Immune-related potential biomarkers and therapeutic targets in coronary artery disease

BackgroundCoronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.MethodsFirst, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.ResultsWe identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.ConclusionWe created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease

Controversial role of ILC3s in intestinal diseases: A novelty perspective on immunotherapy

ILC3s have been identified as crucial immune regulators that play a role in maintaining host homeostasis and modulating the antitumor response. Emerging evidence supports the idea that LTi cells play an important role in initiating lymphoid tissue development, while other ILC3s can promote host defense and orchestrate adaptive immunity, mainly through the secretion of specific cytokines and crosstalk with other immune cells or tissues. Additionally, dysregulation of ILC3-mediated overexpression of cytokines, changes in subset abundance, and conversion toward other ILC subsets are closely linked with the occurrence of tumors and inflammatory diseases. Regulation of ILC3 cytokines, ILC conversion and LTi-induced TLSs may be a novel strategy for treating tumors and intestinal or extraintestinal inflammatory diseases. Herein, we discuss the development of ILCs, the biology of ILC3s, ILC plasticity, the correlation of ILC3s and adaptive immunity, crosstalk with the intestinal microenvironment, controversial roles of ILC3s in intestinal diseases and potential applications for treatment

Near-atomic, non-icosahedrally averaged structure of giant virus Paramecium bursaria chlorella virus 1

Giant viruses are a large group of viruses that infect many eukaryotes. Although components that do not obey the overall icosahedral symmetry of their capsids have been observed and found to play critical roles in the viral life cycles, identities and high-resolution structures of these components remain unknown. Here, by determining a near-atomic-resolution, five-fold averaged structure of Parameciumbursaria chlorella virus 1, we unexpectedly found the viral capsid possesses up to five major capsid protein variants and a penton protein variant. These variants create varied capsidmicroenvironments for the associations of fibers, a vesicle, and previously unresolved minor capsid proteins. Our structure reveals the identities and atomic models of the capsid components that do not obey the overall icosahedral symmetry and leads to a model for how these components are assembled and initiate capsid assembly, and this model might be applicable to many other giant viruses

Extremal Entropy: Information Geometry, Numerical Entropy Mapping, and Machine Learning Application of Associated Conditional Independences

Entropy and conditional mutual information are the key quantities information theory provides to measure uncertainty of and independence relations between random variables. While these measures are key to diverse areas such as physics, communication, signal processing, and machine learning, surprisingly there is still much about them that is yet unknown. This thesis explores some of this unknown territory, ranging from tackling fundamental questions involving the interdependence between entropies of different subsets of random variables via the characterization of the region of entropic vectors, to applied questions involving how conditional independences can be harnessed to improve the efficiency of supervised learning in discrete valued datasets. The region of entropic vectors is a convex cone that has been shown to be at the core of many fundamental limits for problems in multiterminal data compression, network coding, and multimedia transmission. This cone has been shown to be non-polyhedral for four or more random variables, however its boundary remains unknown for four or more discrete random variables. We prove that only one form of nonlinear non-shannon inequality is necessary to fully characterize the region for four random variables. We identify this inequality in terms of a function that is the solution to an optimization problem. We also give some symmetry and convexity properties of this function which rely on the structure of the region of entropic vectors and Ingleton inequalities. Methods for specifying probability distributions that are in faces and on the boundary of the convex cone are derived, then utilized to map optimized inner bounds to the unknown part of the entropy region. The first method utilizes tools and algorithms from abstract algebra to efficiently determine those supports for the joint probability mass functions for four or more random variables that can, for some appropriate set of non-zero probabilities, yield entropic vectors in the gap between the best known inner and outer bounds. These supports are utilized, together with numerical optimization over non-zero probabilities, to provide inner bounds to the unknown part of the entropy region. Next, information geometry is utilized to parameterize and study the structure of probability distributions on these supports yielding entropic vectors in the faces of entropy and in the unknown part of the entropy region. In the final section of the thesis, we propose score functions based on entropy and conditional mutual information as components in partition strategies for supervised learning of datasets with discrete valued features. Partitioning the data enables a reduction in the complexity of training and testing on large datasets. We demonstrate that such partition strategies can also be efficient in the sense that when the training and testing datasets are split according to them, and the blocks in the partition are processed separately, the classification performance is comparable to, or better than, the performance when the data are not partitioned at all.Ph.D., Electrical Engineering -- Drexel University, 201

Disintegration of metro and land development in transition China: A dynamic analysis in Beijing

The spatial disintegration of transit systems and land development is a vital challenge to the effectiveness of transit-oriented development policies. While transit land use integration and disintegration has received much research interest, conclusions remain mixed. This paper aims to clarify this area, using the large city of Beijing, China, as a case study. It evaluates whether and how Beijing’s land and metro developments have disintegrated according to the basic principles of the Alonso-Muth-Mills model. A before-and-after approach is applied during a period of rapid transit development: 2008–2015. The results show that metro development, generally, was not clearly integrated with land use. Although newly transferred land parcels were geographically concentrated around metro stations, land use types and the degree of land intensification were unrelated to metro development; rather, the road system had a greater influence on these aspects. In addition, metro development led to more fragmented land use in the core areas (\u3c1.5 km) of metro stations than in the fringe areas (1.5–3 km away). This situation was mainly caused by institutional barriers to land-metro integration, such as revenue-oriented land development, the remaining centrally planned system of infrastructure investment, improper urban planning, and the fragmentation of development management that has occurred following China’s political decentralization and marketization. Improving the integration of transit and land development will be difficult unless further reforms and institutional capacity-building are achieved