Kinneyfinn6719

This study aimed to analyze the potential genes associated with immune cell infiltration in atherosclerosis (AS).

Gene expression profile data (GSE57691) of human arterial tissue samples were downloaded, and differentially expressed RNAs (DERNAs; long-noncoding RNA [lncRNAs], microRNAs [miRNAs], and messenger RNAs [mRNAs]) in AS vs. control groups were selected. Based on genome-wide expression levels, the proportion of infiltrating immune cells in each sample was assessed. Genes associated with immune infiltration were selected, and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, a competing endogenous RNA (ceRNA) network was constructed, and the genes in the network were subjected to functional analyses.

A total of 1749 DERNAs meeting the thresholds were screened, including 1673 DEmRNAs, 63 DElncRNAs, and 13 DEmiRNAs. The proportions of B cells, CD4+ T cells, and CD8+ T cells were significantly different between the two groups. In total, 341 immune-associated genes such as HBB, FCN1, IL1B, CXCL8, RPS27A, CCN3, CTSZ, and SERPINA3 were obtained that were enriched in 70 significantly related GO biological processes (such as immune response) and 15 KEGG pathways (such as chemokine signaling pathway). A ceRNA network, including 33 lncRNAs, 11 miRNAs, and 216 mRNAs, was established.

Genes such as FCN1, IL1B, and SERPINA3 may be involved in immune cell infiltration and may play important roles in AS progression via ceRNA regulation.

Genes such as FCN1, IL1B, and SERPINA3 may be involved in immune cell infiltration and may play important roles in AS progression via ceRNA regulation.Supply chain management is the basis for the execution of operations, being considered as the core of the business function in the 21st century. On the other hand, at present, factors such as the reduction of natural resources, the search for competitive advantages, government laws and global agreements, have generated a greater interest in the sustainable development, which, in order to achieve it, industries need to rethink and plan their supply chain considering a path of sustainability. read more So sustainable supply chain management emerges as a means to integrate stakeholders' concern for profit and cost reduction with environmental and social requirements, attracting significant interest among managers, researchers and practitioners. The main objective of this study is to provide a synthesis of the key elements of the quantitative model offerings that use sustainability indicators in the design and management of forward supply chains. To achieve this objective, we developed a systematic literature review that includes seventy articles published during the last decade in peer-reviewed journals in English language. In addition a 4 W's analysis (When, Who, What, and Where) is applied and three structural dimensions are defined and grouped by categories Supply chain management, modeling and sustainability. As part of the results we evidenced a continuous growth in the scientific production of this type of articles, with a predominance of deterministic mathematical programming models with an environmental economic perspective. Finally, we identified research gaps, highlighting the lack of integral inclusion of a life cycle analysis in the design of supply chain networks.In order to treat the diseases caused by hepatitis C virus (HCV) more efficiently, the concentration of HCV in blood, cells, tissues and the body has attracted widespread attention from related scholars. This paper studies a dynamic dependent HCV model (more specifically, including age structure and treatment methods model) that concludes states of infection-free and infected equilibrium. Through eigenvalue analysis and Volterra integral formula, it proves that $E_0$ is globally asymptotically stable when $\mathcalR1$ by constructing a suitable Lyapunov function. Through the above proofs, it can be concluded that effective treatment measures can significantly reduce the number of HCVs, so many related researchers are aware of the importance of highly efficient nursing methods and are committed to applying relevant methods to practice.We present the Progression and Transmission of HIV (PATH 4.0), a simulation tool for analyses of cluster detection and intervention strategies. Molecular clusters are groups of HIV infections that are genetically similar, indicating rapid HIV transmission where HIV prevention resources are needed to improve health outcomes and prevent new infections. PATH 4.0 was constructed using a newly developed agent-based evolving network modeling (ABENM) technique and evolving contact network algorithm (ECNA) for generating scale-free networks. ABENM and ECNA were developed to facilitate simulation of transmission networks for low-prevalence diseases, such as HIV, which creates computational challenges for current network simulation techniques. Simulating transmission networks is essential for studying network dynamics, including clusters. We validated PATH 4.0 by comparing simulated projections of HIV diagnoses with estimates from the National HIV Surveillance System (NHSS) for 2010-2017. We also applied a cluster geneailable to assess cluster detection and response at the national-level and could help inform the national strategic plan.In this paper we describe a coupled model for flow and microbial growth as well as nutrient utilization. These processes occur within and outside the biofilm phase formed by the microbes. The primary challenge is to address the volume constraint of maximum cell density but also to allow some microbial presence outside the contiguous biofilm phase. Our model derives from the continuum analogues of the mechanism of cell shoving introduced in discrete biomass models, and in particular from the models exploiting singular diffusivity as well as from models of variational inequality type which impose explicit constraints. We blend these approaches and propose a new idea to adapt the magnitude of the diffusivity automatically so as to ensure the volume constraint without affecting the reactions; this construction can be implemented in many variants without deteriorating the overall efficiency. The second challenge is to account for the flow and transport in the bulk fluid phase adjacent to the biofilm phase. We use the Brinkman flow model with a spatially variable permeability depending on biomass amount. The fluid flow allows some advection of the nutrient within the biofilm phase as well as for the flow even when the pores are close to being plugged up. Our entire model is monolithic and computationally robust even in complex pore-scale geometries, and extends to multiple species. We provide illustrations of our model and of related approaches. The results of the model can be easily post-processed to provide Darcy scale properties of the porous medium, e.g., one can predict how the permeability changes depending on the biomass growth in many realistic scenarios.Gliomas are common malignant tumors of the central nervous system. Despite the surgical resection and postoperative radiotherapy and chemotherapy, the prognosis of glioma remains poor. Therefore, it is important to reveal the molecular mechanisms that promotes glioma progression. Microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to identify 428 differentially expressed genes (DEGs) and a core module from three microarray datasets. Heat maps were drawn based on DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database. The core module was significantly involved in several KEGG pathways, such as "cell cycle", "viral carcinogenesis", "progesterone-mediated oocyte maturation", "p53 signaling pathway". The protein-protein interaction (PPI) networks and modules were built using the STRING database and the MCODE plugin, respectively, which were visualized using Cytoscape software. Identification of hub genes in the core module using the CytoHubba plugin. The top modular genes AURKA, CDC20, CDK1, CENPF, and TOP2A were associated with glioma development and prognosis. In the Human Protein Atlas (HPA) database, CDC20, CENPF and TOP2A have significant protein expression. Univariate and multivariate cox regression analysis showed that only CENPF had independent influencing factors in the CGGA database. GSEA analysis found that CENPF was significantly enriched in the cell cycle, P53 signaling pathway, MAPK signaling pathway, DNA replication, spliceosome, ubiquitin-mediated proteolysis, focal adhesion, pathway in cancer, glioma, which was highly consistent with previous studies. Our study revealed a core module that was highly correlated with glioma development. The key gene CENPF and signaling pathways were identified through a series of bioinformatics analysis. CENPF was identified as a candidate biomarker molecule.Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.We propose an uncertainty propagation study and a sensitivity analysis with the Ocular Mathematical Virtual Simulator, a computational and mathematical model that predicts the hemodynamics and biomechanics within the human eye. In this contribution, we focus on the effect of intraocular pressure, retrolaminar tissue pressure and systemic blood pressure on the ocular posterior tissue vasculature. The combination of a physically-based model with experiments-based stochastic input allows us to gain a better understanding of the physiological system, accounting both for the driving mechanisms and the data variability.