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

The objective of this work is to compare the levels of bioactive compounds in fresh and freeze-dried basil leaves (Ocimum basilicum), using methodological procedures that follow internationally recommended and accepted standards. The results show comparisons of bioactives between fresh and freeze-dried leaves, respectively, with results expressing the average levels of vitamin C (9.50–63.3 mg/100 g); total polyphenols (1.8–3.9 mgEAG/g); flavonoids (0.73–1.78 mg/g); chlorophyll a (2287.8–1003.8 μg/100 g); chlorophyll b (2606–2287 μg/100 g) and total carotenoids with averages of 16.71–20.6 mg/100 g). Regarding color, there was variation in the parameters L*, a*, and b* of the dry sample, but the tendency towards green e (a+) and yellow (b−) was maintained. Infrared analysis (FTIR) demonstrated the presence of functional groups related to cellulose, hemicellulose, and lignin. Thermogravimetry (TG/DTG) shows stability in the range of 234 °C, showing a more intense mass loss at 294.6 °C. Given the data, it is possible to infer that the application of freeze-drying produces few changes in bioactive compounds and chemical groups and maintains good thermal stability, proving to be a viable alternative to increasing the commercialization of basil leaves, as it prolongs their useful life, and increases the forms of food applications. Full article

Underwriters play a pivotal role in the IPO process. Information entropy, a tool for measuring the uncertainty and complexity of information, has been widely applied to various issues in complex networks. Information entropy can quantify the uncertainty and complexity of nodes in the network, providing a unique analytical perspective and methodological support for this study. This paper employs a bipartite network analysis method to construct the relationship network between underwriters and accounting firms, using the centrality of underwriters in the network as a measure of their influence to explore the impact of underwriters’ influence on the distribution of interests and audit outcomes. The findings indicate that a more pronounced influence of underwriters significantly increases the ratio of underwriting fees to audit fees. Higher influence often accompanies an increase in abnormal underwriting fees. Further research reveals that companies underwritten by more influential underwriters experience a decline in audit quality. Finally, the study reveals that a well-structured audit committee governance and the rationalization of market sentiments can mitigate the negative impacts of underwriters’ influence. The innovation of this paper is that it enriches the content related to underwriters by constructing the relationship network between underwriters and accounting firms for the first time using a bipartite network through the lens of information entropy. This conclusion provides new directions for thinking about the motives and possibilities behind financial institutions’ cooperation, offering insights for market regulation and policy formulation. Full article

Functional foods with probiotics are safe and effective dietary supplements to improve overweight and obesity. Thus, altering the intestinal microflora may be an effective approach for controlling or preventing obesity. This review aims to summarize the experimental method used to study probiotics and obesity, and recent advances in probiotics against obesity. In particular, we focused on studies (in vitro and in vivo) that used probiotics to treat obesity and its associated comorbidities. Several in vitro and in vivo (animal and human clinical) studies conducted with different bacterial species/strains have reported that probiotics promote anti-obesity effects by suppressing the differentiation of pre-adipocytes through immune cell activation, maintaining the Th1/Th2 cytokine balance, altering the intestinal microbiota composition, reducing the lipid profile, and regulating energy metabolism. Most studies on probiotics and obesity have shown that probiotics are responsible for a notable reduction in weight gain and body mass index. It also increases the levels of anti-inflammatory adipokines and decreases those of pro-inflammatory adipokines in the blood, which are responsible for the regulation of glucose and fatty acid breakdown. Furthermore, probiotics effectively increase insulin sensitivity and decrease systemic inflammation. Taken together, the intestinal microbiota profile found in overweight individuals can be modified by probiotic supplementation which can create a promising environment for weight loss along enhancing levels of adiponectin and decreasing leptin, tumor necrosis factor (TNF)-α, interleukin (IL)-6, monocyte chemotactic protein (MCP)-1, and transforming growth factor (TGF)-β on human health. Full article

This paper provides insights into the four key behaviors and mechanisms of the aging to failure of batteries in micro-overcharge cycles at different temperatures, as well as the changes in thermal stability. The test results from a scanning electron microscope (SEM) and an energy-dispersive spectrometer (EDS) indicate that battery failure is primarily associated with the rupture of cathode materials, the fracturing and pulverization of electrode materials on the anode current collector, and the formation of lithium dendrites. Additionally, battery safety is influenced by environmental temperatures and the battery’s state of health (SOH), with failed batteries exhibiting the poorest stability and the highest mass loss rates. Under isothermal conditions, micro-overcharge leads to battery failure without thermal runaway. Thus, temperature stands out as the most influential factor in battery safety. These insights hold significant theoretical and practical value for the development of more precise and secure battery management systems. Full article

Familial Alzheimer’s disease (FAD) is a complex and multifactorial neurodegenerative disorder for which no curative therapies are yet available. Indeed, no single medication or intervention has proven fully effective thus far. Therefore, the combination of multitarget agents has been appealing as a potential therapeutic approach against FAD. Here, we investigated the potential of combining tramiprosate (TM), curcumin (CU), and the JNK inhibitor SP600125 (SP) as a treatment for FAD. The study analyzed the individual and combined effects of these two natural agents and this pharmacological inhibitor on the accumulation of intracellular amyloid beta iAβ; hyperphosphorylated protein TAU at Ser²⁰²/Thr²⁰⁵; mitochondrial membrane potential (DY_m); generation of reactive oxygen species (ROS); oxidized protein DJ-1; proapoptosis proteins p-c-JUN at Ser⁶³/Ser⁷³, TP53, and cleaved caspase 3 (CC3); and deficiency in acetylcholine (ACh)-induced transient Ca²⁺ influx response in cholinergic-like neurons (ChLNs) bearing the mutation I416T in presenilin 1 (PSEN1 I416T). We found that single doses of TM (50 μM), CU (10 μM), or SP (1 μM) were efficient at reducing some, but not all, pathological markers in PSEN 1 I416T ChLNs, whereas a combination of TM, CU, and SP at a high (50, 10, 1 μM) concentration was efficient in diminishing the iAβ, p-TAU Ser²⁰²/Thr²⁰⁵, DJ-1Cys¹⁰⁶-SO_3, and CC3 markers by −50%, −75%, −86%, and −100%, respectively, in PSEN1 I417T ChLNs. Although combinations at middle (10, 2, 0.2) and low (5, 1, 0.1) concentrations significantly diminished p-TAU Ser²⁰²/Thr²⁰⁵, DJ-1Cys¹⁰⁶-SO_3, and CC3 by −69% and −38%, −100% and −62%, −100% and −62%, respectively, these combinations did not alter the iAβ compared to untreated mutant ChLNs. Moreover, a combination of reagents at H concentration was able to restore the dysfunctional ACh-induced Ca²⁺ influx response in PSEN 1 I416T. Our data suggest that the use of multitarget agents in combination with anti-amyloid (TM, CU), antioxidant (e.g., CU), and antiapoptotic (TM, CU, SP) actions might be beneficial for reducing iAβ-induced ChLN damage in FAD. Full article

Locating petroleum-productive wells using informed geological data, a conventional means, has proven to be tedious and undesirable by reservoir engineers. The former numerical simulator required a lengthy trial-and-error process to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. Hence, this paper examines the use of a global genetic algorithm (GA) to optimize the placement of wells in complex reservoirs, rather than relying on gradient-based (GB) methods. This is because GB approaches are influenced by the solution’s surface gradient and may only reach local optima, as opposed to global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. The explicit focus of this study was to examine the impact of various initial well placement distributions, the number of random solution sizes and the crossover rate on cumulative oil production, the optimization of the synthetic reservoir model created by CMG Builder, CMOST, and IMEX indicated that using a greater number of random solutions led to an increase in cumulative oil production. Despite the successful optimization, more generations are required to reach the optimal solution, while the application of GA on our synthetic model has proven efficient for well placement; however, different optimization algorithms such as the improved particle swarm (PSO) and grey wolf optimization (GWO) algorithms could be used to redefine well-placement optimization in CMG. Full article

The purpose of this study was to develop an agro-healing program using rural resources for stressed adults and to measure its effectiveness. A one-day agro-healing program was developed using the resources of a care farm located in Cheongju, South Korea. A total of 37 adults in their 20s to 50s who were experiencing mild or more severe stress participated in the agro-healing program. Analysis of stress-related psychological and emotional indicators of the agro-healing program revealed that post-treatment stress, anxiety, and depression indicators of the healing program significantly decreased (p < 0.05). Furthermore, mood state, vitality, and resilience indicators increased significantly, which demonstrated effectiveness in improving the mental health of participants with mental health issues. A Pearson correlation analysis between each psychological and emotional variable after the program found that stress had a significant correlation with all variables. Subjective vitality had a full mediating effect on the relationship between stress and resilience. Finally, the one-day agro-healing program was found to help relieve symptoms in adults with stress. Full article

The aim of this study was to analyze the long-term results of different locoregional treatments for colorectal cancer liver metastases (CRLM), including transarterial chemoembolization (TACE), laser-induced thermotherapy (LITT) and microwave ablation (MWA). A total of 2140 patients with CRLM treated at our department between 1993 and 2020 were included in this retrospective study. The patients were divided into the following groups: LITT (573 patients; median age: 62 years), TACE + LITT (346 patients; median age: 62 years), MWA (67 patients; median age: 59 years), TACE + MWA (152 patients; median age: 65 years), and TACE (1002 patients; median age: 62 years). Median survival was 1.9 years in the LITT group and 1.7 years in the TACE + LITT group. The median survival times in the MWA group and TACE + MWA group were 3.1 years and 2.1 years, respectively. The median survival in the TACE group was 0.8 years. The 1-, 3-, and 5-year survival rates were 77%, 27%, and 9% in the LITT group and 74%, 18%, and 5% in the TACE + LITT group, respectively. The corresponding survival rates were 80%, 55%, and 33% in the MWA group, 74%, 36%, and 20% in the TACE + MWA group and 37%, 3%, and 0% in the TACE group, respectively. The long-term results of this study demonstrate the efficacy of locoregional treatments in treating patients with CRLM. The longest survival was found in the MWA group, followed by the combination therapy of TACE and MWA. Full article

The activation of endothelial cells is crucial for immune defense mechanisms but also plays a role in the development of atherosclerosis. We have previously shown that inflammatory stimulation of endothelial cells on top of elevated lipoprotein/cholesterol levels accelerates atherogenesis. The aim of the current study was to investigate how chronic endothelial inflammation changes the aortic transcriptome of mice at normal lipoprotein levels and to compare this to the inflammatory response of isolated endothelial cells in vitro. We applied a mouse model expressing constitutive active IκB kinase 2 (caIKK2)—the key activator of the inflammatory NF-κB pathway—specifically in arterial endothelial cells and analyzed transcriptomic changes in whole aortas, followed by pathway and network analyses. We found an upregulation of cell death and mitochondrial beta-oxidation pathways with a predicted increase in endothelial apoptosis and necrosis and a simultaneous reduction in protein synthesis genes. The highest upregulated gene was ACE2, the SARS-CoV-2 receptor, which is also an important regulator of blood pressure. Analysis of isolated human arterial and venous endothelial cells supported these findings and also revealed a reduction in DNA replication, as well as repair mechanisms, in line with the notion that chronic inflammation contributes to endothelial dysfunction. Full article