Search Results (313)

Background/Objectives: Age-related macular degeneration (AMD) is the third leading cause of irreversible blindness in elderly individuals aged over 50 years old. Oxidative stress plays a crucial role in the etiopathogenesis of multifactorial AMD disease. The phospholipid bilayer EVs derived from the culture-conditioned medium of human induced pluripotent stem cell (hiPSC) differentiated retinal organoids aid in cell-to-cell communication, signaling, and extracellular matrix remodeling. The goal of the current study is to establish and evaluate the encapsulation of a hydrophobic compound, cannabidiol (CBD), into retinal organoid-derived extracellular vesicles (EVs) for potential therapeutic use in AMD. Methods: hiPSC-derived retinal organoid EVs were encapsulated with CBD via sonication (CBD-EVs), and structural features were elucidated using atomic force microscopy, nanoparticle tracking analysis, and small/microRNA (miRNA) sequencing. ARPE-19 cells and oxidative-stressed (H₂O₂) ARPE-19 cells treated with CBD-EVs were assessed for cytotoxicity, apoptosis (MTT assay), reactive oxygen species (DCFDA), and antioxidant proteins (immunohistochemistry and Western blot). Results: Distinct miRNA cargo were identified in early and late retinal organoid-derived EVs, implicating their roles in retinal development, differentiation, and functionality. The therapeutic effects of CBD-loaded EVs on oxidative-stressed ARPE-19 cells showed greater viability, decreased ROS production, downregulated expression of inflammation- and apoptosis-related proteins, and upregulated expression of antioxidants by Western blot and immunocytochemistry. Conclusions: miRNAs are both prognostic and predictive biomarkers and can be a target for developing therapy since they regulate RPE physiology and diseases. Our findings indicate that CBD-EVs could potentially alleviate the course of AMD by activating the targeted proteins linked to the adenosine monophosphate kinase (AMPK) pathway. Implicating the use of CBD-EVs represents a novel frontline to promote long-term abstinence from drugs and pharmacotherapy development in treating AMD. Full article

Extracellular vesicles (EVs) have emerged as promising biomarkers and therapeutic agents, yet their quantification remains technically challenging due to the limitations of conventional methods. Here, a low-cost, fluorescence-based, paper-strip immunoassay is presented for rapid and semi-quantitative estimation of EV concentration, inspired by pH strips. The assay utilizes nitrocellulose membranes functionalized with capture antibodies (anti-CD63, CD9, CD81) and fluorescent dye (ExoBrite™) for EV detection. Systematic optimization of assay parameters—including dye application sequence, incubation time, antibody configuration, and dye concentration—revealed that labeling EVs with dye and incubating on the nitrocellulose paper strips for 20 min yielded the strongest and most reproducible signal. A 200× dilution of ExoBrite™ dye was determined to provide the best balance between sensitivity and specificity. A standard curve generated through twofold serial dilution of EVs from ovarian cancer cell culture medium confirmed a positive, concentration-dependent fluorescence response, establishing a usable dynamic range. Compared to existing technologies, this platform enables fast, simple-to-implement EV quantification using minimal sample volume and equipment. The simplicity and scalability of the method offer strong potential for use in clinical diagnostics and EV research applications. Full article

Analysis of tumoral RNA from bone marrow (BM) biopsy is essential for diagnosing childhood B-cell acute lymphoblastic leukemia (B-ALL), risk stratification, and monitoring, by detecting fusions and gene expression patterns. However, frequent BM biopsies are invasive and traumatic for patients. Small extracellular vesicles (sEVs) circulating in blood contain a variety of biomolecules, including RNA, that may contribute to cancer progression, offering a promising source of non-invasive biomarkers from liquid biopsies. While most EV studies have focused on small RNAs like microRNAs (miRNAs), the role of longer RNA species, including messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), remains underexplored despite their demonstrated potential for risk-based patient stratification when starting from BM biopsies. We used immuno-purification to isolate sEVs from peripheral blood at diagnosis in B-ALL patients and cell model-based conditioned culture medium (CCM) with ETV6::RUNX1 and TCF3::PBX1 fusions. Using whole-transcriptome sequencing targeting transcripts over 200 nt and a novel data analysis pipeline, we identified 102 RNA transcripts (67 mRNAs, 16 lncRNAs, 10 circRNAs, 4 pseudogenes, and 5 others) in patient-derived sEVs. These transcripts could serve as biomarkers for two distinct molecular subgroups of B-ALL, each with different risk profiles at diagnosis. This is the first study characterizing the long transcriptome in blood-derived sEVs for childhood B-ALL, highlighting the potential use of circulating RNAs for improved risk-based stratification. Full article

Background: Breast cancer (BC) is the most common cancer among women worldwide; therefore, the efforts of many scientists are aimed at finding effective biomarkers for this disease. It is known that exosomes are nanosized extracellular vesicles (EVs) that are released from various cell types, including cancer cells. Exosomes are directly involved in governing the physiological and pathological processes of an organism through the horizontal transfer of functional molecules (proteins, microRNA, etc.) from producing to receiving cells. Since the diagnosis and treatment of BC have been improved substantially with exosomes, in this study, we isolated breast carcinoma cell-derived exosomes, primary endotheliocyte-derived exosomes, and blood exosomes from BC patients (BCPs) in the first stage of disease and investigated their proteomic profiles. Methods: Exosomes were isolated from the samples by ultrafiltration and ultracentrifugation, followed by mass spectrometric and bioinformatics analyses of the data. The exosomal nature of vesicles was verified using transmission electron microscopy and flow cytometry. Results: Exosome proteins secreted by MCF-7 and BT-474 cells were found to form two clusters, one of which enhanced the malignant potential of cancer cells, while the other coincided with a cluster of HUVEC-derived exosome proteins. Despite the different ensembles of proteins in exosomes from the MCF-7 and BT-474 lines, the relevant portions of these proteins are involved in similar biological pathways. Comparison analysis revealed that more BC-associated proteins were found in the exosomal fraction of blood from BCPs than in the exosomal fraction of conditioned medium from cells mimicking the corresponding cancer subtype (89% and 81% for luminal A BC and MCF-7 cells and 86% and 80% for triple-positive BC and BT-474 cells, respectively). Conclusions: Tumor-associated proteins should be sought not in exosomes secreted by cell lines but in the composition of blood exosomes from cancer patients, while the contribution of endotheliocyte exosomes to the total pool of blood exosomes can be neglected. Full article

We propose a novel mechanism for photon–dark photon mass-state oscillations mediated by gravitational separation during propagation through the interstellar medium. This phenomenon establishes a new avenue for the detection of dark matter. By analyzing gravitational lensing data from quasars, we investigate the sensitivity of this approach to dark photons. Our analysis demonstrates constraints of $\epsilon <{10}^{-2}$ in the dark photon mass range of $1.7\times {10}^{-14}$ eV to $5.4\times {10}^{-14}$ eV. Furthermore, we propose potential applications of this mechanism to astrophysical systems with strong gravitational fields, such as neutron stars and black hole accretion disks. Full article

With the growing global focus on environmental protection and carbon emissions, electric vehicles (EVs) are becoming increasingly popular. Permanent magnet synchronous motors (PMSMs) have emerged as a core component of the drive system due to their high-power density and compact design. The rotor temperature of PMSMs significantly affects their operating efficiency, management strategies, and lifespan. However, real-time monitoring and acquisition of rotor temperature are challenging due to cost and space limitations. Therefore, this study proposes a hybrid model named RIME-XGBoost, which integrates the RIME optimization algorithm with XGBoost, for the precise modeling and prediction of PMSM rotor temperature. RIME-XGBoost utilizes easily monitored dynamic parameters such as motor speed, torque, and currents and voltages in the d-q coordinate system as input features. It simultaneously optimizes three hyperparameters (number of trees, tree depth, and learning rate) to achieve high learning efficiency and good generalization performance. The experimental results show that, on both medium-scale datasets and small-sample datasets in high-temperature ranges, RIME-XGBoost outperforms existing methods such as SMA-RF, SO-BiGRU, and EO-SVR in terms of RMSE, MBE, R-squared, and Runtime. RIME-XGBoost effectively enhances the prediction accuracy and computational efficiency of rotor temperature. This study provides a new technical solution for temperature management in EVs and offers valuable insights for research in related fields. Full article

The global adoption of battery electric vehicles (EVs) and hybrid electric vehicles (HEVs) as a substitute for internal combustion engine cars (ICEs) in various nations offers a substantial opportunity to reduce carbon dioxide (CO₂) emissions from land transportation. EVs are fitted with an energy conversion system that efficiently converts stored energy into propulsion, referred to as “tank-to-wheel (TTW) conversion”. Battery-electric vehicles have a significant advantage in that their exhaust system does not produce any pollutants. This hypothesis is equally relevant to public transport. Despite their higher upfront cost, electric buses contribute significantly to environmental sustainability during their operation. This study aimed to evaluate the environmental sustainability of electric buses during their operational phase by utilizing the life cycle assessment (LCA) technique. This paper used the MATLAB R2021b code to ascertain the mean load of the buses during their operation. The energy consumption of battery electric and hybrid electric buses was evaluated using the WLTP Class 2 standard, which refers to vehicles with a power-to-mass ratio between 22 and 34 W/kg, overing four speed phases (low, medium, high, extra high) with speeds up to 131.3 km/h. The code was used to calculate the energy consumption levels for the complete test cycle. The code adopts an idealized rectangular blind box model, disregarding the intricate design of contemporary buses to streamline the computational procedure. Simulating realistic test periods of 1800 s resulted in an average consumption of 1.451 kWh per km for electric buses and an average of 25.3 L per 100 km for hybrid buses. Finally, through an examination of the structure of the Hungarian power system utilization, it was demonstrated that electrification is a more appropriate method for achieving the emission reduction goals during the utilization phase. Full article

Electric vehicles (EVs) play a crucial role in addressing the energy crisis and mitigating the greenhouse effect. Lithium-ion batteries are the primary energy storage medium for EVs due to their numerous advantages. State of health (SOH) is a critical parameter for managing the health of lithium-ion batteries, and accurate SOH estimation forms the foundation of battery management systems (BMS), ensuring the safe operation of EVs. Data-driven deep learning techniques are attracting significant attention because of their strong ability to model complex nonlinear relationships, which makes them highly suitable for SOH estimation in lithium-ion batteries. This paper provides a comprehensive introduction to the common deep learning techniques used for SOH estimation of lithium-ion batteries, with a focus on model architectures. It systematically reviews the application of various deep learning algorithms in SOH estimation in recent years. Building on this, the paper offers a detailed comparison of these deep learning methods and discusses the current challenges and future directions in this field, with the aim of providing an extensive review of the role of deep learning in SOH estimation. Full article

The presence of TGF-β in the tumor microenvironment of cervical cancer (CC) is important for tumor progression. In this study, we analyzed the effect of TGF-β on the expression of the ectonucleotidases CD39 and CD73, which are involved in the generation of adenosine (Ado), in CC cells and in extracellular vesicles (EVs) secreted by these cells. Treatment of HeLa and CaSki cells for 72 h with recombinant human TGF-β increased the expression of CD39 and CD73 by 20 and 30% and by 40 and 100%, respectively. The addition of SB505124, an inhibitor of the TGF-β1 receptor, or GW4869, an inhibitor of exosome formation and release, reduced the expression and release of both ectonucleotidases in CC cells. Furthermore, TGF-β promoted the secretion of medium-large EVs (>130 nm) in HeLa cells (HeLa + TGF-β/EVs) and CaSki cells (CaSki + TGF-β/EVs), which increased the expression of CD39 (>20%) and CD73 (>60%), and EVs obtained from cells treated with TGF-β had a greater capacity to generate Ado than did EVs obtained from cells cultured in the absence of this factor (HeLa/EVs and CaSki/EVs). These findings suggest that the production of TGF-β in the CC TME can promote neoplastic progression through the secretion of EVs enriched with CD39 and CD73. Therefore, the inhibition of CD39+ CD73+ EVs could be a strategy for the treatment of CC. Full article

Lithium-ion batteries are one of the critical components in electric vehicles (EVs) and play an important role in green energy transportation. In this paper, lithium-ion batteries are reviewed from the perspective of battery materials, the characteristics of lithium-ion batteries with different cathode and anode mediums, and their commercial values in the field of electric vehicles. Representative products, including blade battery and Tesla 4680 cells, are inspected. Moreover, the results of commercial application of lithium-ion batteries in electric vehicles are summarized. Furthermore, cutting-edge technologies of lithium-ion batteries are discussed, including electrolyte technology, high-energy-density in situ polymerization technology, and pouch batteries. Finally, the latest EV battery technology development is looked over, including challenges and future development directions. Full article

Ovarian cancer (OC) is characterized by high mortality rates due to late diagnosis, recurrence, and metastasis. Here, we show that extracellular signaling molecules secreted by adipose-derived mesenchymal stem cells (ASCs) and OC cells—either in the conditioned medium (CM) or within small extracellular vesicles (sEVs)—modulate cellular responses and drive OC progression. ASC-derived sEVs and CM secretome promoted OC cell colony formation, invasion, and migration while upregulating tumor-associated signaling pathways, including TGFβ/Smad, p38MAPK/ERK1/2, Wnt/β-catenin, and MMP-9. Additionally, OC-derived sEVs and CM induced a pro-tumorigenic phenotype in ASCs, enhancing their invasiveness and expression of tumor-associated factors. Notably, both ASCs and OC cells exhibited increased expression of E-cadherin and Snail/Slug proteins, key markers of epithelial/mesenchymal hybrid phenotype, enhancing cellular plasticity and metastatic potential. We also demonstrated that these cellular features are, at least in part, due to the presence of tumor-supportive molecules such as TNF-α, Tenascin-C, MMP-2, and SDF-1α in the CM secretome of ASCs and OC cells. In silico analyses linked these molecular changes to poor prognostic outcomes in OC patients. These findings highlight the critical role of sEVs and tumor/stem cell-derived secretome in OC progression through bidirectional interactions that impact cellular behavior and phenotypic transitions. We suggest that targeting EV-mediated communication could improve therapeutic strategies and patient outcomes. Full article

This paper aims to provide small- and medium-sized enterprises (SMEs) owned by families with a simple, achievable technical methodology for the assessment of sustainable mobility alternatives, in particular, the purchase of electric vehicles (EVs) and photovoltaic (PV) systems. By adopting a comprehensive comparative analysis approach, this research aims to empower SMEs to make highly informed decisions concerning the choice of vehicles and energy systems that provide strategic and sustainable value. Based on a quantitative analysis linked to the total costs over ten years, and considering the different types of vehicles (electric, hybrid, and combustion) and the integration of PV systems, practical formulas are used to calculate the total cost of ownership (TCO), energy consumption, and CO₂ emissions. The results show that adopting electric vehicles, especially those complemented by photovoltaic systems with storage for night-time charging, can significantly reduce operating costs and carbon emissions, generating economic and environmental value. This study provides an accessible and applicable approach to the context of family SMEs, facilitating the analysis and choice of mobility options based on simple and commercially available data. By focusing on value creation through informed and strategic decisions, this work offers a relevant contribution to the competitiveness and sustainability of SMEs, promoting the adoption of sustainable mobility technologies in an integrated and effective manner. Full article

A micro electric vehicle (micro-EV) is a small electric car with one or two seats designed for short-to-medium-distance trips. Micro-EVs produce relatively less pollution during operation and, due to their compact size, offer greater mobility in narrow areas compared to conventional transportation. These advantages have led to a continuous increase in the number of micro-EVs. However, their small battery capacity results in a limited driving range per charge, and restrictions on power and speed lead to lower driving performance. Due to these drawbacks, micro-EVs still hold a small share of the overall vehicle market. Therefore, it is necessary to evaluate the strengths of micro-EVs and analyze how they should be utilized to promote their widespread adoption. Therefore, this study analyzed the strengths of micro-EVs and identified the types of services where they can be effectively utilized to promote the use of micro-EVs as a smart mobility option. This study focused on micro-EVs used as a shared transport service, delivery service, and in public service, as part of an R&D project on micro-EVs conducted by the Ministry of Trade, Industry, and Energy. A total of 106 micro-EVs were deployed for each service type: 57 for shared transport, 13 for delivery, and 36 for public service. Each micro-EV was equipped with a GPS device, and the analysis was conducted using GPS data collected from January 2021 to October 2021. Micro-EVs with missing data due to GPS device malfunctions were excluded from the analysis. As a result, two micro-EVs from the shared transport service and one from the public service were excluded. The study compared the travel characteristics of micro-EVs across the three different service types. Additionally, a comparative analysis of the driving characteristics of micro-EVs and conventional vehicles was conducted to assess the advantages of micro-EVs over traditional vehicles. The results of the analyses showed that micro-EVs were more utilized for the delivery service type than other service types in terms of daily usage time and travel distance (3.5 h/day and 38.5 km/day, respectively), trip amounts (24.1 trips/day), and number of trips per trip chain (9.4 trips/trip chain). Moreover, micro-EVs have their strengths compared to other modes of transportation when traveling narrow roads. Analysis of the roads around the areas where micro-EVs were located showed that the micro-EVs were exposed to narrow roads with a width of under 5 m (among the total road link extensions, 57% consisted of road links with a width of less than 5 m), especially the micro-EVs used for delivery service. It is expected that the findings of this study will serve as a foundational resource for developing strategies to promote the adoption of micro electric vehicles. Full article

The increasing presence of various organics poses significant threats to aquatic ecosystems and living organisms. Conventional water treatment methods are often insufficient, necessitating the development of powerful and sustainable alternatives. This study addresses this challenge by exploring the synthesis of ZnO nanoparticles using green tea leaves extract—an eco-friendly approach—for the sunlight-activated removal of organics in agri-food wastewater. The research examined different conditions for the removal of clomazone (CLO), tembotrione (TEM), ciprofloxacin (CIP), and zearalenone (ZEA). Nitrate-derived ZnO synthesized in a water medium (N-gZnO_w) exhibited the highest photocatalytic activity, removing 98.2, 95.8, 96.2, and 96.6% of CLO, TEM, CIP, and ZEA. Characterization techniques (XRD, Raman spectroscopy, SEM, zeta potential measurements, UV–visible spectroscopy) confirmed the synthesis of N-gZnO_w, with an average particle size of 14.9 nm, an isoelectric point of 9.9, and a band gap energy of 2.92 eV. Photocatalytic experiments identified 0.5 mg/cm³ as an optimal catalyst loading, while a higher initial pollutant concentration reduced degradation efficiency. LC-ESI-MS/MS measurements confirmed the efficient pollutant degradation and the formation of degradation intermediates. Hence, this study demonstrates that green tea extract-synthesized ZnO nanoparticles offer a promising, sustainable solution for removing herbicides, pharmaceuticals, and mycotoxins from wastewater, paving the way for eco-friendly water purification technologies. Full article

Bentonite-supported TiO₂ (Montmorillonite (MMT)-TiO₂) and Cu₃TiO₅ oxides (MMT-Cu₃TiO₅) nanomaterials were synthesized via a facile and sustainable sol–gel synthesis approach. The XRD results indicate the presence of mixed phases, namely, TiO₂ anatase and a new semiconductor, Cu₃TiO₅, in the material. The specific surface area (S_BET) exhibits a notable increase with the incorporation of TiO₂ and Cu₃TiO₅, rising from 85 m²/g for pure montmorillonite to 245 m²/g for MMT-TiO₂ and 279 m²/g for MMT-Cu₃TiO₅. The lower gap energy of MMT-Cu₃TiO₅ (2.15 eV) in comparison to MMT-TiO₂ (2.7 eV) indicates that MMT-Cu₃TiO₅ is capable of more efficient absorption of visible light with longer wavelengths. The immobilization of TiO₂ and Cu₃TiO₅ on bentonite not only enhances the textural properties of the samples but also augments their visible light absorption capabilities, rendering them potentially more efficacious for adsorption and photocatalytic applications. The photocatalytic efficacy of both MMT-TiO₂ and MMT-Cu₃TiO₅ was evaluated through the monitoring of the degradation of Orange G, an anionic azo dye. The MMT-Cu₃TiO₅ photocatalyst was observed to induce complete degradation (100%) of the Orange G dye in 120 min when tested in an optimized reaction medium with a pH of 3 and a catalyst concentration of 2 g/L. MMT-Cu₃TiO₅ was demonstrated to be an exceptionally effective catalyst for the degradation of Orange G. Following the synthesis of the catalyst, it can be simply washed with the same recovered solution and reused multiple times for the photocatalytic process without the need for any chemical additives. Full article