Search Results (2,664)

Ischemia–reperfusion (I/R) injury is a complex pathological process underlying numerous acute organ failures and is a significant cause of morbidity and mortality in diseases such as myocardial infarction, stroke, thrombosis, and organ transplantation. Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) have demonstrated considerable therapeutic potential, but their broad tropism and general repair signaling may limit their efficacy. This review addresses the emerging paradigm of using organ-specific EVs for the treatment of I/R injury in the respective organs. We summarize the existing studies performed on experimental animals showing that these native EVs could possess tissue tropism and carry a specialized cargo of proteins, miRNAs, and lipids tailored to the unique regenerative needs of their organ of origin, enabling them to precisely modulate key processes, including inflammation, apoptosis, oxidative stress, and angiogenesis. However, their clinical translation faces challenges related to scalable production, standardization, and the dualistic nature of their effects, which can be either protective or detrimental, depending on the cellular source and pathophysiological context. Future developments need to focus on overcoming these obstacles through rigorous isolation protocols, engineering strategies such as cargo enrichment and hybrid vesicle creation, and validation in large-animal models. Overall, organ-specific EVs offer a novel, cell-free therapeutic strategy with the potential to significantly improve outcomes in I/R injury. Full article

Currently, the global construction industry is facing challenges of stagnant efficiency and cost overruns. The potential of modular construction has not been fully unleashed due to the disconnection between design and manufacturing. This paper proposes a P-DFMA (Platform-Design for Manufacture and Assembly) building product platform architecture for modular apartments. The research establishes a three-level standardization framework of “modular unit-component-connector”, covering core residential modules, light steel keel systems, and high-strength bolt joints. Finite element simulation using SimSolid is employed to ensure manufacturing feasibility, and a standardized component library and a full-process collaborative P-DFMA architecture for modular apartments are developed. Verified through the case of the modular dormitory building project at Tianjin Chengjian University, the results show that compared with the traditional prefabricated construction mode, the P-DFMA platform mode achieves a cost savings rate of 54.8% in project design, production, and cross-link collaboration. This proves the feasibility and architectural advantages of the platform in improving the full-process efficiency and optimizing costs of modular buildings. Full article

Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen frequently associated with delayed wound healing, particularly in chronic skin injuries. Its capability to form biofilms, secrete virulence factors, and the faculty to compete with other microorganisms makes it a major challenge in clinical wound management. Recent literature reveals different molecular and cellular mechanisms through which P. aeruginosa disrupts the wound healing process. Findings highlight that it interferes with key phases of healing by modulating host immune responses, degrading extracellular matrix components, and inhibiting keratinocyte migration. Its quorum-sensing systems regulate the expression of critical virulence factors such as exotoxin A, elastases, pyocyanin, and rhamnolipids. Additionally, the production of the biofilm matrix components alginate, and polysaccharides provide protection against host defenses and antibiotics. Interactions with other microorganisms, including antagonistic effects on Staphylococcus epidermidis and synergistic relationships with Staphylococcus aureus, modify the wound microbiota. Promising therapeutic alternatives have shown efficacy in disrupting biofilms and reducing virulence. These insights remark the importance of targeting both P. aeruginosa and its ecological interactions to enhance wound healing outcomes and develop more effective treatments. This review aimed to highlight the pathogenic role of P. aeruginosa and its interactions with other microbial species in the context of skin wound healing. Full article

Soil organic matter (SOM) is essential for ecosystem health and agricultural productivity. Accurate prediction of SOM content is critical for modern agricultural management and sustainable soil use. Existing digital soil mapping (DSM) models, when processing temporal data, primarily focus on modeling the changes in input data across successive time steps. However, they do not adequately model the relationships among different input variables, which hinders the capture of complex data patterns and limits the accuracy of predictions. To address this problem, this paper proposes a novel deep learning model, 2-Channel Network (2C-Net), leveraging sequential multi-temporal remote sensing images to improve SOM prediction. The network separates input data into temporal and spatial data, processing them through independent temporal and spatial channels. Temporal data includes multi-temporal Sentinel-2 spectral reflectance, while spatial data consists of environmental covariates including climate and topography. The Multi-sequence Feature Fusion Module (MFFM) is proposed to globally model spectral data across multiple bands and time steps, and the Diverse Convolutional Architecture (DCA) extracts spatial features from environmental data. Experimental results show that 2C-Net outperforms the baseline model (CNN-LSTM) and mainstream machine learning model for DSM, with R² = 0.524, RMSE = 0.884 (%), MAE = 0.581 (%), and MSE = 0.781 (%)². Furthermore, this study demonstrates the significant importance of sequential spectral data for the inversion of SOM content and concludes the following: for the SOM inversion task, the bare soil period after tilling is a more important time window than other bare soil periods. 2C-Net model effectively captures spatiotemporal features, offering high-accuracy SOM predictions and supporting future DSM and soil management. Full article

The growing use of experimental radiopharmaceuticals for targeted radionuclide therapy (TRT) highlights the need for robust “in house” radiolabeling protocols. Among these, PSMA-ALB-56 is a PSMA ligand incorporating an albumin-binding moiety to enhance pharmacokinetics, which showed promise for prostate cancer treatment. This study investigated manual radiolabeling conditions of this vector molecule with lutetium-177 and developed a corresponding automated synthesis protocol. Manual experiments on low activities explored buffer systems and antioxidants, identifying sodium acetate buffer and L-methionine as optimal, achieving radiochemical purities above 97% with excellent stability over 48 h. However, when these conditions were transposed directly to an automated process on a GAIA^® module with activities > 2 GBq, radiochemical purity dropped below 70% due to significant radiolysis. This result emphasized that conditions optimized at low activities are not directly transferable to high-activity automated production, and highlighted the crucial role of antioxidant concentration. An optimized automated method was subsequently developed, integrating a solid-phase extraction purification step, higher antioxidant levels during radiolabeling and formulation, and a larger final product volume. These changes led to radiochemical purities above 98.9% and excellent product stability over 120 h for 3 test batches. The presence of high concentrations of methionine and ascorbic acid was essential to protect against radiolysis. This work underscores the importance of adjusting radiolabeling strategies during process scale-up and confirmed that antioxidant concentration is essential for successful ¹⁷⁷Lu radiolabeling. The optimized automated method developed here for [¹⁷⁷Lu]Lu-PSMA-ALB-56 may also be adapted to other radiopharmaceuticals in development for TRT. Full article

The production of fuels and materials from residual biomass through sustainable processes is one of the most challenging goals for science and technology. Such development is highly ambitious because of the complex composition of the feedstock (lipidic and lignocellulosic). To succeed, biomass conversion technologies must be able to compete economically with technologies based on fossil carbon. The use of specific and more available catalysts combined with improved reaction conditions can significantly reduce overall industrial costs and maximize efficiency. The synthesis and application of optimized catalytic systems are essential to modulate their activity, ensuring at the same time a high resistance to deactivation. For this reason, the study of multifunctional systems is gaining increasing interest alongside new industrial technologies. Here, we review significant recent advances in sustainable catalytic biomass conversion using emerging heterogeneous catalysts. Full article

Lattice-based cryptography (LBC) is an essential direction in the fields of homomorphic encryption (HE), zero-knowledge proofs (ZK), and post-quantum cryptography (PQC), while number theoretic transformations (NTT) are a performance bottleneck that affects the promotion and deployment of LBC applications. Field-programmable gate arrays (FPGAs) are an ideal platform for accelerating NTT due to their reconfigurability and parallel capabilities. High-level synthesis (HLS) can shorten the FPGA development cycle, but for algorithms such as NTT, the synthesizer struggles to handle the inherent memory dependencies, often resulting in suboptimal synthesis outcomes for direct designs. This paper proposes a systematic HLS co-design to progressively guide the synthesis of NTT accelerators. The approach integrates several key techniques: arithmetic module resource optimization, conflict-free butterfly scheduling, memory partitioning, and template-based automated design fusion. It reveals how to resolve pipeline bottlenecks in HLS-based designs and expand parallel processing, guiding microarchitecture iterations to achieve an efficient design space. Compared to existing HLS-based designs, the area-latency product achieves a performance improvement of 1.93 to 191 times, and compared to existing HDL-based designs, the area-cycle product achieves a performance improvement of 1.7 to 10.6 times. Full article

Plants have evolved a complex, multilayered immune system that integrates molecular recognition, signaling pathways, epigenetic regulation, and small RNA-mediated control. Recent studies have shown that DNA-level regulatory mechanisms, such as RNA-directed DNA methylation (RdDM), histone modifications, and chromatin remodeling, are critical for modulating immune gene expression, allowing for rapid and accurate pathogen-defense responses. The epigenetic landscape not only maintains immunological homeostasis but also promotes stress-responsive transcription via stable chromatin modifications. These changes contribute to immunological priming, a process in which earlier exposure to pathogens or abiotic stress causes a heightened state of preparedness for future encounters. Small RNAs, including siRNAs, miRNAs, and phasiRNAs, are essential for gene silencing before and after transcription, fine-tuning immune responses, and inhibiting negative regulators. These RNA molecules interact closely with chromatin features, influencing histone acetylation/methylation (e.g., H3K4me3, H3K27me3) and guiding DNA methylation patterns. Epigenetically encoded immune memory can be stable across multiple generations, resulting in the transgenerational inheritance of stress resilience. Such memory effects have been observed in rice, tomato, maize, and Arabidopsis. This review summarizes new findings on short RNA biology, chromatin-level immunological control, and epigenetic memory in plant defense. Emerging technologies, such as ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing), ChIP-seq (Chromatin Immunoprecipitation followed by Sequencing), bisulfite sequencing, and CRISPR/dCas9-based epigenome editing, are helping researchers comprehend these pathways. These developments hold an opportunity for establishing epigenetic breeding strategies that target the production of non-GMO, stress-resistant crops for sustainable agriculture. Full article

Aroma and flavor are central to consumer perception, product acceptance, and market positioning of animal-derived foods such as meat, milk, and eggs. These sensory traits arise from volatile organic compounds (VOCs) formed via lipid oxidation (e.g., hexanal, nonanal), Maillard/Strecker chemistry (e.g., pyrazines, furans), thiamine degradation (e.g., 2-methyl-3-furanthiol, thiazoles), and microbial metabolism, and are modulated by species, diet, husbandry, and post-harvest processing. Despite extensive research on food volatiles, there is still no unified framework spanning meat, milk, and eggs that connects production factors with VOC pathways and links them to sensory traits and consumer behavior. This review explores how production systems, feeding strategies, and processing shape VOC profiles, creating distinct aroma “fingerprints” in meat, milk, and eggs, and assesses their value as markers of quality, authenticity, and traceability. We have also summarized the advances in analytical techniques for aroma fingerprinting, with emphasis on GC–MS, GC–IMS, and electronic-nose approaches, and discuss links between key VOCs and sensory patterns (e.g., grassy, nutty, buttery, rancid) that influence consumer perception and willingness-to-pay. These patterns reflect differences in production and processing and can support regulatory claims, provenance verification, and label integrity. In practice, such markers can help producers tailor feeding and processing for flavor outcomes, assist regulators in verifying claims such as “organic” or “free-range,” and enable consumers to make informed choices. Integrating VOC profiling with production data and chemometric/machine learning pipelines can enable robust traceability tools and sensory-driven product differentiation, supporting transparent, value-added livestock products. Thus, this review integrates production variables, biochemical pathways, and analytical platforms to outline a research agenda toward standardized, transferable VOC-based tools for authentication and label integrity. Full article

Detecting surface cracks on sugarcane is a critical step in ensuring product quality control, with detection precision directly impacting raw material screening efficiency and economic benefits in the sugar industry. Traditional methods face three core challenges: (1) complex background interference complicates texture feature extraction; (2) variable crack scales limit models’ cross-scale feature generalization capabilities; and (3) high computational complexity hinders deployment on edge devices. To address these issues, this study proposes a lightweight sugarcane surface crack detection model, SCS-YOLO (Surface Cracks on Sugarcane-YOLO), based on the YOLOv10 architecture. This model incorporates three key technical innovations. First, the designed RFAC2f module (Receptive-Field Attentive CSP Bottleneck with Dual Convolution) significantly enhances feature representation capabilities in complex backgrounds through dynamic receptive field modeling and multi-branch feature processing/fusion mechanisms. Second, the proposed DSA module (Dynamic SimAM Attention) achieves adaptive spatial optimization of cross-layer crack features by integrating dynamic weight allocation strategies with parameter-free spatial attention mechanisms. Finally, the DyHead detection head employs a dynamic feature optimization mechanism to reduce parameter count and computational complexity. Experiments demonstrate that on the Sugarcane Crack Dataset v3.1, compared to the baseline model YOLOv10, our model achieves mAP50:95 to 71.8% (up 2.1%). Simultaneously, it achieves significant reductions in parameter count (down 19.67%) and computational load (down 11.76%), while boosting FPS to 122 to meet real-time detection requirements. Considering the multiple dimensions of precision indicators, complexity indicators, and FPS comprehensively, the SCS—YOLO detection framework proposed in this study provides a feasible technical reference for the intelligent detection of sugarcane quality in the raw materials of the sugar industry. Full article

Background: Inappropriate antibiotic prescribing by dental practitioners is a significant problem in low- and middle-income settings, such as India, where there are no guidelines for dental prescribing. This study aims to report, in a step-by-step process, the co-development of a computer-based stewardship educational intervention with Indian stakeholders to reduce inappropriate antibiotic prescribing by primary care dental practitioners in India. Methods: The development process of our intervention was guided by the Medical Research Council framework for developing and evaluating complex interventions. In alignment with the framework’s core elements, a co-production research approach was employed. Engagement with local stakeholders, including primary care dental practitioners, academic dentists, and those from the Indian Dental Association, facilitated the development of a contextually appropriate intervention that was informed by a prior needs assessment (a systematic review and a policy document analysis conducted in India) and evidence from global literature. The intervention was refined through iterative feedback from stakeholders and pre-testing. Results: An educational antibiotic stewardship intervention was co-developed in collaboration with stakeholders from Chennai, a major city in southern India. The final intervention comprised three components: 1. A one-page chairside guide summarising common areas of dental antibiotic use for easy reference in clinical settings; 2. A training module based on the chairside guide; and 3. A patient information sheet to facilitate dentists’ communication with patients. The intervention components were designed to be clear, practical, and contextually relevant, with the potential to enhance clinical decision-making and promote evidence-based antibiotic prescribing practices. Conclusions: This research paper describes, in a structured manner, how an educational antibiotic stewardship intervention for dental practitioners in India was co-developed by researchers and local stakeholders. Further feasibility testing is required to address uncertainties identified at the conclusion of the development process, including those related to dentists’ perceptions of the intervention, the utility of the intervention tools, and prescription recording. Full article

Background/Objectives: Fibroblast activation protein (FAP) is highly expressed in tumor stroma and selected inflammatory conditions, offering a promising target for molecular imaging. [⁶⁸Ga]Ga-FAPI-46 is a DOTA-based FAP inhibitor with excellent tumor-to-background ratio and potential advantages over [¹⁸F]FDG in low-glycolytic tumors. This article aims to highlight the quality elements that are relevant to clinical practice and to describe the development of an investigational medicinal product dossier for a bicentric clinical trial involving [⁶⁸Ga]Ga-FAPI-46. Methods: The radiolabeling was performed by the two facilities using different automated synthesizers, but with the same specifications and acceptance criteria Results: Three validation batches per site were analyzed for radiochemical/radionuclidic purity, pH, endotoxin, sterility, and bioburden according to European Pharmacopoeia standards. Stability was as sessed up to 2 h post-synthesis. All batches met predefined acceptance criteria. Conclusions: Despite differences in radiosynthesizer modules, product quality and process reproducibility were maintained across both centers. [⁶⁸Ga]Ga-FAPI-46 can be reliably produced in academic settings under GMP-like conditions, enabling multicenter clinical research and facilitating IMPD submissions for broader adoption of FAP-targeted PET imaging. Full article

Neutrophils are among the first cells to be recruited to the lungs during Chlamydia pneumoniae infection in mouse models; however, their regulatory functions are not yet fully understood. This study examined the mechanisms and significance of IL-10-producing neutrophils throughout C. pneumoniae pulmonary infection in C57BL/6 mice. Our findings revealed that infection with C. pneumoniae induces IL-10 secretion in bone marrow-derived neutrophils, depending on Toll-like receptor 2 (TLR2) activation. This process involves TLR2-dependent mitochondrial reactive oxygen species (ROS) production, which triggers the endoplasmic reticulum (ER) stress pathway, including IRE1α and subsequent Xbp1 splicing. Inhibition of this pathway or depletion of neutrophils (using the 1A8 monoclonal antibody) significantly reduces IL-10 levels in bronchoalveolar lavage fluid (BALF) in vivo. Conversely, the absence of IL-10-producing neutrophils, whether through depletion or TLR2 deficiency, leads to increased IL-12p70 and IFN-γ-positive NK cells, along with decreased regulatory T cells and M2-like macrophages. This results in a lower bacterial burden in the lungs but causes more severe pulmonary damage and decreased survival rates. These findings highlight that IL-10 produced by neutrophils via the TLR2-mitochondrial ROS–ER stress pathway is essential for modulating pulmonary immune responses and maintaining immune homeostasis during C. pneumoniae infection, thereby preventing excessive inflammation and tissue damage. Full article

Pea protein yogurt (PPY), as an alternative to traditional dairy yoghurt, has the advantages of being a green raw material, lactose cholesterol-free, and adaptable to the needs of lactose-intolerant people. PPY was prepared by fermenting a mixture of pea protein and water (1:10, w/v) supplemented with 5% fructose for 10 h after heat sterilisation. During fermentation, lactic acid bacteria metabolise pea protein to produce aldehydes and other aromatic compounds, imparting a unique sweet–sour balance and mellow flavour. However, issues such as weak gel formation and prominent soybean-like off-flavours severely restrict the development and consumer acceptance of PPY. In this study, five fermentation systems were systematically investigated to elucidate the fermentation mechanisms of pea yoghurt and explore effective methods for eliminating undesirable soy flavours. The results indicated that hydrophobic interactions and disulfide bonds are the predominant forces driving gel formation in PPY. Additionally, the protein content increased by 0.81 g/100 g following fermentation. A total of 43 volatile flavour compounds—including aldehydes, alcohols, acids, ketones, and furans—were identified, among which the concentrations of hexanal and 2-pentylfuran, known markers for soybean off-flavour, significantly decreased. Furthermore, high-temperature and high-pressure treatments (121 °C, 3 min) demonstrated superior effectiveness in reducing soybean-like flavours. Although the high-temperature and high-pressure treatment, double-enzyme hydrolysis, and flavour-masking methods operate through distinct mechanisms, their flavour profiles converged, displaying substantial deodorisation effects and synergistic interactions. These findings provide a theoretical basis and processing parameters for flavour modulation in PPY; however, further formulation optimisation is required to enhance its nutritional and textural properties. PPY shows promise as a potential alternative to conventional dairy products in the future. Full article

Photocatalytic nitrogen fixation under ambient conditions offers a sustainable alternative to the energy-intensive Haber–Bosch process, yet remains limited by the inertness of N≡N bonds and sluggish multi-electron/proton transfer kinetics. Nature’s nitrogenase enzymes, featuring the FeMo cofactor and ATP-driven electron cascades, inspire a new generation of artificial systems capable of mimicking their catalytic precision and selectivity. This review systematically summarizes recent advances in bio-inspired photocatalytic nitrogen reduction, focusing on six key strategies derived from enzymatic mechanisms: Fe–Mo–S active site reconstruction, hierarchical electron relay pathways, ATP-mimicking energy modules, defect-induced microenvironments, interfacial charge modulation, and spatial confinement engineering. While notable progress has been made in enhancing activity and selectivity, challenges remain in dynamic regulation, mechanistic elucidation, and system-level integration. Future efforts should prioritize operando characterization, adaptive interface design, and device-compatible catalyst platforms. By abstracting nature’s catalytic logic into synthetic architectures, biomimetic photocatalysis holds great promise for scalable, green ammonia production aligned with global decarbonization goals. Full article