Search Results (26,391)

Botrytis cinerea is a highly versatile pathogenic fungus, causing significant damage across a wide range of plant species. A central focus of this review is the recent advances made through proteomics, an advanced molecular tool, in understanding the mechanisms of B. cinerea infection. Recent advances in mass spectrometry-based proteomics—including LC-MS/MS, iTRAQ, MALDI-TOF, and surface shaving—have enabled the in-depth characterization of B. cinerea subproteomes such as the secretome, surfactome, phosphoproteome, and extracellular vesicles, revealing condition-specific pathogenic mechanisms. Notably, in under a decade, the proportion of predicted proteins experimentally identified has increased from 10% to 52%, reflecting the rapid progress in proteomic capabilities. We explore how proteomic studies have significantly enhanced our knowledge of the fungus secretome and the role of extracellular vesicles (EVs), which play key roles in pathogenesis, by identifying secreted proteins—such as pH-responsive elements—that may serve as biomarkers and therapeutic targets. These technologies have also uncovered fine regulatory mechanisms across multiple levels of the fungal proteome, including post-translational modifications (PTMs), the phosphomembranome, and the surfactome, providing a more integrated view of its infection strategy. Moreover, proteomic approaches have contributed to a better understanding of host–pathogen interactions, including aspects of the plant’s defensive responses. Furthermore, this review discusses how proteomic data have helped to identify metabolic pathways affected by novel, more environmentally friendly antifungal compounds. A further update on the advances achieved in the field of proteomics discovery for the organism under consideration is provided in this paper, along with a perspective on emerging tools and future developments expected to accelerate research and improve targeted intervention strategies. Full article

Skin aging is closely related to mitochondrial dysfunction and cell cycle abnormalities, and developing intervention strategies targeting mitochondrial quality control is an important direction for anti-aging research. In this study, we investigated the anti-aging mechanism of Camellia japonica flower (CJF) extract and its active ingredient hyperoside based on a doxorubicin (DOX)-induced endogenous senescence model in human skin fibroblasts (HSFs). LC-MS proteomics analysis revealed that CJF extract and hyperoside specifically activated the FUNDC1-mediated mitochondrial autophagy pathway, significantly ameliorated the DOX-induced decrease in mitochondrial membrane potential and the accumulation of reactive oxygen species (ROS), and alleviated the cellular S-phase blockade and reversed the high expression of senescence-associated β-galactosidase (SA-β-gal). Further studies showed that the two cleared damaged mitochondria by enhancing mitochondrial autophagy and restoring cellular energy metabolism homeostasis while promoting type III collagen and elastin synthesis and repairing the expression of Claudin 1 related to skin barrier function. For the first time, the present study reveals the molecular mechanism of CJF extract in delaying skin aging by regulating the FUNDC1-dependent mitochondrial autophagy pathway, which provides a theoretical basis and a candidate strategy for developing novel anti-aging agents targeting mitochondrial quality control. Full article

Glioblastoma (GBM) is a highly aggressive brain tumor marked by invasive growth and therapy resistance. Tumor cells adapt to hostile conditions, such as hypoxia and nutrient deprivation, by activating survival mechanisms including autophagy and metabolic reprogramming. Among GBM-associated changes, hypersialylation, particularly, the aberrant expression of polysialic acid (PSA), has been linked to increased plasticity, motility, and immune evasion. PSA, a long α2,8-linked sialic acid polymer typically attached to the NCAM, is abundant in the embryonic brain and re-expressed in cancers, correlating with poor prognosis. Here, we investigated how PSA expression was regulated in GBM cells under nutrient-limiting conditions. Serum starvation induced a marked increase in PSA-NCAM, driven by upregulation of the polysialyltransferase ST8SiaIV and an autophagy-dependent recycling of sialic acids from degraded glycoproteins. Inhibition of autophagy or sialidases impaired PSA induction, and PSA regulation appeared dependent on p53 function. Immunohistochemical analysis of GBM tissues revealed co-localization of PSA and LC3, particularly around necrotic regions. In conclusion, we identified a novel mechanism by which GBM cells sustain PSA-NCAM expression via autophagy-mediated sialic acid recycling under nutrient stress. This pathway may enhance cell migration, immune escape, and stem-like properties, offering a potential therapeutic target in GBM. Full article

Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. Our system first employs a machine vision system to identify and locate mature cherries, distinguishing them from unripe fruits, leaves, and branches, which are treated as obstacles. Based on this visual data, the MSI-RRTCHP algorithm generates an optimal picking trajectory. Its core innovation is a synergistic strategy that enables intelligent navigation by combining probability-guided exploration, goal-oriented sampling, and adaptive step size adjustments based on the obstacle’s density. To optimize the picking sequence for multiple targets, we introduce an enhanced traversal algorithm ($\sigma$-TSP) that accounts for obstacle interference. Field experiments demonstrate that our integrated system achieved a 90% picking success rate. Compared with established algorithms, the MSI-RRTCHP algorithm reduced the path length by up to 25.47% and the planning time by up to 39.06%. This work provides a practical and efficient framework for robotic cherry harvesting, showcasing a significant step toward intelligent agricultural automation. Full article

Background/Objective: Staphylococcus aureus (S. aureus) is a clinically significant pathogenic bacterium. Daptomycin (DAP) is a cyclic lipopeptide antibiotic used to treat infections caused by multidrug-resistant Gram-positive bacteria, including S. aureus. However, DAP currently faces clinical limitations due to its short half-life, toxic side effects, and increasingly severe drug resistance issues. This study aimed to develop a biomimetic nano-drug delivery system to enhance targeting ability, prolong blood circulation, and mitigate resistance of DAP. Methods: DAP-loaded chitosan nanocomposite particles (DAP-CS) were prepared by electrostatic self-assembly. Macrophage membrane vesicles (MM) were prepared by fusion of M1-type macrophage membranes with 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). A biomimetic nano-drug delivery system (DAP-CS@MM) was constructed by the coextrusion process of DAP-CS and MM. Key physicochemical parameters, including particle diameter, zeta potential, encapsulation efficiency, and membrane protein retention, were systematically characterized. In vitro immune escape studies and in vivo zebrafish infection models were employed to assess the ability of immune escape and antibacterial performance, respectively. Results: The particle size of DAP-CS@MM was 110.9 ± 13.72 nm, with zeta potential +11.90 ± 1.90 mV, and encapsulation efficiency 70.43 ± 1.29%. DAP-CS@MM retained macrophage membrane proteins, including functional TLR2 receptors. In vitro immune escape assays, DAP-CS@MM demonstrated significantly enhanced immune escape compared with DAP-CS (p < 0.05). In the zebrafish infection model, DAP-CS@MM showed superior antibacterial efficacy over both DAP and DAP-CS (p < 0.05). Conclusions: The DAP-CS@MM biomimetic nano-drug delivery system exhibits excellent immune evasion and antibacterial performance, offering a novel strategy to overcome the clinical limitations of DAP. Full article

Antibiotic-resistant Pseudomonas aeruginosa presents a critical global health challenge, particularly in hospital-acquired infections. Bacteriophages offer a promising therapeutic avenue due to their ability to target and lyse resistant strains. This study characterizes Pseudomonas phage Banzai, a newly isolated Pbunavirus (family Lindbergviridae) with lytic activity against multiple P. aeruginosa isolates, including multidrug-resistant strains. Genomic analysis revealed a 66,189 bp genome, lacking antibiotic resistance or virulence factors, and suggested a headful packaging mechanism and the presence of a bidirectional component in the replication. In vivo experiments using Galleria mellonella showed therapeutic potential, significantly improving larval survival (87% at 24 h). Host range analysis revealed activity against 13 of 30 P. aeruginosa isolates, including members of O1, O3, O5 and O6 in silico predicted serogroups. Phylogenomic analyses place phage Banzai within the genus Pbunavirus, sharing 94.8% intergenomic similarity with its closest relatives, supporting its classification as a novel species. These findings highlight phage Banzai as a potential candidate for phage therapy, demonstrating genomic stability, a strictly lytic lifestyle, and in vivo efficacy. Full article

Background: Pulmonary fibrosis (PF), the end-stage manifestation of interstitial lung disease, is defined by excessive extracellular matrix deposition and alveolar destruction. Activated fibroblasts, the primary matrix producers, rely heavily on dysregulated glucose metabolism for their activation. While Salt Inducible Kinase 2 (SIK2) regulates glycolytic pathways in oncogenesis, its specific contributions to fibroblast activation and therapeutic potential in PF pathogenesis remain undefined. This study elucidates the functional role of SIK2 in PF and assesses its viability as a therapeutic target. Methods: SIK2 expression/localization in fibrosis was assessed by Western blot and immunofluorescence. Fibroblast-specific Sik2 KO mice evaluated effects on bleomycin-induced fibrosis. SIK2’s role in fibroblast activation and glucose metabolism impact (enzyme expression, metabolism assays, metabolites) were tested. SIK2 inhibitors were screened and evaluated therapeutically in fibrosis models. Results: It demonstrated significant SIK2 upregulation, specifically within activated fibroblasts of fibrotic lungs from both PF patients and murine models. Functional assays demonstrated that SIK2 is crucial for fibroblast activation, proliferation, and migration. Mechanistically, SIK2 enhances fibroblast glucose metabolism by increasing the expression of glycolysis-related enzymes. Additionally, this study demonstrated that the SIK2 inhibitor YKL06-061 effectively inhibited PF in both bleomycin and FITC-induced PF mouse models with the preliminary safety profile. Furthermore, we identified a novel therapeutic application for the clinically approved drug fostamatinib, demonstrating it inhibits fibroblast activation via SIK2 targeting and alleviates PF in mice. Conclusions: Our findings highlight SIK2 as a promising therapeutic target and provide compelling preclinical evidence for two distinct anti-fibrotic strategies with significant potential for future PF treatment. Full article

This study proposes an improved YOLO11n-seg instance segmentation model to address the limitations of existing models in accurately identifying mature blueberries in complex greenhouse environments. Current methods often lack sufficient accuracy when dealing with complex scenarios, such as fruit occlusion, lighting variations, and target overlap. To overcome these challenges, we developed a novel approach that integrates a Spatial–Channel Adaptive (SCA) attention mechanism and a Dual Attention Balancing (DAB) module. The SCA mechanism dynamically adjusts the receptive field through deformable convolutions and fuses multi-scale color features. This enhances the model’s ability to recognize occluded targets and improves its adaptability to variations in lighting. The DAB module combines channel–spatial attention and structural reparameterization techniques. This optimizes the YOLO11n structure and effectively suppresses background interference. Consequently, the model’s accuracy in recognizing fruit contours improves. Additionally, we introduce Normalized Wasserstein Distance (NWD) to replace the traditional intersection over union (IoU) metric and address bias issues that arise in dense small object matching. Experimental results demonstrate that the improved model significantly improves target detection accuracy, recall rate, and mAP@0.5, achieving increases of 1.8%, 1.5%, and 0.5%, respectively, over the baseline model. On our self-built greenhouse blueberry dataset, the mask segmentation accuracy, recall rate, and mAP@0.5 increased by 0.8%, 1.2%, and 0.1%, respectively. In tests across six complex scenarios, the improved model demonstrated greater robustness than mainstream models such as YOLOv8n-seg, YOLOv8n-seg-p6, and YOLOv9c-seg, especially in scenes with dense occlusions. The improvement in mAP@0.5 and F1 scores validates the effectiveness of combining attention mechanisms and multiple metric optimizations, for instance, segmentation tasks in complex agricultural scenes. Full article

Skeletal muscle atrophy is a critical health issue affecting the quality of life of elderly individuals and patients with chronic diseases. These conditions induce dysregulation of glucocorticoid (GC) secretion. GCs play a critical role in maintaining homeostasis in the stress response and glucose metabolism. However, prolonged exposure to GC is directly linked to muscle atrophy, which is characterized by a reduction in muscle size and weight, particularly affecting fast-twitch muscle fibers. The GC-activated glucocorticoid receptor (GR) decreases protein synthesis and facilitates protein breakdown. Numerous antagonists have been developed to mitigate GC-induced muscle atrophy, including 11β-HSD1 inhibitors and myostatin and activin receptor blockers. However, the clinical trial results have fallen short of the expected efficacy. Recently, several emerging pathways and targets have been identified. For instance, GC-induced sirtuin 6 isoform (SIRT6) expression suppresses AKT/mTORC1 signaling. Lysine-specific demethylase 1 (LSD1) cooperates with the GR for the transcription of atrogenes. The kynurenine pathway and indoleamine 2,3-dioxygenase 1 (IDO-1) also play crucial roles in protein synthesis and energy production in skeletal muscle. Therefore, a deeper understanding of the complexities of GR transactivation and transrepression will provide new strategies for the discovery of novel drugs to overcome the detrimental effects of GCs on muscle tissues. Full article

Accurate weld seam recognition is essential in automated welding systems, as it directly affects path planning and welding quality. With the rapid advancement of industrial vision, weld seam instance segmentation has emerged as a prominent research focus in both academia and industry. However, existing approaches still face significant challenges in boundary perception and structural representation. Due to the inherently elongated shapes, complex geometries, and blurred edges of weld seams, current segmentation models often struggle to maintain high accuracy in practical applications. To address this issue, a novel structure-aware and boundary-enhanced YOLO (SABE-YOLO) is proposed for weld seam instance segmentation. First, a Structure-Aware Fusion Module (SAFM) is designed to enhance structural feature representation through strip pooling attention and element-wise multiplicative fusion, targeting the difficulty in extracting elongated and complex features. Second, a C2f-based Boundary-Enhanced Aggregation Module (C2f-BEAM) is constructed to improve edge feature sensitivity by integrating multi-scale boundary detail extraction, feature aggregation, and attention mechanisms. Finally, the inner minimum point distance-based intersection over union (Inner-MPDIoU) is introduced to improve localization accuracy for weld seam regions. Experimental results on the self-built weld seam image dataset show that SABE-YOLO outperforms YOLOv8n-Seg by 3 percentage points in the AP(50–95) metric, reaching 46.3%. Meanwhile, it maintains a low computational cost (18.3 GFLOPs) and a small number of parameters (6.6M), while achieving an inference speed of 127 FPS, demonstrating a favorable trade-off between segmentation accuracy and computational efficiency. The proposed method provides an effective solution for high-precision visual perception of complex weld seam structures and demonstrates strong potential for industrial application. Full article

Arginine kinase is a key phosphagen kinase in invertebrates that facilitates rapid ATP regeneration by reversibly transferring phosphate groups between phosphoarginine and ADP. Structural studies have shown that the enzyme adopts distinct conformations in its ligand-free and ligand-bound states, known as the “open” and “closed” forms, respectively. These conformational changes are crucial for catalytic activity, enabling precise positioning of active-site residues and loop closure during phosphoryl transfer. Transition-state analog complexes have provided additional insights by mimicking intermediate states of catalysis, supporting the functional relevance of the open/closed structural model. Furthermore, studies across multiple species reveal how monomeric and dimeric forms of arginine kinase contribute to its allosteric regulation and substrate specificity. Beyond its metabolic role, arginine kinase is also recognized as a major allergen in crustaceans. Its structural uniqueness and absence in vertebrates make it a promising candidate for selective drug targeting. By integrating crystallographic data with functional context, this review highlights conserved features and species-specific variations of arginine kinase that may inform the design of inhibitors. Such molecules have the potential to serve both as antiparasitic agents and as novel therapeutics to manage crustacean-related allergic responses in humans. Full article

Introduction: Osteoarthritis (OA), a leading cause of disability among the elderly, is characterized by progressive joint tissue destruction. Fucoidan, a sulfated polysaccharide with known anti-inflammatory and antioxidant properties, has been investigated for its potential to protect against interleukin-1 beta (IL-1β)-induced articular tissue damage. Methods: Human primary chondrocytes and synovial fibroblasts were pre-treated with 100 μg/mL fucoidan before stimulation with 1 ng/mL of IL-1β. The protective effects of fucoidan were assessed by measuring oxidative stress markers and catabolic enzyme levels. These in vitro findings were corroborated using a rat anterior cruciate ligament transection-induced OA model. To explore the underlying mechanisms, particularly the interaction between microRNAs (miRs) and heme oxygenase-1 (HO-1), five candidate miRs were identified in silico and experimentally validated. Luciferase reporter assays were used to confirm direct interactions. Results: Fucoidan exhibited protective effects against IL-1β-induced oxidative stress and catabolic processes in both chondrocytes and synovial fibroblasts, consistent with in vivo observations. Fucoidan treatment restored HO-1 expression while reducing inducible nitric oxide synthase and matrix metalloproteinase levels in IL-1β-stimulated cells. Notably, this study revealed that fucoidan modulates the miR-22/HO-1 pathway, a previously uncharacterized mechanism in OA. Specifically, miR-22 was upregulated by IL-1β and subsequently attenuated by fucoidan. Luciferase reporter assays confirmed a direct interaction between miR-22 and HO-1. Conclusion: The results demonstrate that fucoidan mitigates OA-related oxidative stress in chondrocytes and synovial fibroblasts through the novel modulation of the miR-22/HO-1 axis. The miR-22/HO-1 pathway represents a crucial therapeutic target for OA, and fucoidan may offer a promising therapeutic intervention. Full article

Multiple sclerosis (MS) is a chronic, immune-mediated disorder of the central nervous system (CNS) characterized by inflammation, demyelination, axonal degeneration, and gliosis. Its pathophysiology involves a complex interplay of genetic susceptibility, environmental triggers, and immune dysregulation, ultimately leading to progressive neurodegeneration and functional decline. Although significant advances have been made in disease-modifying therapies (DMTs), many patients continue to experience disease progression and unmet therapeutic needs. Drug repurposing—the identification of new indications for existing drugs—has emerged as a promising strategy in MS research, offering a cost-effective and time-efficient alternative to traditional drug development. Several compounds originally developed for other diseases, including immunomodulatory, anti-inflammatory, and neuroprotective agents, are currently under investigation for their efficacy in MS. Repurposed agents, such as selective sphingosine-1-phosphate (S1P) receptor modulators, kinase inhibitors, and metabolic regulators, have demonstrated potential in promoting neuroprotection, modulating immune responses, and supporting remyelination in both preclinical and clinical settings. Simultaneously, artificial intelligence (AI) is transforming drug discovery and precision medicine in MS. Machine learning and deep learning models are being employed to analyze high-dimensional biomedical data, predict drug–target interactions, streamline drug repurposing workflows, and enhance therapeutic candidate selection. By integrating multiomics and neuroimaging data, AI tools facilitate the identification of novel targets and support patient stratification for individualized treatment. This review highlights recent advances in drug repurposing and discovery for MS, with a particular emphasis on the emerging role of AI in accelerating therapeutic innovation and optimizing treatment strategies. Full article

Reactive oxygen species (ROS) are often seen solely as harmful byproducts of oxidative metabolism, yet evidence reveals their paradoxical roles in both promoting and inhibiting cancer progression. Despite advances, precise context-dependent mechanisms by which ROS modulate oncogenic signaling, therapeutic response, and tumor microenvironment dynamics remain unclear. Specifically, the spatial and temporal aspects of ROS regulation (i.e., the distinct effects of mitochondrial versus cytosolic ROS on the PI3K/Akt and NF-κB pathways, and the differential cellular outcomes driven by acute versus chronic ROS exposure) have been underexplored. Additionally, the specific contributions of ROS-generating enzymes, like NOX isoforms and xanthine oxidase, to tumor microenvironment remodeling and immune modulation remain poorly understood. This review synthesizes current findings with a focus on these critical gaps, offering novel mechanistic insights into the dualistic nature of ROS in cancer biology. By systematically integrating data on ROS source-specific functions and redox-sensitive signaling pathways, the complex interplay between ROS concentration, localization, and persistence is elucidated, revealing how these factors dictate the paradoxical support of tumor progression or induction of cancer cell death. Particular attention is given to antioxidant mechanisms, including NRF2-mediated responses, that may undermine the efficacy of ROS-targeted therapies. Recent breakthroughs in redox biosensors (i.e., redox-sensitive fluorescent proteins, HyPer variants, and peroxiredoxin–FRET constructs) enable precise, real-time ROS imaging across subcellular compartments. Translational advances, including redox-modulating drugs and synthetic lethality strategies targeting glutathione or NADPH dependencies, further highlight actionable vulnerabilities. This refined understanding advances the field by highlighting context-specific vulnerabilities in tumor redox biology and guiding more precise therapeutic strategies. Continued research on redox-regulated signaling and its interplay with inflammation and therapy resistance is essential to unravel ROS dynamics in tumors and develop targeted, context-specific interventions harnessing their dual roles. Full article

Object detection in remote sensing imagery is critical in environmental monitoring, urban planning, and land resource management. However, the task remains challenging due to significant scale variations, arbitrary object orientations, and complex background clutter. To address these issues, we propose a novel orientation module (SOAM Block) that jointly models object scale and directional features while exploiting geometric symmetry inherent in many remote sensing targets. The SOAM Block is constructed upon a lightweight and efficient Adaptive Multi-Scale (AMS) Module, which utilizes a symmetric arrangement of parallel depth-wise convolutional branches with varied kernel sizes to extract fine-grained multi-scale features without dilation, thereby preserving local context and enhancing scale adaptability. In addition, a Strip-based Context Attention (SCA) mechanism is introduced to model long-range spatial dependencies, leveraging horizontal and vertical 1D strip convolutions in a directionally symmetric fashion. This design captures spatial correlations between distant regions and reinforces semantic consistency in cluttered scenes. Importantly, this work is the first to explicitly analyze the coupling between object scale and orientation in remote sensing imagery. The proposed method addresses the limitations of fixed receptive fields in capturing symmetric directional cues of large-scale objects. Extensive experiments are conducted on two widely used benchmarks—DOTA and HRSC2016—both of which exhibit significant scale variations and orientation diversity. Results demonstrate that our approach achieves superior detection accuracy with fewer parameters and lower computational overhead compared to state-of-the-art methods. The proposed SOAM Block thus offers a robust, scalable, and symmetry-aware solution for high-precision object detection in complex aerial scenes. Full article