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The IL-2/STAT5 Axis Activates Treg Cells and Suppresses the Pregnancy-Harmful Pro-Inflammatory Response of Th17 Cells in Mice
No full textBackground: Recurrent pregnancy loss (RPL) is characterized by multiple miscarriages before reaching 20 weeks of pregnancy, affecting women in their reproductive age. This study aimed to investigate the effect of interleukin-2 (IL-2)/signal transducer and activator of transcription 5 (STAT5) axis on the RPL and its association with regulatory T cells (Treg) and T helper cells (Th) 17. Methods: The female CBA/J mice were used to construct the RPL model by mating with male DBA/2 mice. The IL-2 administration was used to increase the IL-2 content and activate IL-2/STAT5 axis, and the IL-2 plus STAT5 inhibitor was used to increase the IL-2 content while at the same time inactivate the IL-2/STAT5 axis. Moreover, the resorption rate in mice was evaluated. In the mice uteri tissues and intracardiac blood sera, the levels of IL-2 and phosphorylated (p)-STAT5/STAT5, as well as levels of cytokines and factors related to Treg and Th17 cells were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot analysis. The inflammation associated cytokines in intracardiac blood sera were detected. Additionally, the peripheral blood mononuclear cells (PBMCs) and the single-cell suspension of the spleen tissues were prepared and analyzed employing flow cytometry to determine the percentage of both Th17 cells and Treg cells. Results: IL-2 treatment increased the activation of the IL-2/STAT5 axis and decreased resorption in mice (p < 0.01), which was reversed following STAT5 inhibitor (p < 0.01). Moreover, the IL-2-induced IL-2/STAT5 axis activation promoted the expression of forkhead box P3 (Foxp3), IL-10, and transforming growth factor-beta (TGF-β) while decreasing the expression of retinoid-related orphan receptor gamma t (RORγt), IL-17 and IL-22 (p < 0.001), which was reversed by STAT5 inhibitor (p < 0.001). Furthermore, the activation of the IL-2/STAT5 axis decreased the levels of IL-1β, tumour necrosis factor-alpha (TNF-α), IL-6, Interferon-gamma (IFN-γ), granulocyte-macrophage colony-stimulating factor (GM-CSF), and Th17 percentage while increasing the levels of IL-4 and Treg percentage (p < 0.001), which was reversed with the disruption of the IL-2/STAT5 axis activation (p < 0.01). Conclusion: The IL-2/STAT5 axis can activate Treg cells while suppressing the Th17 cells and their associated inflammatory response, leading to effects on protecting pregnant mice from miscarriage. Thus, the IL-2/STAT5 axis could be a potential therapeutic target for treating RPL
Weighted Gene Co-Expression Network Analysis Identifies an Immunogenic Cell Death Signature to Predict Therapeutic Responses and Prognosis of Glioblastoma
Get PDFBackground: Induction of immunogenic cell death (ICD) breaks down the immunosuppressive tumor microenvironment (TME) and controls tumor progression, but the correlation between glioblastoma (GBM) and ICD is unclear. Therefore, this study aims to investigate the potential prognostic value of ICD-associated genes in GBM. Methods: We collected 34 ICD-related genes from various sources. Utilizing public databases, we extracted relevant GBM data and delineated prognosis-related ICD gene modules using weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) algorithm was employed to develop a risk model, whose accuracy was confirmed by including an independent Gene Expression Omnibus (GEO) dataset. The biological functions and pathways associated with these signals were analyzed by performing enrichment analysis, and the tumor immune infiltration capacity was evaluated. The R package oncoPredict was used to infer the drug sensitivity of patients in different risk groups using data from the Genomics of Drug Sensitivity in Cancer 2 (GDSC2) database with expression profiling. Results: Thirty-four ICD-associated genes were differentially expressed in GBM samples and two gene modules significantly associated with prognosis were identified. Based on these gene modules, vitamin D receptor (VDR) and cell death-inducing DFF45-like effector B (CIDEB) were identified as two signature genes for the prognostic prediction of GBM. Subsequently, multivariate Cox analysis confirmed the validity of this signature as an independent factor for evaluating overall survival in GBM. Receiver operating characteristic (ROC) curves also supported an effective prediction of the signature (1-year area under the ROC curve (AUC): 0.667; 3-year AUC: 0.727; 5-year AUC: 0.762). We observed that the high-risk group had higher immune cell infiltration and sensitivity to some drugs. Conclusions: This work developed a novel ICD-related prognostic model for GBM patients. Our findings highlight the potential of using ICD as a promising prognosis indicator in GBM, contributing to the current understanding of the intricate interplay between ICD and tumor microenvironment
A review on phenomenon of adsorption of inorganic materials: Applications in veterinary pharmacology and material sciences
Get PDFThe inorganic compound water is the second most essential component after air for perpetuation of life on the planet. Due to human activity the natural resources of water are highly contaminated. The water reservoir contains physical, chemical and biological impurities. Sometimes radioactive materials are discharged in the reservoir creating even hazardous effects. The presence of heavy metal ions in drinkable water is a major global challenge for mankind. The heavy inorganic materials can be separated using various techniques. With the advancement in modern science, nanomaterials are found to be effective in removal of heavy inorganic materials from water. As surface area at nanoscale increases, the possibility of adsorption also gets increased. Therefore, nanomaterials are being effectively used in water purification techniques. In this review, we will focus on how heavy metals with negative environmental consequences may be removed using an adsorption approach that involves iron oxide nanoparticles, which has been shown to be effective in this field. We are going to discuss several methods along with current advancements in iron oxide nanoparticles for heavy metal ion adsorption from water. The discussion covers candidate synthesis of iron oxide nanoparticles, mechanisms that enable the applications, advantages, and limitations as compared to existing processes and their adsorption mechanism. Apart from this we have focused the application of adsorption particularly in veterinary science and animal husbandry. The phenomenon of adsorption is of paramount importance in veterinary pharmacology as well
Seasonal dynamics of trace metal concentrations and hydrological parameters in the Balu River, Bangladesh
Get PDFThis study investigates the seasonal dynamics of trace metal concentrations and hydrological parameters in the Balu River, Bangladesh, to inform sustainable water management practices and support efforts to meet environmental standards. Water level, discharge, rainfall, and groundwater data were collected from February to August 2019, alongside sediment samples analyzed for Zn, Pb, Cu, Cr, and Cd concentrations. Statistical analyses, including correlation studies, time series modeling, and Kolmogorov-Smirnov tests, revealed significant seasonal variations. Water levels and discharge rates increased dramatically during the wet season, with upstream levels rising by 862.68% and downstream by 752.92%. Trace metal concentrations showed diverse responses: Cd increased by 554.52%, Zn by 11.86%, while Pb, Cu, and Cr decreased by 44.15%, 5.16%, and 32.72% respectively. Strong correlations were observed between certain metals, particularly in wet periods, with notable relationships between Cr-Cu (r = 0.870) and Zn-Cr (r = 0.742). The integrated analysis of hydrological parameters and metal concentrations, using Gower coefficient and partial correlation analyses, suggests complex interactions between seasonal changes and pollutant dynamics. These findings highlight the need for season-specific water management strategies and more frequent monitoring to better understand and mitigate the environmental impacts of trace metal pollution in the Balu River system. The study contributes valuable insights into the interplay between hydrological conditions and trace metal behavior, essential for developing effective pollution control measures and achieving sustainable river management in Bangladesh
Enhancing IoNT performance with fog computing: A hybrid architecture for real-time data processing
Get PDFThe rapid evolution of the Internet of Nano Things (IoNT) and Fog Computing presents new opportunities for creating advanced smart systems that are both efficient and responsive. Integrating IoNT with Fog Computing offers a powerful paradigm that can address the limitations of cloud-centric architectures, particularly in terms of latency, bandwidth, and real-time processing. The paper explores the synergistic combination of IoNT and Fog Computing, focusing on the development of a hybrid architecture that leverages the proximity and computational capabilities of fog nodes to process data generated by nanoscale devices. Key challenges such as resource management, data processing efficiency, and security concerns are addressed in this study. The proposed architecture not only enhances the performance of smart systems by reducing latency and optimizing resource utilization but also ensures robust security and privacy for the vast amounts of generated data. A comprehensive dataset was generated to assess the integration of the IoNT with Fog Computing, focusing on environmental parameters such as Temperature, Humidity, and Wind Speed, as collected from five IoNT sensors. Python was employed to generate and augment this dataset, ensuring the accurate representation of varied environmental conditions. The data transmission between IoNT sensors and FogNode_1 successfully demonstrated the framework’s ability to capture and process real-time environmental information. Aggregated data from the fog and cloud layers confirmed the system’s efficiency in reducing latency and maintaining data integrity. Furthermore, the implementation of advanced communication protocols and effective resource management highlights the robustness of the integration, contributing to the real-time monitoring and decision-making processes in environmental applications. As well as, this study compares Fog Computing and Cloud Computing, concluding that Fog Computing offers significant advantages in areas like latency, bandwidth utilization, resource efficiency, security, privacy, real-time processing, and edge intelligence. These benefits make Fog Computing particularly suitable for applications requiring low latency, local data processing, enhanced security, and the ability to leverage edge intelligence. While Cloud Computing may have advantages in certain areas, Fog Computing’s overall performance and versatility make it a compelling choice for those seeking to optimize their computing infrastructure. This research aims to pave the way for more resilient and intelligent smart systems that can operate effectively in various domains, including healthcare, environmental monitoring, and industrial automation
A new modification to the A* path-finding algorithm to improve its space and time performance
Get PDFWe propose a new modification to the A* algorithm named AA* that significantly improves space and time complexities. In AA*’s forward pass, the node sets (open and closed) are not used, and only the local node neighborhood is saved to take the next move decision. AA* needs a backward pass to bridge and correct gaps and bad decisions made in the forward pass. The work of the backward pass is far less than that of the forward pass, as most of the task has been done. It is shown via empirical experimental work that our proposed AA* algorithm is superior to the classical A* algorithm in the typical three metrics: running time, number of probed nodes, and length of path. Furthermore, our experimental work showed that AA* is suboptimal in terms of length of path compared to the original Dijkstra’s algorithm with an accuracy of 96.95%
Harnessing big data analytics to promote marketing strategies: A comprehensive literature review
Get PDFThe critical role that big data analytics (BDA) plays for enhancing marketing strategies in a variety of industries is examined in this article. Gaining a competitive edge now requires integrating BDA into marketing frameworks since businesses depend more and more on data-driven decision-making. The literature on BDA applications in marketing is summarized in this review, which also looks at how data collection, processing, and analysis affect customer insights, segmentation, personalization, and campaign efficacy. A thorough systematic literature review (SLR) was conducted, and PRISMA was used for a thorough sample selection process. Out of the 150 articles that were initially found, 100 were eliminated since they did not fit the inclusion requirements. Ultimately, 50 publications that satisfied the inclusion requirements were used in this study. According to the results, BDA helps marketers to increase customer engagement (30%), optimize resource allocation (34%), and improve return on investment (ROI) (36%), which is thought to be the most significant contributor through targeted strategies. The review also identifies obstacles, such as the requirement for qualified staff and data protection issues, and makes suggestions for future research directions. All things considered, this study emphasizes how big data analytics can revolutionize contemporary marketing tactics
Study on Ecological Restoration of Arid City under Land Spatial Planning System: A Case Study of Lanzhou
Get PDFDue to the rapid development of cities and the acceleration of urbanization in recent years, various policies and plans have been issued one after another. However, many plans are not coordinated and unified, which leads to the imbalance of urban development. Therefore, urban and rural planning begins to transform to land and space planning. The new land space planning system has made a series of important instructions for urban restoration and urban ecological restoration. The “double repair” of the city has become an urgent problem to be solved. At the same time, the “double evaluation” system is used to measure the current situation of urban development. This paper deeply explores the ecological restoration of arid city under the land space planning system of Lanzhou as an example. Through the ecological restoration concept put forward under the land space planning system, at the same time, learning from the new evaluation system, it puts forward new solutions for the urban ecological restoration of Lanzhou. From the perspective of sustainable development, the mode of urban ecological restoration in Lanzhou is deeply optimized, and the new direction of urban development in Lanzhou is deeply adjusted from three aspects of mountain, water and urban overall development
Construction of Long-span Tensioned Membrane Structure in Zhijiang Feihu Sports Center
Get PDFBased on the author's experience in the past twenty years, this paper describes the construction process of membrane structure of space structure. The construction process of long-span tensioned membrane structure is mainly introduced
A study of cardio vascular disease prediction and optimization of health care data
Get PDFCardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, accounting for a significant proportion of healthcare costs. It is a major public health concern, and early detection and prevention are critical for reducing its burden. Several risk factors for CVD have been identified, including age, sex, genetics, hypertension, diabetes, smoking, and physical inactivity. Despite the identification of several risk factors, there is still a lack of understanding of the underlying mechanisms that contribute to CVD development. This study aimed to investigate the potential predictors of CVD and identify novel biomarkers that could be used for early detection and prevention. The study explored pre-processing strategies, including Synthetic Minority Oversampling Technique (SMOTE), Z-Score Normalization, and Adaptive Synthetic Sampling (ADASYN), to address class imbalance and enhance model performance. The dataset consisted of medical images labeled with different cardiovascular diseases. By integrating the strengths of Support Vector Machines (SVM) classification and Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and PCA with ReliefF feature retrieval methods, the study investigated various feature extraction approaches for classifying cardiovascular diseases