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Supply Chain Finance (SCF) in the energy sector has emerged as a critical area of focus due to the need for sustainable and efficient financial solutions to manage the complex interactions between various stakeholders, including suppliers, financial institutions, and energy companies. This study proposes a novel hybrid Topological Data Analysis (TDA) and Graph Neural Network (GNN) to optimize credit risk assessment in SCF. By leveraging BallMapper (BM) topological data analysis model and network-based features, the proposed model provides deeper insights into credit risk factors, enhancing the accuracy and dependability of credit risk evaluation for SMEs. Results demonstrate that the proposed BallMapper- Graph Neural Network (BM-GNN) model achieves higher accuracy and F1-scores, outperforming traditional machine learning approaches. Notably, incorporating network-based features alongside financial ratios yields the most favorable results in credit risk assessment. The SHapley Additive exPlanations (SHAP) model highlights the pivotal role of certain features in predicting bankruptcy, offering valuable insights for risk mitigation strategies. These results contribute to the growing body of evidence supporting the efficacy of TDA and GNN in financial applications, particularly in credit risk evaluation for SMEs in supply chain finance. Using network-based models opens up new avenues for improving accuracy and reliability in risk assessment, ultimately empowering financial institutions and stakeholders to make more informed decisions.

Background and objectives: Saffron is a known flavor with medicinal properties. Persian and Chinese medicines use saffron to treat several diseases. Saffron has neuroprotective and various other effects. In the present study the combinatory effects of saffron together with intensive light exposure were studied. Methods: Data are derived from Gene Expression Omnibus (GEO) database and assessed via R program. The significant differentially expressed genes (DEGs) including the individuals related to net effect of light exposure, the compensated dysregulated genes by saffron, and the genes associated to the combinatory effects of saffron and light exposure were determined and analyzed via protein-protein interaction (PPI) network analysis and gene ontology to find the affected critical biological processes. Results: Eighty-five genes dysregulated by light exposure in presence and absence of saffron in Sprague Dawley rats retina. Saffron protected 20 DEGs from light damage. “Cellular response to retinoic acid” and “lymphocyte chemotaxis” were pointed out as the crucial protected biological processes. A number of 559 DEGs including 20 hub-bottlenecks with combinatory effect of saffron and light were identified. Nitric oxide production, gene silencing by miRNAs, regulation of necrotic cell death and apoptosis appeared as related events to combinatory effects of saffron treatment and light exposure in mice. Conclusion: The positive alteration in nitric oxide, the effects on gene silencing by miRNAs, and alteration in cell death are the affected phenomena in response to combinatory effects of light and saffron in mouse retina. These findings open new window about ability of saffron to regulate basic functions in body.

Abstract Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers. In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. We demonstrate that our multi-task generative AI model, Orion, surpasses commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieves an overall sensitivity of 94% (95% CI: 87%–98%) at 87% (95% CI: 81%–93%) specificity for cancer detection across all stages, outperforming the sensitivity of other methods on held-out validation datasets by more than ~ 30%.

This research used the SWARA approach to analyze risk assessment criteria for public–private partnership (PPP) projects in Iran’s water and sewage sectors to identify and prioritize the most significant elements influencing project success from public and private viewpoints. Key results show that the public sector considers “risk probability” to be the most important aspect, highlighting the requirement for stability and predictability in project outcomes. In contrast, the private sector prioritizes the “ability to predict and discover risk”, emphasizing efficiently anticipating and managing uncertainty. Furthermore, this study revealed five common major risk characteristics, including “risk manageability” and “uncertainty of risk”; however, their rankings differ per industry, demonstrating various risk prioritizing methodologies. This study is unique in that it focuses only on Iran’s water and sewage infrastructure, an area historically neglected in PPP research, providing a rare investigation of sector-specific hazards as well as the interaction between public and private interests in a developing country environment. The paper makes specific suggestions, calling for more openness, improved communication, and the use of sophisticated risk management techniques to bridge the gap across sectors. These findings not only add to the scholarly knowledge of PPP dynamics in emerging countries but also provide practical recommendations for governments and private investors navigating Iran’s infrastructure issues.

Abstract Promoting eco-friendly choices is essential for long-term environmental protection, and nudging has emerged as a subtle but impactful tool to guide individuals and organizations toward sustainable behavior. Nudging techniques, such as default option settings, social norm displays, and feedback mechanisms, can be used to encourage actions like recycling, energy conservation, and increased use of public transportation. This comprehensive review spans the period from 2008 to 2024, providing a deep analysis of how nudging strategies have evolved and been applied in diverse domains such as food choices, energy consumption, transportation, and environmental regulation. The study synthesizes empirical research, behavioral theories, and ethical frameworks to assess the effectiveness and challenges of employing nudges for sustainability. One of the key research gaps identified is the lack of cross-cultural studies examining how different cultural contexts affect the success of nudging interventions. Additionally, there is a shortage of longitudinal research that tracks the long-term impact of nudging strategies. This review addresses these gaps by proposing future research directions that focus on cross-cultural analyses and long-term behavioral assessments. Using statistical measures, factorial analysis, content analysis, and thematic evolution analysis, the findings demonstrate fundamental shifts and emerging trends, such as the increasing role of digital nudging in promoting sustainable practices. This review contributes novel insights by integrating behavioral science, environmental psychology, and policy analysis, offering a valuable resource for practitioners, researchers, and policymakers. It also emphasizes the growing importance of technology-driven nudging interventions in shaping sustainable behaviors, thus providing a roadmap for future research and practical implementations.

Abstract Partial shading on series‐connected photovoltaic (PV) panels in conventional PV systems results in lower harvested power. To resolve this, it is vital to utilize module level power electronics (MLPE) such as Solar Power Optimizers (SPOs). This paper introduced a non‐isolated common ground non‐inverting output voltage buck‐boost converter as an SPO. Proposed converter benefits from continuous input and output currents which has a significant role in designing SPOs. Having a quadratic gain, beside acceptable step‐down range are other features of the converter. Operating principle, design, steady‐state, small‐signal analysis, and dynamic performance of proposed converter are included. Proposed converter is compared with other buck‐boost converters in terms of voltage gain, voltage stresses, continuous input and output current, and output polarity. To validate the performance of introduced converter, experimental results for a prototype with input voltage 24 V, output voltage 72 V for step‐up and 15 V for step‐down modes are given and results are examined. The maximum efficiency of the prototype is 93% and 89% for step‐up and step‐down modes, respectively. To evaluate the effect of proposed SPO for extracting maximum available power from PVs, simulation results of a grid connected PV system with two series connected SPOs is discussed.

Background and objectives: There is evidence that alcohol consumption is associated with liver damage. The positive role of garlic in preventing liver versus alcohol-related liver disease has been investigated before. In the present study, the possible protective role of garlic oil on the liver versus alcohol was evaluated via protein-protein interaction (PPI) network analysis to provide a new perspective of molecular events. Methods: GSE40334 from the Gene Expression Omnibus (GEO) database was selected for analysis. The significant differential expressed genes (DEGs) were determined by using the GEO2R program. UMAP analysis was applied to explore the separation of samples by analysis. Action map and gene ontology were used via Cytoscape software v 3.7.2 and its applications to find the critical DEGs and biological processes. Results: A number 798 significant DEGs were selected for analysis. The PPI network analysis showed that two clusters of DEGs play significant roles in response to garlic oil. Ranbp2, Pafah1b1, Seh1l, Plk1, Cenpa, Mis12, and Tgfb1 as the crucial DEGs were highlighted. The biological processes related to the essential function of cells such as proliferation and cell division were identified as the regulated biological terms. Conclusion: Garlic oil plays a protective role in preventing liver damage versus consuming alcohol. Among seven crucial genes, six individuals were regulated to enhance liver health.

Background and objectives: Rutin is a lipophilic natural flavonoid. It is found in vegetables, citrus fruits, and beverages. This study aims to evaluate rutin metabolic pathways in human senescent stromal cells. Methods: Data are extracted from the Gene Expression Omnibus (GEO) database and pre-evaluated via GEO2R to find the significant differentially expressed genes (DEGs). The significant DEGs were assessed via protein-protein interaction PPI network analysis to explore the hub genes. The hubs were screened via directed PPI to find the critical DEGs. Results: Data were assessed via volcano plot, Uniform Manifold Approximation and Projection (UMAP) plot, and Venn diagram. A total number of 9124 significant DEGs were analyzed to determine 33 upregulated and 61 downregulated hubs. The identified hubs were investigated via directed PPI and Il1B. ICAM1, CCL2, EGF, CXCL8, PTGS2, CAMK2B, CCN2, VCAM1, ELN, CXCL12, BGN, and TLR4 were pointed out as critical hub genes. Conclusion: Il1B, CCL2, GNAO1, ICAM1, EGF, and CXCL8 appeared as controller genes affected by rutin while PTGS2 and CAMK2B were the most controlled individuals. The finding refers to the significant advantages of the rutin effect on the function of treated cells. These advantages are corresponded to the usefulness of rutin as an herbal drug candidate. However, more investigations are required to decrease its side effects.

This research aims to enhance portfolio selection by integrating machine learning regression algorithms for predicting stock returns with various risk measures. These measures include mean-value-at-risk (VaR) variance (Var), semi-variance mean-absolute-deviation (MAD) and conditional value-at-risk (C-VaR). Addressing gaps in existing literature. Traditional methods lack adaptability to dynamic market conditions. We propose a hybrid approach optimized by genetic algorithms. The study employs multiple machine learning models. These include Random Forest, AdaBoost XGBoost, Support Vector Machine Regression (SVR) K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN). These models are used to forecast stock returns. Utilizing monthly data from the Tehran Stock Exchange, the results indicate that the genetic algorithm prediction model combined with mean-VaR, Var semi-variance and MAD, produces the most efficient portfolios. These portfolios offer superior returns with minimized risk compared to other models. This hybrid strategy provides a robust and efficient method for investors aiming to optimize returns while managing risk effectively. To implement this approach successfully it is crucial to balance investments. This involves both traditional and alternative asset classes, ensuring diversification. It also capitalizes on market opportunities. Regular review and adjustment of fund allocation are essential. Maintain an optimized strategy for maximum returns and minimal risk.

Extended abstract Background: Bread Wheat is the most extensively cultivated wheat and one of the four major crops in the world that constitutes the principal food of more than 30% of the world population. Biotic and abiotic environmental stressors are major factors limiting plant growth and productivity, which play a significant role in determining the yield and production potential of plants by affecting morphological, physiological, biochemical, and molecular processes. Among the abiotic stresses, the deficiency of micronutrients in the soil is important. Micronutrients regulate food metabolism in humans, and their deficiency endangers human health. Iron and zinc are essential micronutrients for human health and cofactors of many vital enzymes involved in many human metabolic processes. In plants, iron is the most required element among all micronutrients. It is a part of the catalytic group of many oxidation and reduction enzymes and is required for chlorophyll synthesis. To facilitate the adequate uptake and prevent excessive absorption of iron, plants have developed a balanced network to regulate the uptake, use, and storage of ions. In fact, such adjustment processes depend on genes that regulate ion homeostasis in plants. Due to the existence of a large allohexaploid genome and technical challenges in wheat transformation, few genes involved in iron and zinc uptake, transfer, and storage have been characterized functionally. Considering the important role of ZIP proteins in iron uptake efficiency, investigating the expression of the ZIP genes in Fe-efficient and -inefficient bread wheat cultivars can be effective in improving Fe-efficient cultivars in this valuable crop. Therefore, this research aimed to evaluate the expression of ZIP3, ZIP6, and ZIP7 genes in the leaves and roots of two Fe-efficient and -inefficient bread wheat cultivars at different growth stages under iron deficiency stress. Methods: This research was carried out in a completely randomized design (CRD) based on a factorial experiment with three replications in the research greenhouse of the Faculty of Agriculture, Urmia University. The first factor was two Fe-efficient (Pishtaz) and -inefficient (Flat) bread wheat cultivars, the second factor was two soil iron levels (iron deficiency and sufficiency, respectively, 1.4 and 10 mg/kg of soil), and the third factor was two sampling stages (vegetative and reproductive, respectively, one month after planting and 30% heading). To evaluate the expression of genes, the roots and leaves of the plants were sampled at each growth stage. The seeds were obtained from the Iranian Seed and Plant Improvement Institute, disinfected with 1% hydrogen peroxide, and planted at a depth of 4 cm in the soil. The plants were irrigated using distilled water to the extent of field capacity during the growing period. Results: The results of variance analysis of the relative expression of all three studied genes showed that the interaction effect of cultivar × organ × sampling stage was significant at the probability level of 1%. The comparison of the means for the cultivar × organ × sampling stage interaction effect revealed the highest ZIP3 expression in the roots of the Fe-efficient cultivar (Pishtaz) in the vegetative and reproductive stages. The relative expression of this gene was higher in the roots of the Fe-inefficient cultivar (Falat) than that of the Fe-efficient cultivar (Pishtaz). However, the Fe-inefficient cultivar (Falat) showed the highest relative expression increase in the leaf in both reproductive and vegetative stages, but the difference in the gene expression level in the leaf between the two growth stages was not statistically significant. The lowest gene expression level in the leaf belonged to the Pishtaz cultivar. The comparison of the means of cultivar × organ × sampling stage for the ZIP6 gene indicated an increase in the relative expression of this gene in the roots of the Fe-efficient (Pishtaz) and -inefficient (Falat) varieties in the vegetative and reproductive stages, respectively. The comparison of the means of cultivar × organ × sampling stage for the ZIP7 gene indicated the highest relative expression of this gene in the roots of the Fe-efficient cultivar (Pishtaz) at the vegetative stage. The relative expression level of this gene in the root of the Fe-efficient variety in the vegetative stage was significantly higher than that in the reproductive stage. In both vegetative and reproductive stages in the leaf, the increase in gene expression was higher in the Fe-inefficient cultivar. Conclusion: The increased ZIP3 expression in iron deficiency conditions in the roots of the Fe-efficient cultivar at the vegetative stage demonstrates the possible role of this gene in Fe uptake from the soil and its transfer to the aerial parts of the plant in the early growth phase. The ZIP6 gene `was expressed in both roots and leaves throughout the entire growth period of the plant. However, the expression level of this gene increased with the age of the plant. Therefore, the ZIP6 gene is probably responsible for Fe uptake and transport to different organs throughout the entire growth period of the plant and plays an important role in preserving iron in iron deficiency conditions. The ZIP7 gene is expressed in both leaves and roots in iron deficiency conditions, but the level of expression is higher in the roots of the Fe-efficient variety during the vegetative stage. This gene may be involved in iron uptake from the soil and its transfer to aerial organs.

Abstract Stylolites possess a dual function in assessing the quality of the Lower Cretaceous carbonate reservoir in the Abadan Plain, Zagros Basin. They can either operate as barriers or facilitate the flow of fluids. To investigate this, we conducted a comprehensive study using core-plug samples, thin section petrography, high-resolution computed tomography, geochemical analysis, and petrophysical evaluation. Our findings indicate that stylolite surfaces can enhance effective porosity and connectivity by acting as open pathways. In the Fahliyan Formation, stylolites can be classified into four types based on their characteristics, including shape, size, amplitude, and the presence of insoluble material in the seams. The genetic type of stylolites is determined by the dominant stress direction, while various parameters in the burial diagenetic stage, such as pressure, temperature, and the presence of soluble ion-rich fluids, can affect porosity and permeability. Stylolites in the Fahliyan facies create continuous and connected porosity for fluid flow, with their amplitude and morphology impacting reservoir quality, especially in mud-supported facies. Therefore, the presence of stylolites in mud-supported facies can improve porosity and permeability. Dissolution, reduced overburden pressure, and horizontal compression are the main factors that expose the stylolite surfaces in the Fahliyan Formation. The extent of cementation, which is the primary barrier feature, varies significantly across the Fahliyan Reservoir in the Abadan Plain Zone due to the degree of stylolitization in the examined facies. However, our findings from wells and geological data combination indicate that reservoir quality in the examined formation facies is significantly influenced by various conditions, with a particular emphasis on the type of fluid flow in the passages.

IntroductionWater shortage has become a global problem and has caused many problems in agriculture and food supply for the growing world. Barley (Hordeum vulgare L.) as the fourth mostly-cultivated cereal, is one of the most strategic crop plants which is produced almost all over the world as a source of and important staple food and animal feed (Thabet et al., 2020). Food uncertainty is a comprehensive obstacle becomes more serious hazard all over the world in particular in developing countries for the sake of overpopulation and dwindling accessibility of croplands, water and other resources related to agricultural scopes. Water scarcity, results in plenty of disturbances in plant functions like cell division and elongation, water and nutrients relations, photosynthesis, enzymes activity, stomata movement, assimilate partitioning, respiration, oxidative damage, growth, and productivity, as several types of researches show that water shortage in the soil cause many disorders in plant tissues, which in turn leads to a punctual diminish in the photosynthesis rate (Todorova et al., 2022). In such a trouble circumstances, most of the plants are not capable to absorb abundant water, which is required for optimized growth (Danish et al., 2020).Materials and methodsTo investigate the impact of different irrigation regimes as well as foliar application of methyl jasmonate on growh, physiological and biochemical characteristics of barley varieties, an experimental research using factorial split plot design in 3 replications was carried out in experimental farm of the faculty of agriculture at the University of Zanjan in 2021-2022 cultivation season. In this experiment, irrigation regimes as the main factor, including complete irrigation as the control, withholding water in flowering stage, withholding water in grain filling period and complete dry farming, varieties as the secondary factor including Bahman, Sahand, Jolgeh, Abidar as well as Ansar, and foliar application of methyl jasmonate including without spraying (control) and spraying 50 μmol of methyl jasmonate were investigated. Results showed that the effect of irrigation regime had significant impact on almost all of the characteristics except chlorophyll a/b.Results and discussionVarieties showed considerable difference from the aspect of height, concentration of soluble sugar and grain yield. Effect of Methyl jasmonate on the relative water content (RWC), photosynthetic pigments, soluble sugar content, proline content, Malondialdehyde (MDA) and grain yield was significant. The highest grain yield (4762 kg ha-1) was detected when variety of Jolgeh was irrigated normally and was sprayed by 50 μmol of Methyl jasmonate and the lowest grain yield (432 kg ha-1) was seen when variety of Bahman was dry-farmed without foliar application of methyl jasmonate.ConclusionThe current study illustrated that barley can be grown in drought stress conditions if the right management is set on its cultivation. Methyl jasmonate showed a significant impact on the growth characteristics, biochemical and physiological attributes of the barley, despite the fact that drought had substantial adverse effects on the studied parameters of barley. However, drought resulted in different properties in barley varieties.

Abstract Background Multiple trauma has serious complications, which increases the risk of morbidity and mortality in the patients. This study aimed to evaluate the impact of supplementation with phytosomal curcumin on clinical and laboratory factors in critically ill patients with multiple trauma. Methods In this double-blind trial, 53 patients with multiple trauma, who were admitted to the intensive care unit (ICU) were randomized to receive either 2 capsules, each capsule containing 250 mg phytosomal (a total of 500 mg daily) as an intervention group or 2 identical capsules (placebo capsules), each containing 250 mg maltodextrin for 7 days. Clinical and laboratory were parameters assessed before and after the intervention. Results After seven days of intervention, the mean increase from baseline in the Glasgow coma scale (GCS) score was significantly higher in the curcumin compared with the placebo group (P-value: 0.028), while the reduction in the APACHE-II score in the curcumin group was greater than that the placebo group in a marginally non-significant fashion (P-value: 0.055). Serum total bilirubin (P-value: 0.036) and quantitative C-reactive protein (CRP) (P-value: 0.044) levels significantly decreased while potassium (P-value: 0.01) significantly increased in the curcumin compared with the placebo group. Moreover, supplementation with phytosomal curcumin significantly increased platelet count (P-value: 0.024) as compared with placebo. The 28-day mortality rate was 7.7% (n: 2 patients) and 3.7% (n: 1 patients) in the placebo and curcumin groups, respectively (P-value > 0.05). Conclusion Phytosomal curcumin had beneficial effects on several clinical and laboratory factors including GCS, APACHEII, serum total bilirubin, CRP, and platelet count in ICU-admitted patients with multiple trauma. Trial registration IRCT20090306001747N1, Available on: https://www.irct.ir/trial/52692 . The first registration date was 12/01/2021.

Next-generation sequencing (NGS) is crucial for monitoring and investigating infectious disease outbreaks, providing essential data for public health decisions. The COVID-19 pandemic has significantly expanded pathogen sequencing and bioinformatics capacities worldwide, creating an opportunity to leverage these advancements for other pathogens with pandemic and epidemic potential. In response to the need for a systematic cost estimation approach for sustainable genomic surveillance, particularly in low- and middle-income countries, five institutions collaborated to develop the genomics costing tool (GCT). These institutions are the Association of Public Health Laboratories (APHL), FIND, The Global Fund to Fight AIDS, Tuberculosis and Malaria, the UK Health Security Agency (UKHSA), and the World Health Organization (WHO). To validate the GCT, it was piloted in public health laboratories across three WHO regions: African, Eastern Mediterranean, and European. The pilot exercises were intended to assess the tool’s accuracy, utility, and functionality, exploring scenarios for validating past expenditure, routine use, cost optimization, and scaling up sequencing services. Data from these pilots demonstrated significant cost reductions per sample with increased throughput, underscoring the economic benefits of the optimized use of sequencing platforms underpinned by sample throughput. The GCT enables laboratories to estimate and visualize costs, plan budgets, and improve cost-efficiencies for sequencing and bioinformatics based on factors such as equipment purchase and preventative maintenance, reagents and consumables, annual sample throughput, human resources training, quality assurance and management. This publication shares key findings from pilot exercises offering detailed insights into the cost of routine NGS implementation using either short- or long-read sequencing technologies, demonstrating the utility of GCT as an asset to support efforts for sustainable funding and strategic planning in genomic surveillance.

This paper presents a bi-level game model for pricing in a supply chain where the manufacturer (He) is the leader, and the retailer (She) is the follower. The leader decides on the wholesale price, and the follower decides on the selling price and selects seed nodes. The main idea of the model is that the marketing strategy used for promoting the product is focused on giving free samples to potential customers. Hence, the importance of analyzing a social network becomes evident. To maximize her profit, the retailer decides based on three factors: first, the leader's decision about wholesale price; second, the social network structure, which is critical for selecting the seed nodes; and third, people's valuation of the product. Therefore, a bi-level Mixed-Integer Nonlinear Programming model is developed to consider the social binds between potential customers. To solve this model, we employ a meta-heuristic algorithm. Finally, the effect of the model's parameters on decision variables and the objective functions is discussed. Based on the analysis and discussions, the production cost has a prominent impact on the players’ decisions and profits. Furthermore, instead of spending all the marketing budget on increasing seed nodes, it is suggested that they be spent on market research and improving good publicity. Moreover, deciding whether the players want to maximize the profit or market penetration is required before diving into decision-making.

Objective: There is no definitive pharmacological strategy for COVID-19; thus, medicinal herbs can be an appropriate option for COVID-19 management. We investigated the efficacy of a D-reglis® tablet (root extract of licorice) as adjuvant therapy in critically ill patients with COVID-19 at intensive care units (ICUs) of Alzahra Teaching Hospital affiliated with Isfahan University of Medical Sciences, Isfahan, Iran. Methods: In the present double-blind, randomized, placebo-controlled clinical trial, critically ill cases with COVID-19 (n = 52) received a D-reglis® tablet (760 mg) or a placebo tablet for 5 days. The ICU stay length was the primary outcome. The secondary outcome included the changes in oxygen saturation, duration of mechanical ventilation, mortality rate, and Sequential Organ Failure Assessment (SOFA) Score during the study period. Findings: The ICU stay was significantly lower in the licorice group than in the placebo group (P = 0.015). No significant difference was detected between the groups regarding oxygen saturation, SOFA score, duration of mechanical ventilation, and mortality rate. Conclusion: The licorice tablet (D-reglis®) as an adjuvant treatment showed promising results regarding the ICU stay length in critically ill COVID-19 patients. However, further clinical trials with larger sample sizes, further duration of intervention, measurement of inflammatory markers, and further study about the molecular mechanism of the effect of licorice on COVID-19 should be done to obtain more conclusive findings.

Background and objectives: Positive role of coffee consumption on regulation of human blood sugar has been highlighted by researchers. On the other hand, metformin is the common drug against type 2 diabetes. This study aimed to explore possible ways to use coffee as a complementary agent beside metformin or independently versus type 2 diabetes. Methods: Proteomic data about effect of two major compounds of coffee (caffeine and trigonelline) on improvement of diabetic condition was searched and analyzed via protein-protein interaction (PPI) network analysis and gene ontology enrichment. Gene expression profiles of human whole blood of diabetic patients (responsive to metformin) versus control were extracted from GSE83983 which is recorded in Gene Expression Omnibus (GEO) database. After pre-evaluation of data by GEO2R program, the significant differentially expressed genes (DEGs) were assessed via PPI network analysis and regulatory network evaluation. Results: Caffeine and trigonelline effectively regulate the glycolytic processes to fight against diabetic condition. HSP90AA1, TLR4, RELA, ARRB, LRRK2, STAT5B, LYN, and TLR2 genes that are involved in diabetes were affected significantly by metformin. Conclusion: It can be concluded that coffee consumption can improve sugar regulation in diabetes similar to metformin. IT seems that the optimized consumption quantity of coffee can be considered as controller of blood sugar in diabetic patients.

Abstract The threat of methicillin-resistant Staphylococcus aureus (MRSA) is increasing worldwide, making it significantly necessary to discover a novel way of dealing with related infections. The quick spread of MRSA isolates among infected individuals has heightened public health concerns and significantly limited treatment options. Vancomycin (VAN) can be applied to treat severe MRSA infections, and the indiscriminate administration of this antimicrobial agent has caused several concerns in medical settings. Owing to several advantageous characteristics, a niosomal drug delivery system may increase the potential of loaded antimicrobial agents. This work aims to examine the antibacterial and anti-biofilm properties of VAN-niosome against MRSA clinical isolates with emphasis on cytotoxicity and stability studies. Furthermore, we aim to suggest an effective approach against MRSA infections by investigating the inhibitory effect of formulated niosome on the expression of the biofilm-associated gene (icaR). The thin-film hydration approach was used to prepare the niosome (Tween 60, Span 60, and cholesterol), and field emission scanning electron microscopy (FE-SEM), an in vitro drug release, dynamic light scattering (DLS), and entrapment efficiency (EE%) were used to investigate the physicochemical properties. The physical stability of VAN-niosome, including hydrodynamic size, polydispersity index (PDI), and EE%, was analyzed for a 30-day storage time at 4 °C and 25 °C. In addition, the human foreskin fibroblast (HFF) cell line was used to evaluate the cytotoxic effect of synthesized niosome. Moreover, minimum inhibitory and bactericidal concentrations (MICs/MBCs) were applied to assess the antibacterial properties of niosomal VAN formulation. Also, the antibiofilm potential of VAN-niosome was investigated by microtiter plate (MTP) and real-time PCR methods. The FE-SEM result revealed that synthesized VAN-niosome had a spherical morphology. The hydrodynamic size and PDI of VAN-niosome reported by the DLS method were 201.2 nm and 0.301, respectively. Also, the surface zeta charge of the prepared niosome was − 35.4 mV, and the EE% ranged between 58.9 and 62.5%. Moreover, in vitro release study revealed a sustained-release profile for synthesized niosomal formulation. Our study showed that VAN-niosome had acceptable stability during a 30-day storage time. Additionally, the VAN-niosome had stronger antibacterial and anti-biofilm properties against MRSA clinical isolates compared with free VAN. In conclusion, the result of our study demonstrated that niosomal VAN could be promising as a successful drug delivery system due to sustained drug release, negligible toxicity, and high encapsulation capacity. Also, the antibacterial and anti-biofilm studies showed the high capacity of VAN-niosome against MRSA clinical isolates. Furthermore, the results of real-time PCR exhibited that VAN-niosome could be proposed as a powerful strategy against MRSA biofilm via down-regulation of icaR gene expression.

Abstract Transition metal carbides have been adopted in energy storage, conversion, and extreme environment applications. Advancements in their 2D counterparts, known as MXenes, enable the design of unique structures at the ~1 nm thickness scale. Alkali cations have been essential in MXenes manufacturing processing, storage, and applications, however, exact interactions of these cations with MXenes are not fully understood. In this study, using Ti3C2T x , Mo2TiC2T x , and Mo2Ti2C3T x MXenes, we present how transition metal vacancy sites are occupied by alkali cations, and their effect on MXene structure stabilization to control MXene’s phase transition. We examine this behavior using in situ high-temperature x-ray diffraction and scanning transmission electron microscopy, ex situ techniques such as atomic-layer resolution secondary ion mass spectrometry, and density functional theory simulations. In MXenes, this represents an advance in fundamentals of cation interactions on their 2D basal planes for MXenes stabilization and applications. Broadly, this study demonstrates a potential new tool for ideal phase-property relationships of ceramics at the atomic scale.

Objective(s): Our study aimed to formulate a niosomal system for enhancing tannic acid’s antibacterial and antibiofilm activities against vancomycin-intermediate staphylococcus aureus (VISA). Materials and Methods: Niosomal tannic acid was formulated by the thin-film hydration technique, which their physicochemical attributes, including drug loading, particle size, zeta potential, morphology, encapsulation efficiency (EE%), polydispersity index (PDI), release profile, were evaluated. To investigate the cell viability of the prepared niosomes, the cytotoxicity effect was analyzed against the human foreskin fibroblast (HFF) cell line. Finally, the antibacterial and anti-biofilm activities of niosomal formulation were examined against VISA strains and compared tothe free drug.Results: Scanning electron microscopy images showed that the niosomal formulation incorporated tannic acid was homogeneous, spherical, and identical in size (151.9 nm). EE% and surface charge of the synthesized niosomes were 68.90% and -60 mV, respectively. The tannic acid-encapsulated niosomes showed a significant antibacterial potential in comparison with the free drug. Furthermore, niosomal tannic acid reduced the biofilm formation ability in all VISA strains and efficiently eradicated the formed bacterial biofilms at the same concentrations of the free drug.Conclusion: Niosomes, as vesicular-based nanoparticles, are known to be potent drug delivery vehicles due to numerous features such as non-toxicity, small size, sustained-release profile, and protection from pharmaceutical degradation. Niosomes have a high capacity to deliver wide range of antimicrobial agents, including natural compounds, which could be presented as a novel approach against bacterial infections, particularly VISA strains.Objective(s): Our study aimed to formulate a niosomal system for enhancing tannic acid’s antibacterial and antibiofilm activities against vancomycin-intermediate staphylococcus aureus (VISA). Materials and Methods: Niosomal tannic acid was formulated by the thin-film hydration technique, which their physicochemical attributes, including drug loading, particle size, zeta potential, morphology, encapsulation efficiency (EE%), polydispersity index (PDI), release profile, were evaluated. To investigate the cell viability of the prepared niosomes, the cytotoxicity effect was analyzed against the human foreskin fibroblast (HFF) cell line. Finally, the antibacterial and anti-biofilm activities of niosomal formulation were examined against VISA strains and compared tothe free drug.Results: Scanning electron microscopy images showed that the niosomal formulation incorporated tannic acid was homogeneous, spherical, and identical in size (151.9 nm). EE% and surface charge of the synthesized niosomes were 68.90% and -60 mV, respectively. The tannic acid-encapsulated niosomes showed a significant antibacterial potential in comparison with the free drug. Furthermore, niosomal tannic acid reduced the biofilm formation ability in all VISA strains and efficiently eradicated the formed bacterial biofilms at the same concentrations of the free drug.Conclusion: Niosomes, as vesicular-based nanoparticles, are known to be potent drug delivery vehicles due to numerous features such as non-toxicity, small size, sustained-release profile, and protection from pharmaceutical degradation. Niosomes have a high capacity to deliver wide range of antimicrobial agents, including natural compounds, which could be presented as a novel approach against bacterial infections, particularly VISA strains.

In recent years, there has been a significant amount of literature on design, design thinking, and the design process. Many researchers have conducted studies to describe and explain the essence and mechanisms of the design process, as well as the unique nature of design thinking. However, this research aims to systematically review the literature in the field of design studies and related fields on the subject of negative emotional experiences as perceived in these studies, striving to present a comprehensive content and theoretical framework for previous studies. The study of this topic is significant due to its potential to enhance our understanding of how designers' function during the creative design process and the effects of these emotions on them. This research serves as a foundation for more precise analyses of these negative emotional experiences, which can lead to the development of effective and structured design approaches. n recent years, there has been a significant amount of literature on design, design thinking, and the design process. Many researchers have conducted studies to describe and explain the essence and mechanisms of the design process, as well as the unique nature of design thinking. However, this research aims to systematically review the literature in the field of design studies and related fields on the subject of negative emotional experiences as perceived in these studies, striving to present a comprehensive content and theoretical framework for previous studies.....

Abstract Background Wilms tumor (WT), also known as nephroblastoma, is rare in adults, accounting for merely 3% of all nephroblastomas or 0.2 cases per million individuals. Extrarenal Wilms tumor (ERWT) emerges outside the renal boundaries and comprises 0.5 to 1% of all WT cases, with even rarer incidences in adults. Oncogenic mutations associated with ectopic nephrogenic rests (NR) may contribute to ERWT development. Diagnosis involves surgical resection and pathology examination. Due to scarce cases, adults often rely on pediatric guidelines. We thoroughly searched PubMed, Scopus, and Web of Science databases to establish our case’s uniqueness. To the best of our knowledge, this is the first documented incidence of extrarenal Wilms tumor within the spinal canal in the adult population. Case presentation A 22-year-old woman with a history of congenital lipo-myelomeningocele surgery as an infant presented with a 6-month history of back pain. This pain gradually resulted in limb weakness, paraparesis, and loss of bladder and bowel control. An MRI showed a 6 × 5 × 3 cm spinal canal mass at the L4-S1 level. Consequently, a laminectomy was performed at the L4-L5 level to remove the intramedullary tumor. Post-surgery histopathology and immunohistochemistry confirmed the tumor as ERWT with favorable histology without any teratomatous component. Conclusion This report underscores the rarity of extrarenal Wilms tumor (ERWT) in adults, challenging conventional assumptions about its typical age of occurrence. It emphasizes the importance of clinical awareness regarding such uncommon cases. Moreover, the co-occurrence of spinal ERWTs and a history of spinal anomalies warrants further investigation.

Objective: Self-sufficiency and reliance on natural materials are fundamental principles of Iranian architecture, with brick being a locally abundant material in Iran. Over time, brick buildings require retrofitting, restoration, and enhancement. To gain a deeper understanding of brick and its retrofitting, it is essential to analyze historical bricks and incorporate past experiences in brick making. This research aims to examine the technical characteristics of bricks used in the historical buildings of Kashan city across different time periods. Five brick samples from these historical structures have been selected for analysis. Method: The research employs a quantitative approach based on field studies and laboratory experiments. The experiments are categorized into three groups. The first group comprises basic tests, including density, specific gravity, moisture content, and water absorption. The second group encompasses mechanical tests, focusing on compressive and bending strength. The third category involves chemical tests conducted through X-ray Diffraction (XRD) analysis. Results: The tests revealed that the specific weight of the samples ranged from 1.6 to 2.14 grams per cubic centimeter, while water absorption varied between 9.3 and 18.5 percent. The compressive strength was found to be between 53.12 and 195.27 kg/cm², and the bending strength ranged from 11.62 to 125.83 kg/cm². The findings indicate that basic characteristics significantly influence mechanical properties. As specific weight and volume density of the samples increase, their internal pore structure diminishes, leading to reduced moisture content and lower water absorption. Furthermore, higher sample density correlates with increased compressive and bending strength. The predominant chemical composition in the samples was quartz, while the presence of gypsum contributed to decreased mechanical resistance. In contrast, higher levels of quartz, anorthite, and albite enhanced the resistance of the samples Conclusions: The physical and mechanical characteristics of brick samples remain relatively stable over time. Consequently, it appears that the resistance parameters are more influenced by the quality of the raw materials, sample density, and production methods rather than the age of the samples.

Sonchus arvensis is recognized for its high content of phenolic compounds and antioxidants, exhibiting various medicinal benefits, including anti-diabetic, anti-depressant, and anti-cancer properties. This has positioned the plant as a significant candidate for commercial food, medicinal, and antioxidant supplements. Salinity may enhance the level of chlorogenic and caffeic acid, which are key secondary metabolites in S. arvensis. To investigate this, a completely randomized design experiment with three replications was implemented in a greenhouse to examine the impact of salinity on the expression of six genes responsible for the biosynthesis of chlorogenic acid. Additionally, the study examined how salinity affects the accumulation of chlorogenic acid, caffeic acid, chicoric acid, and apigenin in the lower and middle leaves of plants. Salinity stress treatments were applied at four different levels: 0 (control), 50, 100, and 150 mM of NaCl. The results indicated that the expression levels of phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), 4-coumarate-CoA ligase (4CL), and p-coumaroyl ester 3′-hydroxylase (C3′H) were highest in the middle leaves at a concentration of 150 mM NaCl. Notably, there was an eight-fold increase in C4H expression in these leaves under the same salinity conditions. Conversely, the expression of shikimate/quinate O-hydroxycinnamoyl transferase (HCT) and quinate O-hydroxycinnamoyl transferase (HQT) genes decreased across all salinity treatments. Additionally, the levels of chlorogenic acid, caffeic acid, and chicoric acid were significantly elevated at 50 mM NaCl in both the lower and middle leaves, suggesting that cultivating S. arvensis in mildly saline environments could be beneficial. Furthermore, the findings from this study may serve as a preliminary step towards the cloning and full characterization of the genes examined in S. arvensis.

Construction operation is among the most high-risk sectors in terms of work-related accident, making it highly challenging to surveil the safety of such projects. In construction projects, failure to observe safety represents a leading cause of fatal accidents, not to mention the losses incurred by such accidents to national assets of the country. Accordingly, recent decades have witnessed the emergence of modern techniques for improving the occupational safety of construction projects. The main purpose of the present research is to identify and classify different preventable risk mitigation factors in mass housing projects following a building information modeling (BIM) approach. The research methodology included interviews with relevant experts and elites followed by analysis of the data on the 12 identified-as-significant variables for mitigating the preventable risk factors in mass house construction projects by means of the inferential – structural modeling (ISM) in MICMAC software. In order to explore the relationships among and succession of different criteria and further classify them at different levels, ISM was implemented, with the MICMAC software used to analyze the direct and indirect influences, develop influence/dependence maps, and judge about the role of each criterion. Findings of the present research showed that the mutual relations (H3), the reward system (H6), the reporting system (H7), and the supervisors' supervision (H8) are autonomous variables and hence impose the smallest contributions to the system. Accordingly, they can be eliminated from the model though their effects may not be completely ignored. On the other hand, the employees’ empowering (H4), the safety management system (H5), the teamwork (H9), the self-efficiency (H10), and the knowledge and awareness (H11) were identified as the linkage variables that fill in the gap between the safety and occupational accident reduction in the mass house construction projects. Further, the continuous improvement (H2) and the safe behavior (H12) were identified as dependent variables, implying that they exhibit the weakest influence coupled with highest dependence on any change in the conditions of the system. Last but not the least, the management commitment (H1) was identified as the only dependent variable which deserves lots of attention. This information can be helpful to safety decision-makers, end users, research organizations, and academic institutes who work to reduce the preventable risk factors in mass house construction projects.

Successful thoracic endovascular aortic repair (TEVAR) for type B aortic dissections (TBAD) relies on securing the stentgraft's distal landing zone to prevent retrograde flow and false lumen pressurization. We describe a 60-year-old male with a TBAD undergoing TEVAR with electrocautery-wire fenestration of the dissection septum, providing a controlled method to enhance the distal landing zone for graft placement. Postoperatively, the patient experienced spinal cord ischemia, resolving with the placement of a lumbar drain and increasing mean arterial pressures. Follow-up imaging demonstrated a well-sealed repair without endoleaks. This case highlights the importance of surgical technique selection and postoperative monitoring for complications.

Abstract Background Cancer is a considerable health problem worldwide and the second leading cause of death in children. It has many physical, psychological, and social consequences for children and their families. The ability to adapt to cancer plays a vital role in the recovery and quality of life of affected children. This study aimed to explain the adaptation of children with cancer to their disease. Methods This qualitative study adopted the directed content analysis approach based on the Roy nursing model. The participants were nine children with cancer aged 6–18 years old, five family members, four nurses, one doctor, one teacher, and two charity association members, recruited by purposive sampling method. The information was collected via individual semi-structured interviews, a focus group discussion, and field notes. The data were analyzed simultaneously with data collection using the Elo and Kyngäs method. The study rigor was ensured based on the Guba and Lincoln criteria. Findings Of the four categories of physical challenges, fragile self-concept, the difficulty of role transition, and disruption of the path to independence, the theme of Falling and rising in the cancer vortex was abstracted. Conclusion Based on the Roy model, the children in the present study were at the compensatory level of adaptation. This research demonstrates that the adaptation of children being treated for cancer is fragile and not constant. With each hospitalization and exacerbation of the disease, they made efforts to adapt to their disease using regulatory and cognitive subsystems. Paying attention to different stimulants and the effects of support systems on physical challenges, fragile self-concept, difficult role transition, and disruption of the path to independence for each child, as well as providing individualized care for these children, can help their adaptation to and healthy transition from the vortex of cancer. The Roy adaptation model was helpful and efficient for elucidating the adaptation of children with cancer. Providing care for children by healthcare specialists, especially nurses, should be theory-based and individualized.

Abstract The supervisory control and data acquisition (SCADA) system is one of the key requirements for monitoring the power systems state. The power system state is estimated from the state estimation (SE) function, which uses the measurements results from different points. Inaccurate estimation may cause inefficient management of power systems and making decisions. Usually, the number of measurement units (MUs) is limited so the optimal location of MUs is very important in successful and accurate state estimation. The optimal placement of MUs gets more complicated when the actual state of the power system is uncertain due to the high penetration of renewable generation and load uncertainties. Considering the power system's actual state uncertainty and measurement units’ inherent error, this work presents a probabilistic model for the uncertainty of measuring based on the K‐medoids data clustering technique. Then the probabilistic optimal placement of MUs is solved by a binary particle swarm optimization (PSO) method. Considering the mentioned uncertainties makes the obtained solution robust against all the probable scenarios. The proposed technique is implemented on the IEEE 14‐bus test system and the results are discussed. Results show the effectiveness of the proposed method in improving the accuracy of power system state estimation.

Abstract This study presents a meticulously edited article that assesses the uncertainty associated with contamination dispersion in a porous aquifer environment. The article employs a conceptual model incorporating finite difference flow modeling and particle tracking methods grounded in particle movement theory within groundwater flow. The groundwater flow model utilizes an automatic calibration technique, PES, and independent verification, to minimize statistical discrepancies in the optimized parameters. The statistical summary of the model output reveals the average pollution concentration in the study area, specifically the landfill aquifer in northern Iran. The results indicate that the initial contamination value at the boundaries is zero, whereas 91,802 units are discharged into the sea. Moreover, approximately 3,317,290,003 pollution units have been extracted from pumping wells along the contamination plume over 10 years. Furthermore, the drainage network releases 1,778,680,001 pollution units, resulting in a net outflow of 5,082,740,007 units from the same drainage network. Additionally, 9,912,180,003 units were discharged from the leaky boundaries. It should be noted that these statistics can be reversed depending on the specified period. The difference between the input and output pollution values in the study area indicates a net accumulation of 8192 units. To analyze the sensitivity of contamination dispersion in the porous aquifer environment and the variations in coastal drainage patterns obtained from the finite difference simulation, the explicit and implicit methods, as well as the random walk particle tracking method, were investigated in a portion of the basin area using the South Side Implicit method. One of the main objectives of this sensitivity analysis is to evaluate the results of the uncalibrated qualitative model and examine the particle dispersion in the adjacent area of the contaminated landfill region to assess the error of the proposed model by developing a limited test. The calculated error or computational difference between the explicit and implicit methods and the applet model is considered negligible. For example, the calculated error between the explicit and backward methods and the applet model ranges from 0.086 to 0.372 units for a concentration interval of 0.512 units and 0.680 units for a time interval 0.537. Therefore, the computational differences are not significant. Furthermore, based on theoretical sensitivity analysis of contamination dispersion, the laboratory model demonstrates that the flow direction in this modeling is from west to east, as expected.

Rural tourism, as one of the fundamental elements of development, contributes to the economic, social, cultural, and environmental capabilities of rural areas, and by utilizing its countless potentials, it significantly aids in the sustainable development of rural regions. However, in rural development programs and plans, tourism is often either overlooked or, if considered, solely focused on increasing the number of tourists without taking into account the spatial, historical, social, and geographical characteristics of the villages. This has resulted in existing development initiatives not only failing to significantly increase the number of tourists in rural areas but also causing serious damage to the natural landscape and socio-historical fabric of the villages. This research aims to examine rural tourism from an epistemological perspective and explore its dimensions of development. How can a place-based approach be utilized to optimize the potential of rural tourism, capitalize on available opportunities, and achieve sustainable rural tourism development? The research methodology employed is primarily exploratory, with some library-based components. The findings indicate that adopting a holistic and integrated approach to tourism development, considering the local characteristics of each village, can reduce the negative impacts of fragmentation and compartmentalization. Additionally, an epistemological approach to tourism can contribute to strengthening rural tourism, not only through physical interventions but also by leveraging the cultural, social, and identity capacities of each village. Operational strategies, including the preservation of ecological and natural landmarks, revitalization of local qualities, updating livelihood practices, and establishing local cooperation networks, contribute to improving tourism development and enhancing the spatial capacities of rural areas.

Abstract Background Sepsis, a life-threatening organ dysfunction caused by a host’s dysregulated response to infection with an inflammatory process, becomes a real challenge for the healthcare systems. L-carnitine (LC) has antioxidant and anti-inflammatory properties as in previous studies. Thus, we aimed to determine the effects of LC on inflammation, oxidative stress, and clinical parameters in critically ill septic patients. Methods A randomized double-blinded controlled trial was conducted. A total of 60 patients were randomized to receive LC (3 g/day, n = 30) or placebo (n = 30) for 7 days. Inflammatory and oxidative stress parameters (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), superoxide dismutase (SOD), malondialdehyde (MDA), total antioxidant capacity (TAC), 28-day mortality rate, and some monitoring variables were evaluated. Results There was no statistically significant difference between study arms in baseline characteristics and disease severity scores. CRP (p

Abstract Background Pseudomonas aeruginosa is an opportunistic pathogen in the health-care systems and one of the primary causative agents with high mortality in hospitalized patients, particularly immunocompromised. The limitation of effective antibiotic administration in multidrug-resistant and extensively drug-resistant P. aeruginosa isolates leads to the development of nosocomial infections and health problems. Quorum sensing system contributes to biofilm formation, expression of bacterial virulence factors, and development of drug resistance, causing prolonged patient infections. Therefore, due to the significance of the quorum sensing system in increasing the pathogenicity of P. aeruginosa, the primary objective of our study was to investigate the frequency of quorum sensing genes, as well as the biofilm formation and antibiotic resistance pattern among P. aeruginosa strains. Methods A total of 120 P. aeruginosa isolates were collected from different clinical specimens. The disk diffusion method was applied to detect the antibiotic resistance pattern of P. aeruginosa strains. Also, the microtiter plate method was carried out to evaluate the biofilm-forming ability of isolates. Finally, the frequency of rhlI, rhlR, lasI, and lasR genes was examined by the polymerase chain reaction method. Results In total, 88.3% P. aeruginosa isolates were found to be multidrug-resistant, of which 30.1% had extensively drug-resistant pattern. The highest and lowest resistance rates were found against ceftazidime (75.0%) and ciprofloxacin (46.6%), respectively. Also, 95.8% of isolates were able to produce biofilm, of which 42.5%, 33.3%, and 20.0% had strong, moderate, and weak biofilm patterns, respectively. The frequency of quorum sensing genes among all examined strains was as follows: rhlI (81.6%), rhlR (90.8%), lasI (89.1%), and lasR (78.3%). The most common type of quorum sensing genes among multidrug-resistant isolates were related to rhlR and lasI genes with 94.3%. Furthermore, rhlI, rhlR, and lasI genes were positive for all extensively drug-resistant isolates. However, the lasR gene had the lowest frequency among both multidrug-resistant (83.0%) and extensively drug-resistant (90.6%) isolates. Moreover, rhlR (94.7%) and lasR (81.7%) genes had the highest and lowest prevalence among biofilm-forming isolates, respectively. Conclusion Our findings disclosed the significantly high prevalence of drug resistance among P. aeruginosa isolates. Also, the quorum sensing system had a significant correlation with biofilm formation and drug resistance, indicating the essential role of this system in the emergence of nosocomial infections caused by P. aeruginosa.

Background: Escherichia coli consists of a wide range of strains with huge diversity in their genome, distributed in nature and the alimentary tracts of animals and humans. This study analyzed the phylogenetic group determination and genetic diversity of E. coli strains isolated from domestic animals and human clinical samples. Methods: Twenty E. coli isolates from domestic animals were analyzed for phylogenetic grouping. Also, 100 clinical samples and 20 animal samples were evaluated by the enterobacterial repetitive intergenic consensus–polymerase chain reaction (ERIC-PCR) technique. The results and the similarity between the strains were determined based on the Dice similarity coefficient in the SAHN program of the NTSYS-pc software. Results: The frequency of phylogroups among animal samples were A = 5%, B1 = 65%, B2 = 20%, and D = 10%. Based on the ERIC-PCR results, the clinical strains were allocated into 19 clusters. Most strains were in the E7 cluster. Fifty percent of the E. coli isolated from animal specimens belonged to the E4 group, and the lowest number of strains was in the E3 and E5 (1 strain) groups. Conclusion: The results confirmed the efficiency and usefulness of the ERIC-PCR tool for the identification and classification of bacteria. Also, we demonstrated the most phylogroup among animal samples.

Background and aimsAllergic asthma has a considerable burden on the quality of life. A significant portion of moderate-to-severe allergic asthma patients need omalizumab, an anti-immunoglobulin-E monoclonal antibody, as an add-on therapy. In this phase III clinical trial P043 (Zerafil®, CinnaGen, Iran) efficacy, safety, and immunogenicity were compared with Xolair® (the originator omalizumab). The primary outcome was the rate of protocol-defined asthma exacerbations.MethodsExacerbation rates, Asthma Control Test (ACT) results, spirometry measurements, immunogenicity, and safety were evaluated. Each subject received either medication with a dose ranging from 150 to 375 mg based on pre-treatment serum total IgE level (IU/mL) and body weight (kg) every two or four weeks for a duration of 28 weeks.ResultsExacerbation rates were 0.150 (CI: 0.079-0.220) in the P043 group, and 0.190 (CI: 0.110-0.270) in the omalizumab group (per-protocol). The least squares mean differences of predicted Forced Expiratory Volume in the First second (FEV1) were -2.51% (CI: -7.17-2.15, P=0.29) and -3.87% (CI: -8.79-1.04, P=0.12), pre- and post-bronchodilator use. The mean ± SD of ACT scores at the screening and the last visit were 10.62 ± 2.93 and 20.93 ± 4.26 in P043 and 11.09 ± 2.75 and 20.46 ± 5.11 in the omalizumab group. A total of 288 adverse events were reported for the 256 enrolled participants. Among all, “dyspnea” and “headache” were the most reported ones. The overall incidence of adverse events (P=0.62) and serious adverse events (P=0.07) had no significant differences between the two groups. None of the samples were positive for anti-drug antibodies.ConclusionP043 was equivalent to omalizumab in the management of asthma in reduction of exacerbations. There was no significant difference in other efficacy and safety parameters.Clinical trial registrationwww.clinicaltrials.gov (NCT05813470) and www.IRCT.ir (IRCT20150303021315N20).

We present a case of a symptomatic, giant, left upper back arteriovenous malformation that was treated through a staged endovascular and open approach. Through a series of embolizations, followed by resection, we were able to preserve the limb and upper back neurovascular supply, demonstrating an approach to preserve sensation and function and improving quality of life.

Career selection is one of the most important decisions every person faces in their life. Finding the right career path can be a complicated task, particularly in choosing careers with similarly required proficiencies. One of the critical factors affecting a person's career success is their personality, and taking account of this factor is of paramount importance. This study uses the NEO-FFI questionnaire to find personality patterns of software engineering and data science experts based on the Big Five personality traits: Neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. Afterward, an ANFIS (Adaptive Network-Based Inference System) is conducted using the experts' personality data to match the participants of these fields with their corresponding choices. This study demonstrated that data scientists and software engineers score higher in conscientiousness and agreeableness, respectively. Also, data experts have higher scores in all traits overall. In the end, the ANFIS is tested with another similar dataset and the prediction accuracy of the model is measured.

The global incidence of cancer is increasing, with estimates suggesting that there will be 26 million new cases and 17 million deaths per year by 2030. Cancer stem cells (CSCs) and extracellular vesicles (EVs) are key to the resistance and advancement of cancer. They play a crucial role in tumor dynamics and resistance to therapy. CSCs, initially discovered in acute myeloid leukemia, are well-known for their involvement in tumor initiation, progression, and relapse, mostly because of their distinct characteristics, such as resistance to drugs and the ability to self-renew. EVs, which include exosomes, microvesicles, and apoptotic bodies, play a vital role in facilitating communication between cells within the tumor microenvironment (TME). They have a significant impact on cellular behaviors and contribute to genetic and epigenetic changes. This paper analyzes the mutually beneficial association between CSCs and EVs, emphasizing their role in promoting tumor spread and developing resistance mechanisms. This review aims to investigate the interaction between these entities in order to discover new approaches for attacking the complex machinery of cancer cells. It highlights the significance of CSCs and EVs as crucial targets in the advancement of novel cancer treatments, which helps stimulate additional research, promote progress in ideas for cancer treatment, and provide renewed optimism in the effort to reduce the burden of cancer.

Turmeric is a medicinal herb with antioxidant and anti-inflammatory components that can affect metabolic parameters via various pathways, including the gut-brain axis. Although positive effects of turmeric on health have been reported, findings are conflicting. Accordingly, the current review aimed to provide an overview of the biochemical and biological characteristics of turmeric and examine the impacts of turmeric on cardio-metabolic risk factors with a special focus on its abilities to modulate gut microbiota. In the present comprehensive review, findings of systematic reviews/narrative reviews, clinical trials, animal, and in vitro studies on turmeric in the English language published between 2010 and March 2023 were summarized. Findings revealed that turmeric is a safe medicinal herb with mild gastrointestinal side effects in some cases. It can help improve the glycemic status, lipid profile, and blood pressure. However, food processing and fermentation can affect the bioavailability of its effective components, including curcumin. Several mechanisms, including those affecting intestinal microbiota diversity, intestinal permeability, inflammatory and oxidative pathways, are proposed for their positive effects on metabolic factors. However, due to high between-study heterogeneity, limited high-quality clinical trials, differences in the duration of the intervention, and the form of turmeric supplement, more studies on each metabolic parameter are needed to determine effective dosages and confirm its efficacy as a complementary therapy to modulate microbiota and cardio-metabolic parameters.

Background: The interaction between commensal bacteria and the host is essential for health and the gut microbiota-brain axis plays a vital role in this regard. Obesity as a medical problem not only affect the health of the individuals, but also the economic and social aspects of communities. The presence of any dysbiosis in the composition of the gut microbiota disrupts in the gut microbiota-brain axis, which in turn leads to an increase in appetite and then obesity. Because common treatments for obesity have several drawbacks, the use of microbiota-based therapy in addition to treatment and prevention of obesity can have other numerous benefits for the individual. In this review, we intend to investigate the relationship between obesity and the gut microbiota-brain axis as well as novel treatment strategies based on this axis with an emphasis on gut microbiota.

Abstract Background The prevalence of Type 2 Diabetes Mellitus (T2DM) in the North Africa and Middle East region is alarmingly high, prompting us to investigate the burden and factors contributing to it through the GBD study. Additionally, there is a lack of knowledge about the epidemiological status of T2DM in this region, so our aim is to provide a comprehensive overview of the burden of T2DM and its associated risk factors. Methods Using data from the 2019 Global Burden of Disease Study, we calculated the attributable burden of T2DM for each of the 21 countries in the region for the years 1990 and 2019. This included prevalence, mortality, disability-adjusted life years (DALYs), and risk factors. Results Between 1990 and 2019, there was a significant increase in the age-standardized incidence (79.6%; 95% Uncertainty Interval: 75.0 to 84.5) and prevalence (85.5%; [80.8 to 90.3]) rates of T2DM per 100,000 populations. The age-standardized mortality rate (1.7%; [-10.4 to 14.9]), DALYs (31.2%; [18.3 to 42.2]), and years lived with disability (YLDs) (82.6%; [77.2 to 88.1]) also increased during this period. Modifiable risk factors, such as high body mass index (56.4%; [42.8 to 69.8]), low physical activity (15.5%; [9.0 to 22.8]), and ambient particulate matter pollution (20.9%; [15.2 to 26.2]), were the main contributors to the number of deaths. Conclusion The burden of T2DM, in terms of mortality, DALYs, and YLDs, continues to rise in the region. The incidence rate of T2DM has increased in many areas. The burden of T2DM attributed to modifiable risk factors continues to grow in most countries. Targeting these modifiable risk factors could effectively reduce the growth and disease burden of T2DM in the region.

Given the nature of time series and their vast applications, it is essential to find clustering algorithms that depict their real-life properties. Among the features that can hugely effect the options available for time series are overlapping and hierarchical properties. In this paper a novel approach to analyze time series with such features is introduced. Using the two concepts of network construction and link community detection, we have attempted to analyze and identify the mentioned properties of time series using data that is often gathered first hand. The proposed algorithm has been applied using both recent and common similarity measures on ten synthetic time series with hierarchal and overlapping features, alongside various distance measures. When testing the proposed approach, the element-centric measure of similarity indicated a clear increased accuracy for this algorithm, showing the highest accuracy when used alongside the Dynamic Time Warping distance measure. Moreover, the proposed algorithm has been very successful in identifying and forming communities for both large and small time series, thus solving another one of the main issues previous algorithms tended to have.

Financial distress prediction is crucial to financial planning, particularly amid emerging uncertainties. This study introduces a novel methodology for predicting financial distress by amalgamating network analysis and machine learning techniques. The approach involves establishing two company networks based on their similarity and correlation in crucial financial indicators. The first network reflects similarity across five features, while the second captures correlation in the most critical feature. Subsequently, seven network-centric features are extracted and integrated into the dataset as new variables. Community detection algorithms are also applied to cluster companies, with the resulting labels added as categorical variables. This process yields a modified dataset comprising both initial and network-based variables. Five classification algorithms are employed to forecast financial distress across three scenarios. Initially, models are trained using only the initial features. In subsequent scenarios, network-centric features from similarity and correlation networks are incorporated, enhancing the predictive accuracy of machine learning models. Notably, features from the similarity network play a pivotal role in this improvement. The proposed model showcases superior predictive capabilities and offers a holistic understanding of the dynamic interactions among financial entities. The results underscore the efficacy of network-based strategies in refining financial distress prediction models, providing valuable insights for decision-makers.

Customer churn is a significant concern for firms due to the high cost of acquiring new customers. The expenditure related to developing new consumers surpasses that of customer retention. Customer churn prediction models were given to analyze the impact of this problem on organizations’ revenues. These models primarily utilize machine learning algorithms to predict outcomes using data from demographic factors and customer service information components. This study investigates the impact of social relationships on customer churn probability and evaluates the performance of machine learning methods after introducing a new concept called the conformity factor. To improve the performance of standard machine learning models, we performed feature engineering by leveraging phone call network data and developing influence and conformity metrics. These metrics capture the social connections of individuals within the network. We employed various machine learning classification approaches and evaluated their performance using standard measures like AUC, accuracy, precision, F1-score, MCC, Cohen’s kappa, and Brier score. The experiments demonstrated that incorporating these social network variables, particularly the proposed influence and conformity indices, significantly enhanced the performance of all churn prediction models developed in this study. Among the tested approaches, the gradient boosting model achieved the highest level of performance.

The quest for content differentiation and audience engagement is paramount in the competitive landscape of Video-on-Demand (VOD) platforms. The paper proposes a system investigating VOD platform series data to recommend actors and themes to VOD vendors. To achieve this, we analyzed the storylines of 72 VOD movie series to extract keywords. Using these keywords, we generated a network using Social Network Analysis. By examining the network, we identified content gaps. After semantic clustering, we utilized TOPSIS to determine keywords with high centrality scores. These are a foundation for connecting peripheral keywords and generating new content. The paper’s findings suggest that the proposed content-based recommender system can help VOD platforms create innovative storylines by leveraging structural holes in a TV series content keyword graph. The study also suggests that successful directors take risks by combining different keywords from various network parts. This approach allows them to attract a wider range of audiences and increase their chances of success.

This paper addresses the imperative task of assessing and ranking cryptocurrencies, particularly pertinent in the context of the burgeoning popularity of public blockchains. The proliferation of available options necessitates a rigorous evaluation, prompting the formulation of a novel model grounded in both objective and subjective criteria. To contend with the challenge posed by the expanding landscape of public blockchains, ten discerning criteria are delineated, encompassing facets such as Technology, TPS, Market capitalization, GitHub fork, GitHub stars, Twitter followers, Twitter hashtags, trading volume, sentiment score, and the price range differential. Leveraging expert opinions, the pairwise impact of these criteria is ascertained, and the DEMATEL method is judiciously employed to derive their respective weights. Subsequently, the PROMETHEE method is harnessed to effectuate the ranking of 20 cryptocurrencies predicated on the identified criteria. Furthermore, the integration of LSTM enables the prediction of values for four predictable criteria, seamlessly incorporated into the PROMETHEE model to furnish rankings across diverse temporal intervals. The proposed model, thus, presents a holistic and pragmatic approach to inform investment decision-making within the dynamic cryptocurrency market. By embracing a comprehensive set of criteria and integrating predictive analytics, this model stands as a valuable contribution to the field, offering nuanced insights to stakeholders navigating the complexities of cryptocurrency investment.

The growing use of online social networks has intensified the focus on customer lifetime value (CLV), particularly how social conformity and influence shape purchasing decisions. Traditional CLV models have primarily captured positive interactions within networks but have largely ignored negative influences and the degree of customer susceptibility to conformity. These oversights limit the effectiveness of existing models when applied to real-world datasets. This paper introduces a novel model that enhances the estimation of CLV by incorporating both positive and negative interactions through sentiment analysis. The model leverages the CASINO algorithm to integrate social influence and conformity extracted from interpersonal interactions, building upon the foundational customer lifetime network value model. We validate our model using both semi-simulated and real-world data, comparing its performance with traditional approaches and illustrating its improved capacity to reflect complex social dynamics in CLV calculations.

The study of Music Recommender Systems (MRS) has become crucial in digital music consumption, influencing how people discover and interact with music. This comprehensive analysis examines the complex field of MRS research from 2005 to 2023; with the growing importance of music recommendation systems in enhancing user experience, it is crucial to comprehend their development. By utilising rigorous social network analysis, statistical measures, and factor analysis, our investigation not only identifies essential themes and influential contributors but also emphasises the complex and diverse nature of MRS. The field trend significantly increased between 2017 and 2021, with periodic oscillations highlighting its dynamic nature. This analysis offers a broad perspective by examining highly cited articles, current researchers, and local sources. Factorial analysis uncovers thematic clusters, highlighting collaborative filtering, user experience, emotion identification, and reinforcement learning. A scientific mapping analysis classifies research themes in different historical periods, focusing on essential areas such as collaborative filtering, hybrid recommendation, sentiment analysis, and emotion identification. A review of thematic evolution highlights the importance of digitalisation, emotion recognition, personalisation, user experience, and collaborative filtering in determining future research directions. Although there has been a recent decrease in general interest, investigating context-aware models and hybrid techniques offers encouraging opportunities for further inquiry. This research enhances our comprehension of MRS dynamics, leading to future improvements and developments in the field. Ultimately, it improves the music discovery experience for people globally.