARDS). The NOD-like receptor pyrin domain-containing 3 (NLRP3) inflammasome, a key regulator of innate immunity, is activated by reactive oxygen species and tissue damage, contributing to inflammatory responses. This study examines the role of NLRP3 in fat embolism-induced ARDS and evaluates the therapeutic potential of MCC950, a selective NLRP3 antagonist. Fat embolism was induced by fatty micelle injection into the tail vein of Sprague Dawley rats. Pulmonary injury was assessed through lung weight gain as an edema indicator, NLRP3 expression via Western blot, and IL-1β levels using ELISA. Histological damage and macrophage infiltration were evaluated with hematoxylin and eosin staining. Fat embolism significantly increased pulmonary NLRP3 expression, lipid peroxidation, IL-1β release, and macrophage infiltration within four hours, accompanied by severe pulmonary edema. NLRP3 was localized in type I alveolar cells, co-localizing with aquaporin 5. Administration of MCC950 significantly reduced inflammatory responses, lipid peroxidation, pulmonary edema, and histological damage, while attenuating MAPK cascade phosphorylation of ERK and Raf. These findings suggest that NLRP3 plays a critical role in fat embolism-induced acute respiratory distress syndrome, and its inhibition by MCC950 may offer a promising therapeutic approach. Full article

Microglia-mediated chronic neuroinflammation is a common pathological feature of Parkinson’s disease (PD). Strong evidence suggests that activated microglia can lesion neurons by releasing exosomes. However, the mechanisms of exosome release from activated microglia remain unclear. We recently revealed a key role of complement receptor 3 (CR3) in regulating microglial activation in the process of progressive neurodegeneration. This study aimed to investigate whether CR3 can regulate exosome release from activated microglia, as well as the underlying mechanisms. We found that LPS, an inducer of microglial M1 activation, induced exosome release from activated microglia. Inhibition of exosome synthesis suppressed LPS-induced microglial activation, gene expression of proinflammatory factors, and related neurotoxicity. Silencing or knocking out CR3 attenuated LPS-induced exosome release in microglia. NADPH oxidase (NOX2) was further identified as a downstream signal of CR3, mediating microglial exosome release and related neurotoxicity. CR3 silencing blocked LPS-induced NOX2 activation and superoxide production through inhibition of p47^phox phosphorylation and membrane translocation. Moreover, NOX2 activation elicited by PMA or supplementation of H₂O₂ recovered exosome release from CR3-silenced microglia. Subsequently, we demonstrated that the CR3-NOX2 axis regulates syntenin-1 to control microglial exosome release. Finally, we observed that the expression of CR3 was increased in the brain of LPS-treated mice, and genetic ablation of CR3 significantly reduced LPS-induced NOX2 activation, microglial M1 polarization, and exosome production in mice. Overall, our findings revealed a critical role of the CR3-NOX2 axis in controlling microglial exosome release and related neurotoxicity through syntenin-1, providing a novel target for the development of a therapeutic strategy for neuroinflammation-mediated neurodegeneration. Full article

Background: Nasopharyngeal carcinoma (NPC) is an epithelial malignancy arising from the nasopharyngeal mucosa. Despite treatment advances such as the use of intensity-modulated radiotherapy and immune checkpoint inhibitors, resistance remains a significant clinical challenge. Many tumours are also diagnosed at an advanced stage associated with poor prognosis. Objective: This review aims to explore the biological roles of autophagy in NPC, primarily highlighting its involvement in disease pathogenesis and treatment resistance. Methods: We performed a review of the recent literature examining the role of autophagy-related pathways in NPC pathogenesis, biomarker discovery, and therapeutic targeting. Results: Autophagy plays a dual role in NPC as it contributes to both tumour suppression and progression. It is involved in tumour initiation, metastasis, immune modulation, and treatment resistance. Autophagy-related genes such as SQSTM1, Beclin-1, and AURKA may serve as prognostic and therapeutic biomarkers. Various strategies are being investigated for their role to modulate autophagy using pharmacologic inhibitors, RNA interventions, and natural compounds. Conclusions: Further research into autophagy’s context-dependent roles in NPC may inform the development of personalised therapies and allow progress in translational and precision oncology. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV), an enveloped single-stranded positive-sense RNA virus, poses a significant threat to global swine production. Despite the availability of modified live virus and inactivated vaccines, their limited efficacy and safety concerns highlight the urgent need for novel antiviral therapeutics. This study aimed to investigate the molecular mechanisms by which lycopene inhibits PRRSV replication. Initial assessments confirmed that lycopene did not adversely affect cellular viability, cell cycle progression, or apoptosis. Using fluorescence microscopy, flow cytometry, immunoblotting, quantitative real-time PCR (qRT-PCR), and viral titration assays, lycopene was shown to exhibit potent antiviral activity against PRRSV. Mechanistic studies revealed that lycopene suppresses reactive oxygen species (ROS) production, which is critical for PRRSV proliferation. Additionally, lycopene attenuated PRRSV-induced inflammatory responses, as demonstrated by immunoblotting, ELISA, and qRT-PCR assays. These findings suggest that lycopene inhibits PRRSV replication by modulating ROS levels and mitigating inflammation, offering a promising avenue for the development of antiviral therapeutics. This study provides new insights and strategies for combating PRRSV infections, emphasizing the potential of lycopene as a safe and effective antiviral agent. Full article

With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning framework that coordinates and integrates multiple types of flexibility resources through joint optimization and network reconfiguration to enhance system adaptability and operational resilience. A novel virtual network coupling modeling approach is proposed to address topological constraints during network reconfiguration, ensuring radial operation while allowing rapid topology adjustments to isolate faults and restore power supply. Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. In this model, the upper-level planning stage optimizes the siting and sizing of flexibility resources, while the lower-level operational stage coordinates real-time dispatch strategies through demand response, energy storage operation, and dynamic network reconfiguration. Finally, a hybrid SA-PSO algorithm combined with conic programming is employed to enhance computational efficiency while ensuring high solution quality for practical system scales. Case study analyses demonstrate that, compared to single-resource configurations, the proposed coordinated planning of multiple flexibility resources can significantly reduce the total system cost and markedly improve system resilience under fault conditions. Full article

In the modern era of smart grids, integrating advanced Geographic Information Systems (GISs) with protection coordination functionalities is pivotal for enhancing the reliability and efficiency of distribution networks. This paper presents an implementation of a web-based distribution network GIS platform that seamlessly integrates distribution system feeder GIS monitoring with the system model file layout, fault current analysis, and coordination simulation functions. The system can provide scalable and accessible solutions for power utilities, ensuring that protective devices operate in a coordinated manner to minimize outage impacts and improve service restoration times. The proposed GIS platform has demonstrated significant improvements in fault management and relay coordination through extensive simulation and field testing. This research advances the capabilities of distribution network management and sets a foundation for future enhancements in smart grid technology. Full article

Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction. Full article

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

Background and Objectives: Depression is a leading cause of disability globally, with treatment challenges including limited access, stigma, and poor adherence. Gamification, which applies game elements such as points, levels, and storytelling into non-game contexts, offers a promising strategy to enhance engagement and augment traditional treatments. Our research is the first study designed to explore the implementation of gamification within the Malaysian context. The objective was to explore the feasibility of implementation of gamification as an adjunctive treatment for adults with depression. Materials and Methods: Focus group discussions were held with five mental health professionals and ten patients diagnosed with moderate depression. The qualitative component assessed perceptions of gamified interventions, while quantitative measures evaluated participants’ depressive and anxiety symptomatology. Results: Three key themes were identified: (1) understanding of gamification as a treatment option, (2) factors influencing its acceptance, and (3) characteristics of a practical and feasible intervention. Clinicians saw potential in gamification to boost motivation, support psychoeducation, and encourage self-paced learning, but they expressed concerns about possible addiction, stigma, and the complexity of gameplay for some patients. Patients spoke of gaming as a source of comfort, escapism, and social connection. Acceptance was shaped by engaging storylines, intuitive design, balanced difficulty, therapist guidance, and clear safety measures. Both groups agreed that gamification should be used in conjunction with standard treatments, be culturally sensitive, and be presented as a meaningful therapeutic approach rather than merely as entertainment. Conclusions: Gamification emerges as an acceptable and feasible supplementary approach for managing depression in Malaysia. Its success depends on culturally sensitive design, robust clinical oversight, and seamless integration with existing care pathways. Future studies should investigate long-term outcomes and establish guidelines for the safe and effective implementation of this approach. We recommend targeted investment into culturally adapted gamified tools, including training, policy development, and collaboration with key stakeholders to realistically implement gamification as a mental health intervention in Malaysia. Full article

Xi’an, China, has a complex geological environment, with geological hazards seriously hindering urban development and safety. This study analyzed the conditions leading to disaster formation and screened 12 evaluation factors (e.g., slope and slope direction) using Spearman’s correlation. Furthermore, it also introduced an innovative combined weighting method, integrating subjective weights from the hierarchical analysis method and objective weights from the entropy method, as well as an information value model for susceptibility assessment. The main results are as follows: (1) There are 787 hazard points—landslides/collapses are concentrated in loess areas and Qinling foothills, while subsidence/fissures are concentrated in plains. (2) The combined weighting method effectively overcame the limitations of single methods. (3) Validation using hazard density and ROC curves showed that the combined weighting information value model achieved the highest accuracy (AUC = 0.872). (4) The model was applied to classify the disaster susceptibility of Xi’an into high (12.31%), medium (18.68%), low (7.88%), and non-susceptible (61.14%) zones. The results are consistent with the actual distribution of disasters, thus providing a scientific basis for disaster prevention. Full article

Triple-negative breast cancer (TNBC) is a clinically and molecularly heterogeneous subtype defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. In this study, tumor specimens from 104 TNBC patients were analyzed to characterize molecular and clinicopathological features and to assess the expression and therapeutic potential of four key surface markers: epidermal growth factor receptor (EGFR), epithelial cell adhesion molecule (EpCAM), tissue factor (TF), and trophoblast cell surface antigen (TROP2). Multiplex immunofluorescence (mIF) demonstrated elevated EGFR and TROP2 expression in the majority of samples. Significant positive correlations were observed between EGFR and TF, as well as between TROP2 and both TF and EpCAM. Expression analyses revealed increased EGFR and TF levels with advancing tumor stage, whereas EpCAM expression declined in advanced-stage tumors. TROP2 and TF expression were significantly elevated in higher-grade tumors. Additionally, EGFR and EpCAM levels were significantly higher in patients with elevated Ki-67 indices. Binding specificity assays using single-chain variable fragment (scFv-SNAP) fusion proteins confirmed robust targeting efficacy, particularly for EGFR and TROP2. These findings underscore the therapeutic relevance of EGFR and TROP2 as potential biomarkers and targets in TNBC. Full article