Hydrogen−rich water (HRW) is widely recognized for its growth promoting, antioxidant, and anti−inflammatory properties. However, little is known about the role of HRW in aquaculture. This study aims to investigate how different concentrations of HRW affect the growth performance, digestive ability, antioxidant capacity, mTOR signaling pathway, and gut microbiota of juvenile largemouth bass. We randomly assigned 360 fish (13.73 ± 0.1 g) to three treatments. The control group was maintained in regular water, while the treatment groups were treated with different concentrations of H₂ dissolved in water, which were H1 (179.65 ± 31.95 ppb) and H2 (280.65 ± 64.43 ppb), respectively. Through an analysis of the three treatments, it was found that H1 significantly increased the final body weight, weight gain rate, specific growth rate, and survival rate, and reduced the feed conversion ratio (p < 0.05). In addition, the trypsin activity was significantly increased in the intestine (p < 0.05), and the expression of genes related to the glucose metabolism (pk and pepck) and mTOR (tor, akt, s6k1, 4ebp1, and ampka) signaling pathways were significantly increased in the liver in H1 (p < 0.05). The relative abundance of Blautia in the gut microbiota (p < 0.05) was significantly increased in H1. Therefore, these results indicated that H1 can significantly improve growth performance, promote intestinal digestion, activate the glucose metabolism pathway and mTOR signaling pathway, and increase the abundance of beneficial bacteria in the gut of largemouth bass. These findings provided valuable support for the application of HRW to support the healthy aquaculture of largemouth bass. Full article

Obtaining mathematical models of nonlinear cyber–physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed. Full article

Light-induced phase segregation, particularly when incorporating bromine to widen the bandgap, presents significant challenges to the stability and commercialization of perovskite solar cells. This study explores the influence of hole transport layers, specifically poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine (PTAA) and [4-(3,6-dimethyl-9H-carbazol-9-yl)butyl]phosphonic acid (Me-4PACz), on the dynamics of phase segregation. Through detailed characterization of the buried interface, we demonstrate that Me-4PACz enhances perovskite photostability, surpassing the performance of PTAA. Nanoscale analyses using in situ Kelvin probe force microscopy and quantitative nanomechanical mapping techniques elucidate defect distribution at the buried interface during phase segregation, highlighting the critical role of substrate wettability in perovskite growth and interface integrity. The integration of these characterization techniques provides a thorough understanding of the impact of the buried bottom interface on perovskite growth and phase segregation. Full article

Four complexes of essential metal ions, Cu(II) and Ni(II), with the new sulfonamide ligand N-(pyridin-2-ylmethyl)quinoline-8-sulfonamide (HQSMP) were synthesized and physicochemically and structurally characterized. Complex [Cu(QSMP)Cl]_n (2) consists of a polymeric chain formed by distorted square pyramidal units. In two, the sulfonamide ligand acts as a bridge coordinating to one Cu(II) through its three N atoms and to another metal ion via one O atom in the sulfonamido group, while the pentacoordinate complex [Cu(QSMP)(C₆H₅COO)] (3) presents a highly distorted square pyramidal geometry. Complex [Ni(QSMP)(C₆H₅COO)(CH₃OH)][Ni(QSMP)(CH₃COO)(CH₃OH)] (4) consists of two mononuclear entities containing different anion coligands, either a benzoate or an acetate group. Both units exhibit a distorted octahedral geometry. The interaction of the complexes with CT-DNA was studied by means of UV-Vis and fluorescence spectroscopy, interestingly revealing that the Ni(II) complex presents the highest affinity towards the nucleic acid. Complexes 1 and 2 are able to cleave DNA. Both compounds show promising nuclease activity at relatively low concentrations by mediating the production of a reactive oxygen species (ROS). The interaction of the four complexes with bovine serum albumin (BSA) was also investigated, showing that the compounds can bind to serum proteins. The antitumor potential of complexes 1 and 2 was evaluated against the A549 lung adenocarcinoma cell line, revealing cytotoxic properties that were both dose- and time-dependent. Full article

Despite all the literature on building energy management, building-stock-scale models depicting its impact for energy-market-scale optimisation models are lacking. To address this shortcoming, an open-source tool called ArchetypeBuildingModel.jl has been developed for aggregating building-stock-level data into simplified lumped-capacitance thermal models compatible with existing open-source energy-system modelling frameworks. This paper aims to demonstrate the feasibility of these simplified thermal models by comparing their performance against dedicated building simulation software, as well as examining their sensitivity to key modelling and parameter assumptions. Modelling and parameter assumptions comparable to the existing literature achieved an acceptable performance according to ASHRAE Guideline 14 across all tested buildings and nodal configurations. The most robust performance was achieved with a period of variations above 13 days and interior node depth between 0.1 and 0.2 for structural thermal mass calibrations, and with external shading coefficients between 0.6 and 1.0 and solar heat gain convective fractions between 0.4 and 0.6 for solar heat gain calibrations. Furthermore, three-plus-node lumped-capacitance thermal models are recommended when modelling buildings with structures varying in terms of thermal mass. Nevertheless, the ArchetypeBuildingModel.jl performance was found to be robust against uncertain key parameter assumptions, making it plausible for energy-market-scale applications. Full article

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (M^pro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a K_i value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds. Full article

Improving fracture toughness, electrical conductivity, and biocompatibility has consistently presented challenges in the development of artificial bone replacement materials. This paper presents a new strategy for creating high-performance, multifunctional composite ceramic materials by doping graphene oxide (GO), which is known to induce osteoblast differentiation and enhance cell adhesion and proliferation into barium calcium zirconate titanate (BCZT) ceramics that already exhibit good mechanical properties, piezoelectric effects, and low cytotoxicity. Using fast hot-pressed sintering under vacuum conditions, (1 − x)(Ba_0.85Ca_0.15Zr_0.1Ti_0.9)O₃₋xGO (0.2 mol% ≤ x ≤ 0.5 mol%) composite piezoelectric ceramics were successfully synthesized. Experimental results revealed that these composite ceramics exhibited high piezoelectric properties (d₃₃ = 18 pC/N, k_p = 62%) and microhardness (173.76 HV_0.5), meeting the standards for artificial bone substitutes. Furthermore, the incorporation of graphene oxide significantly reduced the water contact angle and enhanced their wettability. Cell viability tests using Cell Counting Kit-8, alkaline phosphatase staining, and DAPI staining demonstrated that the GO/BCZT composite ceramics were non-cytotoxic and effectively promoted cell proliferation and growth, indicating excellent biocompatibility. Consequently, with their superior mechanical properties, piezoelectric performance, and biocompatibility, GO/BCZT composite ceramics show extensive potential for application in bone defect repair. Full article

We aimed to characterize SARS-CoV-2 infection in companion animals living in households with COVID-19-positive people and understand the dynamics surrounding how these animals become infected. Public health investigators contacted households with at least one confirmed, symptomatic person with COVID-19 for study recruitment. Blood, nasal, and rectal swab specimens were collected from pet dogs and cats and a questionnaire was completed. Specimens were tested for SARS-CoV-2 by RT-PCR, and for neutralizing antibodies; genomic sequencing was performed on viral-positive samples. A total of 36.4% of 110 pets enrolled had evidence of infection with SARS-CoV-2. Pets were more likely to test positive if the pet was immunocompromised, and if more than one person in the home was positive for COVID-19. Among 12 multi-pet households where at least one pet was positive, 10 had at least one other pet test positive. Whole-genome sequencing revealed the genomes of viral lineages circulating in the community during the time of sample collection. Our findings suggest a high likelihood of viral transmission in households with multiple pets and when pets had very close interactions with symptomatic humans. Further surveillance studies are needed to characterize how new variants impact animals and to understand opportunities for infection and spillover in susceptible species. Full article

This study addresses the current lack of research on the effectiveness assessment of Artificial Intelligence (AI) technology in architectural education. Our aim is to evaluate the impact of AI-assisted architectural teaching on student learning. To achieve this, we developed an AI-embedded teaching model. A total of 24 students from different countries participated in this 9-week course, completing a comprehensive analysis of architectural programming and design using AI technologies. This study conducted questionnaire surveys with students at both midterm and final stages of the course, followed by structured interviews after the course completion, to explore the effectiveness and application status of the teaching model. The results indicate that the AI-embedded teaching model positively and effectively influenced student learning. The “innovative capability” and “work efficiency” of AI technologies were identified as key factors affecting the effectiveness of the teaching model. Furthermore, the study revealed a close integration of AI technologies with architectural programming but identified challenges in the uncontrollable expression of architectural design outcomes. Student utilization of AI technologies appeared fragmented, lacking a systematic approach. Lastly, the study provides targeted optimization suggestions based on the current application status of AI technologies among students. This research offers theoretical and practical support for the further integration of AI technologies in architectural education. Full article

To understand the spatial temporal distribution characteristics of Illex argentinus caught by trawl fishing vessels in the Southwestern Atlantic Ocean and their relationship with key marine environmental factors, this study analyzed the temporal and spatial changes in the fishing ground center of trawl vessels at the ten-day scale from December 2019 to May 2022, combining Chinese trawl fishing log data marine environmental data with satellite remote sensing marine environmental data. Utilizing the Maxent model, ten-day intervals were used as the temporal scale, and ten marine environmental factors, including sea surface temperature, sea surface height, sea surface salinity, chlorophyll concentration, temperature at 50 m and 100 m depth, and the meridional and zonal velocities of ocean currents were quantitatively analyzed to explore the correlation between the spatial distribution of catch and environmental factors. The study reveals that the trawl fishing grounds for Illex argentinus are divided into southern and northern grounds. The southern grounds first appear near 45°20′ S in December, gradually moving southeastward in February and March. The northern grounds do not appear until April, near 42° S in the high seas. On the ten-day time scale, the central fishing grounds of Illex argentinus show significant spatial variability but minor interannual differences. The Maxent model results indicate that sea surface temperature and chlorophyll a concentration are the key environmental factors influencing the spatial and temporal variability of the high seas trawl fishing grounds for most of the time, with high environmental contribution rates during the fishing season. While the range of suitable habitats with an HSI > 0.6 identified by the Maxent model varies significantly between years, a pattern is observed where the range expands at the start and end of the fishing season and contracts during the peak fishing season. This suggests that a more concentrated range of suitable habitats is conducive to accurate predictions of trawl fishing grounds, enabling efficient fishing operations. Full article

Glioblastoma is a highly aggressive neoplasm and the most common primary malignant brain tumor. Endothelial tissue plays a critical role in glioblastoma growth and progression, facilitating angiogenesis, cellular communication, and tumorigenesis. In this review, we present an up-to-date and comprehensive summary of the role of endothelial cells in glioblastomas, along with an overview of recent developments in glioblastoma therapies and tumor endothelial marker identification. Full article

Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives. Full article

Compressors are an essential component of aircraft engines. Their design and operation must be extremely reliable as engine safety and performance depend greatly on these elements. Axial compressors exhibit instabilities, such as surge or rotating stall, in a region close to the peak of their performance curves. These fluid dynamic instabilities can cause drops in efficiency, stress on the blades, fatigue, and even failures. Compressors are handled therefore by operating with a safety margin far from the surge line. Moreover, models able to predict onset instabilities and to reproduce them are of great interest. A dynamic system able to describe successfully both surge and rotating stall is the model presented by Moore and Greitzer That model has also been used for developing control laws of the compressor dynamics. The present work aims at developing an artificial neural network (ANN) approach able to predict either the permanence of the system in stable working condition or the onset instabilities from a time sequence of the compressor dynamics. Different solutions were tried to find the most suitable model for identifying the system, as well as the effects of the duration of the time sequence on the accuracy of the predicted compressor working conditions. The network was further tried for sequences with different initial values in order to perform a system analysis that included multiple variations from the initial database. The results show how it is possible to identify with high accuracy both rotating stall and surge with the ANN approach. Moreover, the presence of an underlying fluid dynamic model shares some similarities with physically informed AI procedures. Full article

Saline soils limit plant growth due to high salinity. Straw returning has proven effective in enhancing soil adaptability and agricultural stability on saline lands. This study evaluates the effects of different straw-returning methods—straw mulching (SM), straw incorporation (SI), and straw biochar (BC)—on soil nutrients, water dynamics, and salinity in a barley–cotton rotation system using field box experiments. SM improved soil water retention during barley’s jointing and heading stages, while SI was more effective in its filling and maturation stages. BC showed lesser water storage capacity. During cotton’s growth, SI enhanced early-stage water retention, and SM benefited the flowering and boll opening stages. Grey relational analysis pinpointed significant water relationships at 10 cm and 20 cm soil depths, with SM regulating water across layers. SM and BC notably reduced soil conductivity, primarily within the top 20 cm, and their effectiveness decreased with depth. SI significantly lowered soil conductivity at barley’s jointing stage. SM effectively reduced salinity at 10 cm and 20 cm soil depths, whereas BC decreased soil conductivity throughout barley’s jointing, filling, and heading stages. For cotton, SI lowered soil conductivity at the seedling and boll opening stages. SM consistently reduced salinity across all stages, and BC decreased conductivity in the top 30 cm of soil during all growth stages. Both SM and BC significantly enhanced the total nutrient availability for barley and cotton, especially improving soil organic carbon and available potassium, with BC showing notable improvements. At barley’s heading stage, SI maximized dry matter accumulation, while SM boosted accumulation in leaves, stems, and spikes during the filling and maturation stages. Straw returning increased barley yield, particularly with SM and BC, and improved water use efficiency by 11.60% and 5.74%, respectively. For cotton, straw returning significantly boosted yield and water use efficiency, especially with SI and SM treatments, enhancing the total bolls and yield. In conclusion, straw returning effectively improves saline soils, enhances fertility, boosts crop yields, and supports sustainable agriculture. These results provide a robust scientific foundation for adopting efficient soil improvement strategies on saline lands, with significant theoretical and practical implications for increasing agricultural productivity and crop resilience to salt stress. Full article

Power electronics plays a key role in the management and conversion of electrical energy in a variety of applications, including the use of renewable energy sources such as solar, wind and hydrogen energy, as well as in electric vehicles, industrial technologies, homes and smart grids. These technologies are essential for the successful implementation of the green transition, as they help reduce carbon emissions and promote the production and consumption of cleaner and more sustainable energy. The present work presents a new generation of power electronic devices and systems, which includes the following main aspects: advances in semiconductor technologies, such as the use of silicon carbide (SiC) and gallium nitride (GaN); nanomaterials for the realization of magnetic components; using a modular principle to construct power electronic devices; applying artificial intelligence techniques to device lifecycle design; and the environmental aspects of design. The new materials allow the devices to operate at higher voltages, temperatures and frequencies, making them ideal for high-power applications and high-frequency operation. In addition, the development of integrated and modular power electronic systems that combine energy management, diagnostics and communication capabilities contributes to the more intelligent and efficient management of energy resources. This includes integration with the Internet of Things (IoT) and artificial intelligence (AI) for automated task solving and work optimization. Full article

Glycerol-3-phosphoacyltransferase (GPAT) is an important rate-limiting enzyme in the biosynthesis of triacylglycerol (TAG), which is of great significance for plant growth, development, and response to abiotic stress. Although the characteristics of GPAT have been studied in many model plants, little is known about its expression profile and function in barley, especially under abiotic stress. In this study, 22 GPAT genes were identified in the barley genome and divided into three groups (I, II, III), with the latter Group III subdivided further into three subgroups based on the phylogenetic analysis. The analyses of conserved motifs, gene structures, and the three-dimensional structure of HvGPAT proteins also support this classification. Through evolutionary analysis, we determined that HvGPATs in Group I were the earliest to diverge during 268.65 MYA, and the differentiation of other HvGPATs emerged during 86.83–169.84 MYA. The tissue expression profile showed that 22 HvGPAT genes were almost not expressed in INF1 (inflorescence 1). Many functional elements related to stress responses and hormones in cis-element analysis, as well as qRT-PCR results, confirm that these HvGPAT genes were involved in abiotic stress responses. The expression level of HvGPAT18 was significantly increased under abiotic stress and its subcellular localization indicated its function in the endoplasmic reticulum. Various physiological traits under abiotic stress were evaluated using transgenic Arabidopsis to gain further insight into the role of HvGPAT18, and it was found that transgenic seedlings have stronger resistance under abiotic stress than to the wild-type (WT) plants. Overall, our results provide new insights into the evolution and function of the barley GPAT gene family and enable us to explore the molecular mechanism of functional diversity behind the evolutionary history of these genes. Full article

The present work is aimed at studying the effects of the magnetorheological finishing process, using a low-frequency alternating magnetic field, on the finishing performance of 6063 aluminum alloy. The study investigates the influence of key excitation parameters such as current, frequency, excitation gap, and iron powder diameter on the material removal and surface roughness (Ra) of the finished workpiece by experiments. This study employs a single-factor experimental method, and the finish surface is analyzed by a Zigo non-contact white light interferometer. The magnetic field strength in the processing area increases with the increase in the excitation current and decreases with the increase in the excitation gap. When the current frequency is set to 1 Hz, the circulation and renewal of abrasives in the magnetic cluster is most sufficient, resulting in the optimal surface roughness value for the workpiece. According to the experimental results of the excitation parameters, more suitable process parameters were selected for a two-stage finishing experiment. The surface roughness of 6063 aluminum alloy was improved from 285 nm to 3.54 nm. Experimental results highlighted that the magnetorheological finishing using a low-frequency alternating magnetic field is a potential technique for obtaining nano-scale finishing of the 6063 aluminum alloy. Full article

Afforestation is an effective approach for restoring degraded ecological functions in the dry-hot valleys of southwest China. Afforestation can affect soil carbon and nitrogen storage; however, how it affects soil P fractions, and their driving factors. is poorly understood in this region. To address these questions, we conducted a field study of Leucaena leucocephala plantations at three different stand age sites (3, 10, and 20 years) and an adjacent natural shrub-grass community control site to investigate changes in soil total phosphorus (Pt), Pi (inorganic phosphorus), Po (organic phosphorus), and phosphorus (P) fractions and their driving factors. Soil Pt, Po, labile P, and moderately labile P significantly increased in the Leucaena leucocephala plantation compared with the natural shrub grass site, and the Leucaena leucocephala plantation increased soil Pt content by significantly increasing soil Po. Soil Pt, Po, Pi, labile P, moderately labile P and non-labile P were not significantly different among the different stages of the Leucaena leucocephala plantation, and soil Pt and its fractions were all significantly higher in the middle-age forest stage of the Leucaena leucocephala plantation. These results indicate that Leucaena leucocephala plantations increased the soil P transformation ability, and soil Po played a critical role in sustaining soil P availability. The middle-age forest stage of Leucaena leucocephala plantations had the best conditions for P stocks and P conversion capacity. The abundance of actinomycetes and fungi showed significant positive relationships with soil Pi fractions (NaHCO₃-Pi, NaOH-Pi, and NaOH_u.s.-Pi); soil Pt and moderately labile P were significantly and directly influenced by fungal abundance. Soil organic carbon (SOC), NH₄⁺-N, and NO₃⁻-N showed significant and positive relationships with the soil Pi fractions (NaHCO₃-Pi, NaHCO₃-Po, and HCl-Po). SOC and NO₃⁻-N were the key drivers of soil Pt, labile P, moderately labile P and non-labile fractions. These results indicate that abiotic and biotic factors differently affected the soil P fractions and Pt in Leucaena leucocephala plantations in the dry-hot valley. Full article

Three-dimensional games are an indispensable tool in education and cultural transmission, offering users immersive experiences that facilitate learning through engagement and direct experience. The aim of this study was to design and implement an advanced cutscene sequencer in Unity 3D, targeted at educational and cultural games, to assist game developers in producing cinematic interludes, which are a key narrative element in games. The project methodology encompassed a detailed process of planning, design, and implementation. This involved the comprehensive use of various tools, including Unity 3D for game development, C# for scripting, Visual Studio for integrated development, Git for version control, Blender for 3D modeling, Substance Painter for texturing, and Audacity for audio editing. These tools collectively facilitated the development of a robust cutscene sequencer system designed to create engaging and dynamic narrative scenes. The project’s results indicate that the use of an advanced sequencer can significantly impact the efficiency and creativity of the game and educational material creation process, offering developers the opportunity to explore practically unlimited viewing perspectives. This tool enables the creation of rich and diverse visual experiences, which is crucial for engaging and educating players. Full article

Technological changes (such as Construction 4.0) in an organization cause the workforce to exhibit resistance to change, job redundancy, etc. Geographical location will no longer provide a competitive advantage, but resources will be the source of competitive advantage in the future, and these resources will be intangible, valuable, and not be easily imitated. The aim of this study is to provide an understanding of the future competitive advantages of organizations in the construction industry that could help the construction workforce easily adapt to technological changes. This study is based on resource-based theory and the ADKAR change management model. This study developed an ADREKA sequence for organizations to achieve future competitive advantage during technological changes in the construction industry. Hence, building social, relational, and human capital is necessary during technological changes to achieve competitive advantage for an organization and foster workforce adaptability to change. Full article

The demonstration of bioequivalence proposed in the European Medicines Agency’s (EMA’s) draft guideline for topical products with the same qualitative and quantitative composition requires the confirmation of the internal structure equivalence. The impact of the shelf-life on the parameters proposed for internal structure comparison has not been studied. The objectives of this work were: (1) to quantify the effect of the time since manufacturing on the mean value and variability of the parameters proposed by the EMA to characterize the internal structure and performance of topical formulations of a complex topical formulation, and (2) to evaluate the impact of these changes on the assessment of the microstructure equivalence. A total of 5 batches of a topical emulgel containing 1% diclofenac diethylamine were evaluated 5, 14, and 23 months after manufacture. The zero-shear viscosity (η₀), viscosity at 100 s⁻¹ (η₁₀₀), yield stress (σ₀), elastic (G′) and viscous (G″) moduli, internal phase droplet size and in vitro release of the active ingredient were characterized. While no change in variability over time was detected, the mean value of all the parameters changed, especially the droplet size and in vitro release. Thus, combining data from batches of different manufacturing dates may compromise the determination of bioequivalence. The results confirm that to assess the microstructural similarity of complex formulations (such as emulgel), the 90% confidence interval limit for the mean difference in rheological and in vitro release parameters should be 20% and 25%, respectively. Full article

Cardiovascular complications are the most deadly and cost-driving effects of diabetes mellitus (DM). One of them, which is steadily attracting attention among scientists, is diabetes-induced heart failure, also known as diabetic cardiomyopathy (DCM). Despite significant progress in the research concerning the disease, a universally accepted definition is still lacking. The pathophysiology of the processes accelerating heart insufficiency in diabetic patients on molecular and cellular levels also remains elusive. However, the recent interest concerning extracellular vesicles (EVs) has brought promise to further clarifying the pathological events that lead to DCM. In this review, we sum up recent investigations on the involvement of EVs in DCM and show their therapeutic and indicatory potential. Full article