Search Results (703)

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, while ultra-high-energy cosmic rays (UHECRs; $E\gtrsim {10}^{18}$ eV) remain among the most important unresolved problems in astroparticle physics. This review examines the viability of FRBs and their central engines as sources of UHECRs within a comprehensive multi-messenger framework. We summarize the observational constraints on UHECR source populations imposed by the energy spectrum, nuclear composition, anisotropy measurements, diffuse $\gamma$-ray background, and high-energy neutrino observations, which, together, favor source classes capable of accelerating heavy nuclei with hard injection spectra, modest cosmological evolution, and sufficiently high source densities. We then review the current landscape of FRB progenitor and engine models, including magnetars, supramassive neutron stars, compact-object mergers, and accretion-powered systems, emphasizing their energetics, environments, and particle-acceleration capabilities through relativistic shocks, magnetic reconnection, magnetar wind nebulae, and direct electromagnetic acceleration by ultra-relativistic FRB pulses. We discuss how these scenarios are constrained by neutrino and $\gamma$-ray observations from IceCube, KM3NeT, and Fermi-LAT, as well as by large-scale UHECR anisotropy measurements from the Pierre Auger Observatory and Telescope Array. Finally, we examine the observational tests that will become possible in the coming decade through large samples of localized FRBs, composition-resolved UHECR measurements, next-generation neutrino observatories, and wide-field $\gamma$-ray facilities. We emphasize that FRB dispersion and rotation measures provide unique probes of the baryonic and magnetic environments relevant for UHECR acceleration and propagation, enabling a new form of multi-messenger tomography of cosmic-ray source environments and allowing the FRB–UHECR connection to become a quantitatively testable astrophysical framework. Full article

Real-time scheduling in large-scale electric vehicle charging stations is challenged by stochastic vehicle arrivals, dynamic departures, limited charging resources, and station-level power constraints. To address this problem, this paper proposes a two-stage Supervised Service Matching and Reinforcement Power Dispatch (SMPD) framework, termed SMPD, which decomposes the original coupled scheduling problem into supervised service matching and reinforcement learning-based power dispatch. In the first stage, a supervised matching network learns EV-charger service suitability from historical charging-session records and determines service access decisions for feasible EV–charger pairs. In the second stage, a Soft Actor-Critic-based controller allocates continuous charging power to connected EVs under EV-side charging limits, charger capacity constraints, and the station-level total power constraint. The proposed framework is evaluated using public charging-session data from the ElaadNL dataset. Experimental results show that SMPD achieves lower average waiting time, higher average revenue, lower composite penalty, and comparable demand satisfaction compared with rule-based, single-stage reinforcement learning, and multi-agent baselines. Sensitivity and robustness analyses further indicate that SMPD maintains favorable scheduling performance and acceptable online decision time under the tested charger-scale settings and operational disturbance scenarios. These results suggest that the proposed two-stage design provides an effective and computationally tractable approach for real-time scheduling in large-scale EV charging stations. Full article

High-sensitivity detection of formaldehyde is critically important for environmental protection and public health. Zinc oxide (ZnO) is a widely used core material for chemiresistive gas sensors; however, its conventional low-index facets suffer from a limited number of active sites, creating a bottleneck for further sensitivity enhancement. To overcome this limitation, this study pioneers the application of the highly reactive ZnO $\left[20\overline{2}1\right]$ high-index polar surface for formaldehyde detection. By leveraging its unique stepped atomic configuration and unprecedented density of coordination-unsaturated active sites, we systematically investigate the formaldehyde adsorption behavior and the underlying sensing mechanism using first-principles calculations based on density functional theory (DFT). The pristine ZnO $\left[20\overline{2}1\right]$ surface exhibits intrinsic metallic character. At a coverage of 1 monolayer (ML), the most stable G1 configuration achieves an adsorption energy of −1.54 eV per CH₂O molecule. Within a 2 × 1 supercell, formaldehyde adopts both associative and dissociative adsorption modes. At a lower coverage, formaldehyde forms a stable bidentate structure through dual C–O and Zn–O bonding interactions. Electronic structure analysis reveals significant orbital hybridization and interfacial charge redistribution upon adsorption. Notably, associative adsorption opens a bandgap of 0.04 eV at the Fermi level, inducing a metal-to-semiconductor transition. In contrast, dissociative adsorption results in pronounced n-type doping, thereby elucidating the microscopic origin of the resistivity decrease observed in ZnO-based sensors. Overall, this work highlights the structural advantages of high-index facets and demonstrates for the first time the superior formaldehyde adsorption capability of the ZnO $\left[20\overline{2}1\right]$ facet, providing robust theoretical guidance for the rational design of next-generation, high-performance gas-sensing materials. Full article

Extracellular vesicles (EVs) are key mediators of intercellular communication across biological kingdoms, with central roles in immune regulation and disease processes. Despite shared structural features, EVs derived from bacteria, plants, and mammalian cells differ substantially in their biogenesis, molecular composition, and immunological functions. EV formation pathways generate vesicles with distinct cargo profiles, including pathogen-associated molecular patterns (PAMPs) in bacterial EVs, regulatory small RNAs in plant-derived vesicles, and cytokines, microRNAs, and antigen-presenting complexes in mammalian EVs. Differences in cargo result in divergent immune outcomes. Bacterial EVs predominantly activate innate immunity via pattern recognition receptors such as Toll-like receptors, whereas plant-derived EVs exhibit low immunogenicity and mediate cross-kingdom RNA interference. In contrast, mammalian EVs primarily regulate immune responses by modulating antigen presentation and cytokine signaling. These findings support a framework in which EV origin determines immunological function and therapeutic applicability. This perspective highlights the importance of selecting appropriate EV sources for vaccine development, regenerative medicine, and targeted delivery strategies, while addressing current challenges related to heterogeneity, standardization, and safety. Full article

Extracellular vesicles (EVs), particularly small extracellular vesicles (sEVs), are lipid bilayer-delimited particles involved in intercellular communication through the transfer of proteins, lipids, and nucleic acids; many products and studies in aesthetic medicine refer to these preparations as exosomes, although endosomal origin is not always demonstrated. This review examines current evidence on the mechanisms, clinical effectiveness, safety, therapeutic applications, and regulatory challenges of EV- and sEV-based interventions, complemented by an exploratory qualitative assessment of physicians’ perceptions regarding clinical implementation. A narrative review of studies indexed in Scopus and PubMed was conducted with emphasis on skin rejuvenation, hair restoration, wound healing, pigmentation disorders, and inflammatory dermatoses, and responses from 12 aesthetic physicians in Colombia were analyzed qualitatively. Available evidence suggests that EVs/sEVs may promote extracellular matrix remodeling, angiogenesis, immunomodulation, and tissue repair, with potential benefits across several aesthetic and regenerative indications. However, the literature remains heterogeneous and limited by variability in biologic sources, isolation and administration protocols, insufficient high-quality clinical trials, and unresolved regulatory issues. Reports of adverse reactions linked to unapproved products marketed as exosome-based formulations further highlight the need for stronger oversight. EVs, particularly sEVs, often referred to as exosomes in the aesthetic literature, remain a promising therapeutic platform, but safe clinical integration requires rigorous validation, technical standardization, and robust regulatory frameworks. Full article

Traumatic brain injury (TBI) is a global public health problem which causes long-term neurologic damage caused by both primary mechanical injury and secondary pathological processes. Extracellular vesicles (EVs) such as exosomes, microvesicles (MVs) and apoptotic bodies (ApoBDs) serve as critical vehicles mediating intercellular communication in the central nervous system (CNS) following TBI. The biogenesis and the content of EVs, including proteins, lipids and RNAs, are greatly changed and involved in the evolution of inflammation or tissue repairing after TBI. In this overview, we recapitulate the cellular origin of EVs and the function of EVs in the neuroinflammatory process after TBI, highlighting the dual regulatory roles of EVs in the biological response to TBI, whereby certain EV populations amplify secondary injury cascades, while others promote endogenous repair and recovery processes. We next investigate the progress in EV engineering and targeted delivery systems and report the potential mechanisms, emphasize the prospects and potential of engineered EVs for therapy, and comment on challenges and perspectives for clinical application in TBI. Full article

Astrocytes are increasingly implicated in the pathophysiology of schizophrenia (SCZ), yet how astrocytic dysfunction contributes to disease-relevant neuronal abnormalities remains unclear. Here, we used mass spectrometry–based proteomics to profile lysates (proteome) and secreted proteins (secretome) from iPSC-derived astrocytes originating from 9 SCZ patients and 8 healthy controls. Compartment-specific analyses showed that lysates were enriched for mitochondrial and nuclear pathways, whereas astrocyte-conditioned media (ACM) were enriched for extracellular matrix (ECM) and vesicle-associated proteins. Differential expression analysis revealed minimal overlap between dysregulated proteins in lysates and ACM, suggesting modality-specific effects of SCZ-associated donor background. Interestingly, ECM proteins and key secreted cues involved in synaptic development, including MFGE8 and SEMA3C, were selectively reduced in SCZ ACM, whereas RNA-processing proteins were aberrantly increased. This is in line with previously reported microRNA enrichment in extracellular vesicles (EV) derived from SCZ patients. Gene set analyses further identified the alteration in secretion and nuclear processes as well as the potential involvement of autophagy-dependent release mechanism in SCZ astrocytes. Together, these findings suggest disrupted astrocytic protein homeostasis and extracellular signalling in SCZ iPSC-derived astrocytes, providing mechanistic insight into astrocyte-mediated contributions to synaptic and circuit deficits in the disorder. Full article

Extracellular vesicles (EVs) have emerged as key mediators of intercellular communication in the brain, with glial cell-derived EVs increasingly recognized for their roles in maintaining brain homeostasis and contributing to the progression of neurodegenerative diseases. By transferring a diverse cargo of bioactive molecules, including proteins, RNAs, and organelles, EVs influence recipient cell behavior and overall brain function. In neurodegenerative conditions, glial EVs can either propagate pathogenic signals or deliver neuroprotective and regenerative cues, depending on their cellular origin and molecular composition. This context-dependent heterogeneity highlights the need for physiologically relevant human models to investigate EVs biology. Human induced pluripotent stem cell (iPSC)-derived glial models provide a disease-relevant platform, as they recapitulate key pathological features of Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). When further integrated with brain organoid platforms, these iPSC-based systems enable the generation of three-dimensional environments that closely resemble in vivo EVs dynamics. Importantly, glial EVs can modulate cellular pathways involved in neuronal survival and function. Indeed, their potential to interact with and, under specific experimental conditions, traverse the blood–brain barrier (BBB) has contributed to growing interest in their application for biomarker discovery and therapeutic development. Engineered and patient-specific EVs derived from iPSCs are emerging as promising tools for targeted, cell type-specific, therapeutic approaches, although their clinical applicability still requires further validation. This review discusses the emerging evidence supporting the dual role of iPSC-derived glial EVs in health and disease, underscores the translational potential of iPSC-based platforms for mechanistic studies, and outlines their promise as precision medicine tools for diagnostics and therapy. Full article

Background/Objectives: African Swine Fever (ASF) represents one of the most serious threats to animal health and global food security. The causative agent of ASF is the African swine fever virus (ASFV), a DNA virus belonging to the Asfarviridae family. Here, we describe ex vivo results for an original anti-ASFV vaccine approach based on the cellular immune response induced by extracellular vesicles (EVs) engineered to express four ASFV proteins. EV engineering was achieved by expressing a DNA vector encoding a biologically inactive HIV-1 Nef protein (Nef^mut), which exhibits unusually high efficiency of incorporation into EVs, even when fused to foreign proteins. Previous studies have demonstrated that intramuscular injection of Nef^mut-based vectors leads to the engineering of Evs, spontaneously released by muscle cells, and induction of antigen-specific CD8⁺ T cell immunity. Methods: We designed DNA vectors expressing the fusion products between Nef^mut and each of the four ASFV structural proteins p30, p54, pp62, and p72. Engineered EVs were molecularly characterized by Western blot and nanotrack analysis, and their potential immunogenicity was assessed by priming and cross-presentation assays. Results: We assessed that the four fusion proteins were successfully expressed in transfected mammalian cells, with the release of valuable amounts of engineered EVs. When immature swine dendritic cells were challenged with the engineered EVs and then co-cultivated with autologous peripheral blood lymphocytes in priming assays, lymphocyte subpopulations specifically reacting against each ASFV antigen were elicited, as detected by an IFN-γ ELISpot assay. In addition, we provide evidence that the Nef^mut-based fusion products incorporated into the engineered EVs can be cross-presented by professional antigen-presenting cells, leading to cross-priming of autologous lymphocytes. Conclusions: These results represent the best premise to go forward with experiments examining immunogenicity and antiviral efficiency in pigs. Full article

Accurate forecasting of urban electric vehicle (EV) charging demand is important for power system operation, sustainable transport electrification, and charging infrastructure planning. However, this task remains challenging because EV charging demand is shaped by temporal usage patterns as well as changing relationships among weather conditions, operational factors, and historical charging behavior. Many existing forecasting models treat these explanatory variables mainly as parallel inputs, while their mutual relationships are often predefined, simplified, or left implicit in the temporal learning process. To support AI-driven charging demand management, this study proposes an adaptive graph-based recurrent network (A-GRN) for city-level aggregated EV charging-load forecasting. In the proposed framework, key explanatory variables are represented as feature nodes, and their connections are learned through an adaptive adjacency matrix rather than a fixed spatial topology. The adaptive graph neural network (AGN) module captures feature-level interactions, while a dual-path gated recurrent unit module (DG-GRU) extracts temporal representations from the charging-load sequence. Experiments on a city-level EV charging dataset show that A-GRN outperforms several baseline models, including naive persistence forecasting, GRU, LSTM, BiGRU, TCN, and GCN. Compared with the BiGRU baseline, A-GRN reduces MAE, MSE, and RMSE by 31.36%, 34.65%, and 20.48%, respectively. In the original physical unit, the MAE is reduced from 187.43 kWh to 128.64 kWh, and the RMSE is reduced from 222.69 kWh to 177.08 kWh. The results indicate that feature-level graph learning can improve short-term EV charging-load forecasting, especially when the target is an aggregated urban load rather than the load of a single charging station. The proposed model provides a data-driven forecasting tool for sustainable urban charging demand management, low-carbon transport operation, and charging infrastructure planning. Full article

Early cancer detection using minimally invasive biomarkers remains a significant challenge, particularly in early-stage disease, where circulating tumor DNA is often below the limit of detection. Extracellular vesicles (EVs), which are actively secreted by viable cancer cells and carry tumor-associated proteins, represent a promising alternative target for liquid biopsy. In this study, we developed EV-finder^®, a conceptual framework for the direct detection of EV-associated proteins in serum using proximity extension assay (PEA) technology. Unlike conventional EV-based analytical methods that require prior EV isolation or enrichment, the EV-finder approach enables direct profiling of EV-associated proteins from small serum volumes without an EV isolation step, thereby simplifying the analytical workflow while preserving EV-derived molecular information. Using serum samples from patients with five cancer types (n = 193) and independent healthy controls (n = 138), we established a two-step supervised machine learning framework for cancer detection and tissue-of-origin prediction. The screening model demonstrated promising discriminative performance, with an AUC of 0.985, sensitivity of 0.929, and specificity of 0.957. Notably, no false positives were observed in an external Japanese control cohort, whereas 4 of 29 Korean control samples were classified as cancer-positive. Analysis of EV-associated protein profiles identified both pan-cancer and cancer-type-specific signatures, supporting their value for multi-cancer detection. Collectively, these findings demonstrate the potential feasibility of direct detection of EV-associated proteins from serum using PEA technology and highlight its potential as a scalable and minimally invasive strategy for multi-cancer screening. Full article

Extracellular vesicles (EVs) have attracted growing attention for their diagnostic and prognostic potential as they carry molecular cargo such as DNA, RNA, proteins and lipids derived from their cells of origin. While EV research has traditionally focused on blood, this study explicitly explored saliva as a promising, non-invasive sample matrix for EV isolation and biomarker discovery. Six different EV isolation methods were compared for their ability to recover salivary small EVs suitable for downstream DNA and microRNA analysis. Nanoparticle tracking analysis (NTA) revealed variation in vesicle sizes, concentrations and surface charges across all tested EV isolation approaches. In addition to being the fastest and simplest isolation method, the miRCURY Exosome Isolation kit—serum and plasma from Qiagen (ExiQ) also resulted in the highest EV yields with average particle sizes of ~130 nm. Western blot analysis further verified the presence of EV-specific markers (CD9, Alix) and no detectable signal for ApoA1 as an indicator for lipoprotein contamination, underscoring the purity of ExiQ-isolated vesicles. Always applying the same protocol for parallel DNA and RNA isolation on vesicles extracted by various methods, differences in DNA and RNA yields were observed across the evaluated isolation kits. ExiQ-isolated EVs showed the best recovery for both nucleic acid types. Notably, nuclease treatment of isolated EVs revealed that substantial amounts of DNA were present on the EV surface, whereas microRNA was predominantly localized within the vesicles. The present study, extensively comparing different EV isolation methods, demonstrates that salivary EVs are a viable source for non-invasive diagnostics and suggests the miRCURY Exosome Isolation kit—serum and plasma from Qiagen (ExiQ) to be a good choice for integration in future salivary EV-based diagnostic assays given its simplicity, speed and excellent performance. Full article

Precision battery-health monitoring and rapid degradation detection are essential for improving the security, durability, and efficacy of electric vehicles (EVs). By incorporating short-term State-of-Health (SoH) forecasting, mid-term deterioration alarms, and degradation acceleration risk modeling into a temporally consistent machine learning architecture, this research suggests a hierarchical predictive-maintenance framework. The rolling-origin cross-validation approach is implemented to maintain the chronological order of the data and prevent any potential information leaks. The predictive core employs an ensemble learning approach that integrates Random Forest, Extremely Randomized Trees, and Histogram-Based Gradient Boosting. Validation-driven model blending and training only feature selection are implemented to improve generalizability. The one-hour SoH forecasting model for short-horizon monitoring exhibits exceptional accuracy in an assessment of health prediction, with an R² of 0.9254, an RMSE of 0.0033, and a MAPE of 0.32%. Early detection of anomalies and the provision of a seven-day degradation warning may be achieved by a proactive maintenance scheduling model with an area under the curve (AUC) of 0.7838 and a recall of 0.8205. In addition, the degradation acceleration risk module could identify rapid health decline with a robustness of 0.8796 and a precision–recall AUC of 0.7101 when operating under significant stress. Reliability in critical domains is demonstrated through validation using scenarios that simulate severe temperature and stress conditions. Achieving intelligent predictive maintenance of electric vehicle battery packs is now feasible due to the proposed multi-layer ensemble structure. Full article

Tin disulfide (SnS₂) has attracted extensive research attention due to its superior properties originating from its unique crystalline structure. However, its practical applications are greatly restricted by difficult morphology regulation and insufficient photoresponse capability. Herein, we successfully synthesized copper and carbon co-doped SnS₂ (Cu-C-SnS₂) nanosheets via a carbon dot-assisted hydrothermal method. The morphology, crystal structure and chemical composition of the obtained samples were characterized by FE-SEM, XRD and XPS. The experimental results reveal that the synthesized Cu-C-SnS₂ presents nanosheet morphology with a bandgap of approximately 2.445 eV. Moreover, carbon dots and copper doping can effectively regulate the morphology of SnS₂, which provides a reliable strategy for the controllable synthesis of SnS₂ nanosheets. Meanwhile, the photoelectric device based on the as-fabricated Cu-C-SnS₂ nanosheets were successfully fabricated, and exhibited favorable photoelectric response under 405 nm light irradiation. Full article

Cognitive impairment (CI) is prevalent among heart failure (HF) patients. Although the brain injury in HF is multifactorial, oxidative stress and neuroinflammation are common pathological features of neurological disorders and are increasingly recognized as key mechanisms underlying CI. Extracellular vesicles (EVs) are well-established mediators of biological signaling in myocardial function and are widely recognized for transporting a variety of microRNAs. However, whether myocardial injury alters the miRNA profiles of brain EVs, potentially contributing to cognitive impairment (CI) by disrupting brain homeostasis, remains poorly understood. Using a rodent myocardial infarction (MI) model, we isolated brain EVs and characterized their miRNA profiling by means of small RNA sequencing. Our results demonstrate that miRNA profiles in brain EVs vary with HF progression. Only three miRNAs were significantly changed at 3 weeks post-MI, whereas thirty-two miRNAs and sixty-five miRNAs demonstrated significant changes post-MI, showed significant alterations at 6 and 12 weeks post-MI, respectively. Bioinformatic analysis suggests that some miRNAs against oxidative stress and inflammation were downregulated in brain EVs at 6 and 12 weeks post-MI. Conversely, several miRNAs responsible for oxidative stress and neuroinflammation were significantly increased, which may be of cardiac origin following MI. Collectively, these findings suggest that cardiac EVs may contribute to miRNA alterations in brain EVs, potentially driving CI by disrupting brain homeostasis. Full article