Anthropogenic activities and natural disturbances cause changes in natural ecosystems, leading to altered Plant Ecological Units (PEUs). Despite a long history of land use and land cover change detection, the creation of change detection maps of PEUs remains problematic, especially in arid and semiarid landscape. This study aimed to determine and describe the changes in PEUs patterns in the past and present, and also predict and monitor future PEUs dynamics using the multi-layer perceptron-Markov chain (MLP-MC) model in a semiarid landscape in Central Zagros, Iran. Analysis of PEUs classification maps formed the basis for the identification of the main drivers in PEUs changes. First, an optimal time-series dataset of Landsat images were selected to derive PEUs classification maps in three periods, each separated by 16 years. Then, PEUs multi-temporal maps classified for period 1 (years 1986–1988) period 2 (years 2002–2004), and period 3 (years 2018–2020) were employed to analyze and predict PEUs dynamics. The dominant transitions were identified, and the transition potential was determined by developing twelve sub-models in the final change prediction process. Transitions were modeled using a Multi-Layer Perceptron (MLP) algorithm. To predict the PEU map for period 3, two PEUs classification maps of period 1 and period 2 were used using the MLP-MC method. The classified map and the predicted map of period 3 were used to evaluate and validate the predicted results. Finally, based on the results, transitions of future PEUs were predicted for the year 2036. The MLP-MC model proved to be a powerful model that can predict future PEUs dynamics that are the result of current human and managerial activities. The findings of this study demonstrate that the impact of anthropogenic processes and management activities will become visible in the natural environment and ecosystem in less than a decade. Full article

This comprehensive review addresses the need for sustainable and efficient energy storage technologies against escalating global energy demand and environmental concerns. It explores the innovative utilization of waste materials from oil refineries and coal processing industries as precursors for carbon-based electrodes in next-generation energy storage systems, including batteries and supercapacitors. These waste-derived carbon materials, such as semi-coke, coal gasification fine ash, coal tar pitch, petroleum coke, and petroleum vacuum residue, offer a promising alternative to conventional electrode materials. They present an optimal balance of high carbon content and enhanced electrochemical properties while promoting environmental sustainability through effectively repurposing waste materials from coal and hydrocarbon industries. This review systematically examines recent advancements in fabricating and applying waste-derived carbon-based electrodes. It delves into the methodologies for converting industrial by-products into high-quality carbon electrodes, with a particular emphasis on carbonization and activation processes tailored to enhance the electrochemical performance of the derived materials. Key findings indicate that while higher carbonization temperatures may impede the development of a porous structure, using KOH as an activating agent has proven effective in developing mesoporous structures conducive to ion transport and storage. Moreover, incorporating heteroatom doping (with elements such as sulfur, potassium, and nitrogen) has shown promise in enhancing surface interactions and facilitating the diffusion process through increased availability of active sites, thereby demonstrating the potential for improved storage capabilities. The electrochemical performance of these waste-derived carbon materials is evaluated across various configurations and electrolytes. Challenges and future directions are identified, highlighting the need for a deeper understanding of the microstructural characteristics that influence electrochemical performance and advocating for interdisciplinary research to achieve precise control over material properties. This review contributes to advancing electrode material technology and promotes environmental sustainability by repurposing industrial waste into valuable resources for energy storage. It underscores the potential of waste-derived carbon materials in sustainably meeting global energy storage demands. Full article

This research addresses the critical opportunities and challenges confronting economic sustainability for hydrocarbon-based economies. The primary objective is to advocate for a transformative shift towards diversified and knowledge-centric economic models to ensure long-term sustainability. The literature review exposes vulnerabilities in hydrocarbon-based economies. The study innovatively explores multifaceted dimensions of economic diversification, including governance issues, societal well-being, and the necessity for robust ecosystem frameworks. Utilizing a qualitative case study methodology, the research investigates Qatar’s initiatives, such as the Qatar National Vision 2030 and government programs, aiming to reduce dependence on hydrocarbons and foster innovation. Insightful semi-structured interviews provide nuanced perspectives on innovation in the hydrocarbon context with business and academic professionals. The primary data collection method involved the following distinct groups: five business professionals and eleven academic experts, representing eight outstanding local and external organizations. Key findings underscore a holistic view of innovation, associating it with practical solutions, adaptability, and transformative potential, showcasing diverse approaches ranging from business-centric to collaborative and user-centric methods. Identified challenges in the education system, the urgency for a matured innovation ecosystem, and opportunities in sustainable energy further enrich the study. Moreover, this research examines the challenges and mitigation strategies associated with economic diversification in hydrocarbon-driven economies, focusing on Qatar. The research recommends sustained efforts in economic diversification, educational reform, and technological integration for hydrocarbon-based economies. Policymakers, businesses, and academics can leverage these insights to navigate the complexities of resource dependency and ensure long-term viability. The commitment to addressing challenges faced by hydrocarbon-dependent nations remains crucial, with a dedication to fostering economic diversity, innovation, and educational excellence for a resilient and prosperous future. Full article

This study explores low-intensity extracorporeal shock wave therapy (LiESWT)’s efficacy in alleviating detrusor hyperactivity with impaired contractility (DHIC) induced by ovarian hormone deficiency (OHD) in ovariectomized rats. The rats were categorized into the following four groups: sham group; OVX group, subjected to bilateral ovariectomy (OVX) for 12 months to induce OHD; OVX + SW4 group, underwent OHD for 12 months followed by 4 weeks of weekly LiESWT; and OVX + SW8 group, underwent OHD for 12 months followed by 8 weeks of weekly LiESWT. Cystometrogram studies and voiding behavior tracing were used to identify the symptoms of DHIC. Muscle strip contractility was evaluated through electrical-field, carbachol, ATP, and KCl stimulations. Western blot and immunofluorescence analyses were performed to assess the expressions of various markers related to bladder dysfunction. The OVX rats exhibited significant bladder deterioration and overactivity, alleviated by LiESWT. LiESWT modified transient receptor potential vanilloid (TRPV) channel expression, regulating calcium concentration and enhancing bladder capacity. It also elevated endoplasmic reticulum (ER) stress proteins, influencing ER-related Ca²⁺ channels and receptors to modulate detrusor muscle contractility. OHD after 12 months led to neuronal degeneration and reduced TRPV1 and TRPV4 channel activation. LiESWT demonstrated potential in enhancing angiogenic remodeling, neurogenesis, and receptor response, ameliorating DHIC via TRPV channels and cellular signaling in the OHD-induced DHIC rat model. Full article

Sensors based on solidly mounted resonators (SMRs) exhibit a good set of properties, such as high sensitivity, fast response, low resolution limit and low production cost, which makes them an appealing technology for sensing applications. However, they can suffer from cross-sensitivity issues, as their response can be altered by undesirable ambient factors, such as temperature and humidity variations. In this work we propose a method to discriminate humidity variations from the general frequency response using an SMR specifically manufactured to operate in a dual-mode (displaying two close resonances). The two modes behave similarly towards humidity changes (−1.94 kHZ/(%RH)) for resonance one and −1.62 kHZ/(%RH) for resonance two), whereas their performance under temperature changes is significantly different, displaying 2.64 kHZ/°C for resonance one and 34.21 kHZ/°C for resonance two. This allows for the decoupling process to be carried out in a straightforward manner. Frequency response is tracked under different humidity conditions, in the −20 °C to room temperature region, proving that this behavior is reproducible in any given environment. Full article

Sensor faults are one of the most common faults that cause performance degradation or functional loss in permanent magnet traction drive systems (PMTDSs). To quickly diagnose faulty sensors, this paper proposes a real-time joint diagnosis method for multi-sensor faults based on structural analysis. Firstly, based on limited monitoring signals on board, a structured model of the system was established using the structural analysis method. The isolation and detectability of faulty sensors were analyzed using the Dulmage–Mendelsohn decomposition method. Secondly, the minimum collision set method was used to calculate the minimum overdetermined equation set, transforming the higher-order system model into multiple related subsystem models, thereby reducing modeling complexity and facilitating system implementation. Next, residual vectors were constructed based on multiple subsystem models, and fault detection and isolation strategies were designed using the correlation between each subsystem model and the relevant sensors. The validation results of the physical testing platform based on online fault data recordings showed that the proposed method could achieve rapid fault detection and the localization of multi-sensor faults in PMTDS and had a good application value. Full article

Background: Studies have reported higher in-hospital mortality rates in patients living with dementia (PlwD) with limited evidence across age groups, clinical departments, and admission diagnoses. The aim of this study was to compare the in-hospital mortality rate of PlwD with patients without dementia across groups, clinical departments, and admission diagnoses. Methods: This case-control study included patients aged ≥ 60 years hospitalized in 1 of 14 German hospitals between January 2019 and July 2023. PlwD were matched to patients without dementia. The associations between dementia and in-hospital mortality across groups were assessed using univariable logistic regression analyses. Results: 15,956 patients with and 15,956 without dementia were included (mean age: 83.9 years, 60.7% female). PlwD had a significantly higher in-hospital mortality rate (14.0% vs. 11.7%; OR 1.24, 95% CI: 1.16–1.32) than non-dementia controls. The highest excess mortality rate was observed in the youngest age group (60–70 years: 10.9% vs. 5.7%; OR: 2.05, 95% CI: 1.30–3.24), decreased with age, and became non-significant in the oldest age group (≥90 years: 16.2% vs. 17.3%; OR: 0.93, 95% CI: 0.80–1.08). Significant differences were found for digestive system disorders (OR: 1.59; 95% CI: 1.15–1.89), cardiovascular and cerebrovascular disorders (OR: 1.51; 95% CI: 1.30–1.75), endocrine, nutritional, and metabolic diseases (OR: 1.42; 95% CI: 1.06–1.90), and pneumonia (OR: 1.20; 95% CI: 1.04–1.37), as well as for all clinic departments except for geriatric departments. Conclusion: The excess mortality rate was highest in younger age groups, where the general mortality and complication rate is relatively low in the general population. Appropriate approaches are needed, especially in non-geriatric wards. Full article

DNA methylation is a form of epigenetic regulation, having pivotal parts in controlling cellular expansion and expression levels within genes. Although blood DNA methylation has been studied in humans and other species, its prominence in cattle is largely unknown. This study aimed to methodically probe the genomic methylation map of Xinjiang brown (XJB) cattle suffering from bovine respiratory disease (BRD), consequently widening cattle blood methylome ranges. Genome-wide DNA methylation profiling of the XJB blood was investigated through whole-genome bisulfite sequencing (WGBS). Many differentially methylated regions (DMRs) obtained by comparing the cases and controls groups were found within the CG, CHG, and CHH (where H is A, T, or C) sequences (16,765, 7502, and 2656, respectively), encompassing 4334 differentially methylated genes (DMGs). Furthermore, GO/KEGG analyses showed that some DMGs were involved within immune response pathways. Combining WGBS-Seq data and existing RNA-Seq data, we identified 71 significantly differentially methylated (DMGs) and expressed (DEGs) genes (p < 0.05). Next, complementary analyses identified nine DMGs (LTA, STAT3, IKBKG, IRAK1, NOD2, TLR2, TNFRSF1A, and IKBKB) that might be involved in the immune response of XJB cattle infected with respiratory diseases. Although further investigations are needed to confirm their exact implication in the involved immune processes, these genes could potentially be used for a marker-assisted selection of animals resistant to BRD. This study also provides new knowledge regarding epigenetic control for the bovine respiratory immune process. Full article

Materials with enhanced wear resistance are constantly in high demand. Nickel-based self-fluxing materials deposited by atmospheric plasma spraying (APS) have feasible wear resistance performance. This study aimed to evaluate the results of a nickel-based self-fluxing alloy coating deposited on AISI 4340 steel substrate using APS. Additionally, the temperature at which the remelting process achieved optimal results was investigated. The AISI 4340 steel substrate samples were coated with a self-fluxing NiCrBSiCFe powder by APS. The post-coating remelting process was performed in a controlled atmosphere tube furnace at 900, 1000, and 1100 °C. Microstructural analysis was carried out by Scanning Electron Microscopy (SEM) before and after remelting. The estimated porosity of the as-sprayed sample was 3.28%, while the remelted coating sample at 1100 °C had only 0.22% porosity. Furthermore, a microhardness measurement was conducted, and the best condition yielded an average value of 750 HV_0.5. Tribological tests were performed to evaluate the coefficient of friction and wear rates, revealing that at 1100 °C, the as-sprayed coating had a wear rate of 9.16 × 10⁻⁵ [mm³/(N*m] and the remelted coating had 4.106 × 10⁻⁵ [mm³/(N*m]. The wear-loss volume was determined to be 14.1 mm³ for the as-sprayed coating sample and 3.6 mm³ for the remelted coating at 1100 °C. Full article

The growing global burden of malignant tumors with increasing incidence and mortality rates underscores the urgent need for more effective and less toxic therapeutic options. Herbal compounds are being increasingly studied for their potential to meet these needs due to their reduced side effects and significant efficacy. Pristimerin (PS), a triterpenoid from the quinone formamide class derived from the Celastraceae and Hippocrateaceae families, has emerged as a potent anticancer agent. It exhibits broad-spectrum anti-tumor activity across various cancers such as breast, pancreatic, prostate, glioblastoma, colorectal, cervical, and lung cancers. PS modulates several key cellular processes, including apoptosis, autophagy, cell migration and invasion, angiogenesis, and resistance to chemotherapy, targeting crucial signaling pathways such as those involving NF-κB, p53, and STAT3, among others. The main objective of this review is to provide a comprehensive synthesis of the current literature on PS, emphasizing its mechanisms of action and molecular targets with the utmost clarity. It discusses the comparative advantages of PS over current cancer therapies and explores the implications for future research and clinical applications. By delineating the specific pathways and targets affected by PS, this review seeks to offer valuable insights and directions for future research in this field. The information gathered in this review could pave the way for the successful development of PS into a clinically applicable anticancer therapy. Full article

This paper develops a thermodynamic entropy-based life prediction model to estimate the low-cycle fatigue (LCF) life of the nickel-based superalloy GH4169 at elevated temperature (650 °C). The gauge section of the specimen was chosen as the thermodynamic system for modeling entropy generation within the framework of the Chaboche viscoplasticity constitutive theory. Furthermore, an explicitly numerical integration algorithm was compiled to calculate the cyclic stress–strain responses and thermodynamic entropy generation for establishing the framework for fatigue life assessment. A thermodynamic entropy-based life prediction model is proposed with a damage parameter based on entropy generation considering the influence of loading ratio. Fatigue lives for GH4169 at 650 °C under various loading conditions were estimated utilizing the proposed model, and the results showed good consistency with the experimental results. Finally, compared to the existing classical models, such as Manson–Coffin, Ostergren, Walker strain, and SWT, the thermodynamic entropy-based life prediction model provided significantly better life prediction results. Full article

BCG vaccination is increasingly reconsidered in the effective prevention of bovine tuberculosis (bTB). However, the primary challenge in BCG vaccination for cattle is the lack of a technique for differentiating between infected and vaccinated animals (DIVA). This study aimed to establish a novel DIVA diagnostic test based on an interferon-gamma in vitro release assay (IGRA). The plasmid encoding three differential antigens (Rv3872, CFP-10, and ESAT-6) absent in BCG genes but present in virulent M. bovis was previously constructed. Thus, a recombinant protein called RCE (Rv3872, CFP-10, and ESAT-6) was expressed, and an RCE-based DIVA IGRA (RCE-IGRA) was established. The RCE concentration was optimized at 4 μg/mL by evaluating 97 cattle (74 of which were bTB-positive, and 23 were negative) using a commercial IGRA bTB diagnostic kit. Further, 84 cattle were tested in parallel with the RCE-IGRA and commercial PPD-based IGRA (PPD-IGRA), and the results showed a high correlation with a kappa value of 0.83. The study included BCG-vaccinated calves (n = 6), bTB-positive cattle (n = 6), and bTB-negative non-vaccinated calves (n = 6). After 3 months post-vaccination, PPD-IGRA generated positive results in both vaccinated and infected calves. However, RCE-IGRA developed positive results in infected calves but negative results in vaccinated calves. In conclusion, this DIVA method has broad prospects in differentiating BCG vaccination from natural infection to prevent bTB. Full article

Skin wounds, leading to infections and death, have a huge negative impact on healthcare systems around the world. Antibacterial therapy and the suppression of excessive inflammation help wounds heal. To date, the application of wound dressings, biologics and biomaterials (hydrogels, epidermal growth factor, stem cells, etc.) is limited due to their difficult and expensive preparation process. Cinnamomum burmannii (Nees & T. Nees) Blume is an herb in traditional medicine, and its essential oil is rich in D-borneol, with antibacterial and anti-inflammatory effects. However, it is not clear whether Cinnamomum burmannii essential oil has the function of promoting wound healing. This study analyzed 32 main components and their relative contents of essential oil using GC-MS. Then, network pharmacology was used to predict the possible targets of this essential oil in wound healing. We first proved this essential oil’s effects in vitro and in vivo. Cinnamomum burmannii essential oil could not only promote the proliferation and migration of skin stromal cells, but also promote M2-type polarization of macrophages while inhibiting the expression of pro-inflammatory cytokines. This study explored the possible mechanism by which Cinnamomum burmannii essential oil promotes wound healing, providing a cheap and effective strategy for promoting wound healing. Full article

As industrial modernization advances rapidly, the need for energy becomes increasingly urgent. This paper aims to enhance the current burner design by optimizing the combustion calorific value, minimizing pollutant emissions, and validating the accuracy of the burner model using experimental data from previous studies. The enhanced porous medium burner model is used to investigate the burner’s combustion and pollutant emission characteristics at various flow rates, equivalence ratios, combustion orifice sizes, and porosity of porous media. In comparison with the previous model, the combustion traits during ethylene combustion and the emission properties of pollutants under various operational circumstances have been enhanced with the enhanced porous medium burner model. The maximum temperature of ethylene combustion in the enhanced model is 174 k higher than that before the improvement, and the CO emissions are reduced by 31.9%. It is believed that the findings will serve as a guide for the practical implementation of porous media combustion devices. Full article

One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and specific parameters that could aid in early diagnosis. An ideal screening test, in view of the low incidence of ovarian cancer, should have a sensitivity of greater than 75% and a specificity of at least 99.6%. To enhance sensitivity and specificity, diagnostic panels are being created by combining individual markers. The drive to develop better screening tests for ovarian cancer focuses on modern diagnostic methods based on molecular testing, which in turn aims to find increasingly effective biomarkers. Currently, researchers’ efforts are focused on the search for a complementary parameter to those most commonly used that would satisfactorily enhance the sensitivity and specificity of assays. Several biomarkers, including microRNA molecules, autoantibodies, cDNA, adipocytokines, and galectins, are currently being investigated by researchers. This article reviews recent studies comparing the sensitivity and specificity of selected parameters used alone and in combination to increase detection of ovarian cancer at an early stage. Full article

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km²) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km². The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering. Full article

Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases. Full article

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a $\overline{MAE}$ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a $\overline{MAE}$ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll. Full article

The high mortality rate among patients with acute myocardial infarction (AMI) is one of the main problems of modern cardiology. It is quite obvious that there is an urgent need to create more effective drugs for the treatment of AMI than those currently used in the clinic. Such drugs could be enzyme-resistant peptide analogs of glucagon-like peptide-1 (GLP-1). GLP-1 receptor (GLP1R) agonists can prevent ischemia/reperfusion (I/R) cardiac injury. In addition, chronic administration of GLP1R agonists can alleviate the development of adverse cardiac remodeling in myocardial infarction, hypertension, and diabetes mellitus. GLP1R agonists can protect the heart against oxidative stress and reduce proinflammatory cytokine (IL-1β, TNF-α, IL-6, and MCP-1) expression in the myocardium. GLP1R stimulation inhibits apoptosis, necroptosis, pyroptosis, and ferroptosis of cardiomyocytes. The activation of the GLP1R augments autophagy and mitophagy in the myocardium. GLP1R agonists downregulate reactive species generation through the activation of Epac and the GLP1R/PI3K/Akt/survivin pathway. The GLP1R, kinases (PKCε, PKA, Akt, AMPK, PI3K, ERK1/2, mTOR, GSK-3β, PKG, MEK1/2, and MKK3), enzymes (HO-1 and eNOS), transcription factors (STAT3, CREB, Nrf2, and FoxO3), K_ATP channel opening, and MPT pore closing are involved in the cardioprotective effect of GLP1R agonists. Full article

Background and Objectives: The influence of montelukast (MK), an antagonist of cysLT1 leukotriene receptors, on lung lesions caused by experimental diabetes was studied. Materials and Methods: The study was conducted on four groups of six adult male Wistar rats. Diabetes was produced by administration of streptozotocin 65 mg/kg ip. in a single dose. Before the administration of streptozotocin, after 72 h, and after 8 weeks, the serum values of glucose, SOD, MDA, and total antioxidant capacity (TAS) were determined. After 8 weeks, the animals were anesthetized and sacrificed, and the lungs were harvested and examined by optical microscopy. Pulmonary fibrosis, the extent of lung lesions, and the lung wet-weight/dry-weight ratio were evaluated. Results: The obtained results showed that MK significantly reduced pulmonary fibrosis (3.34 ± 0.41 in the STZ group vs. 1.73 ± 0.24 in the STZ+MK group p < 0.01) and lung lesion scores and also decreased the lung wet-weight/dry-weight (W/D) ratio. SOD and TAS values increased significantly when MK was administered to animals with diabetes (77.2 ± 11 U/mL in the STZ group vs. 95.7 ± 13.3 U/mL in the STZ+MK group, p < 0.05, and 25.52 ± 2.09 Trolox units in the STZ group vs. 33.29 ± 1.64 Trolox units in the STZ+MK group, respectively, p < 0.01), and MDA values decreased. MK administered alone did not significantly alter any of these parameters in normal animals. Conclusions: The obtained data showed that by blocking the action of peptide leukotrienes on cysLT1 receptors, montelukast significantly reduced the lung lesions caused by diabetes. The involvement of these leukotrienes in the pathogenesis of fibrosis and other lung diabetic lesions was also demonstrated. Full article

One way to reduce the negative impact of human activity on the natural environment is to use natural, easily available and relatively cheap to produce compounds in industrial processes. One such compound is naturally occurring calcium carbonate (CaCO₃). This compound has adsorption properties so that it can be an alternative to commonly used adsorbents. The aim of this work is to determine the possibility of using CaCO₃ to remove pharmaceutical substances such as sulfadiazine and tetracycline from water. The CaCO₃ used in this work was synthesised using our own method, which allows the production of CaCO₃ particles with nanometric size. In the conducted research, calcium carbonate was used in the form of a suspension in purified solutions and as an inorganic filling of the developed membranes. The mass of pharmaceutical substances removed from their aqueous solutions was determined in the tests carried out. Based on the results obtained, it can be concluded that CaCO₃ has the ability to adsorb both tetracycline and sulfadiazine. In suspension tests, the mass of the removed substances per unit mass of adsorbent was 1.52 mg/g and 6.85 mg/g, respectively. In turn, in the case of the integrated process using the developed membranes, the mass of the removed substances per unit mass of adsorbent was 109 mg/g and 97 mg/g. Full article

As water and electrical networks cannot be entirely independent, a more integrated approach, the water–energy nexus (WEN), is developed. A WEN is the basis of a smart city where water and electrical networks are interconnected and integrated by implementing efficient management strategies. Accordingly, this study develops a dynamic co-optimization model for designing and operating an integrated power and water system. The proposed co-optimization model minimizes the total annual and operational costs of a micro-WEN system while capturing its optimum design values and operating conditions and meeting the demands of the electrical and water networks. Furthermore, this work presents a plan for transitioning from thermal desalination to reverse osmosis (RO) desalination in the United Arab Emirates (UAE). The key objective is to decouple electricity and water production, effectively tackling the issue of operating the UAE’s power plants at low efficiency during the winter while ensuring an adequate water supply to meet the growing demand. The results show that the co-optimization model provides a significant reduction in the total operational cost with the integration of photovoltaic energy and shifting to RO. Most importantly, the micro-WEN system is optimized over multiple timescales to reduce the computation effort and memory requirements. Full article