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Macrophages Role in Integrating Tissue Signals and Biological Processes in Chronic Inflammation and Fibrosis

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889453320 Year: Pages: 104 DOI: 10.3389/978-2-88945-332-0 Language: English
Publisher: Frontiers Media SA
Subject: Medicine (General) --- Allergy and Immunology
Added to DOAB on : 2018-02-27 16:16:45
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Abstract

Macrophages comprehend a heterogeneous mononuclear phagocytic population with wide range phenotypes and roles in homeostasis maintenance and diseases, such as infections, autoimmunity and cancer. Technology improvements enable researchers to track different macrophage populations in different tissues and situations and hypothesize on their role in promoting inflammation or stimulating tissue repair. Through innate immune recognition system macrophages can launch several effector artilleries that culminate in the production of various types of inflammatory mediators as cytokines, chemokines, lipid mediators and oxygen reactive species, which in turn, influence the behavior of other cells. Furthermore, macrophages and interacting cells are also susceptible to metabolic changes that ultimately will define the outcome macrophage signaling and its effect in the tissue. Here, we present a concise series of discussions on the role of macrophages, its response to the microenvironment and effects on other cells during tissue injury and repair. Triggering of inflammasome in macrophage activation and function is of special interest in this issue. We will emphasize the role of different macrophage subpopulations and the plasticity of these cells during fibrotic process in different models of diseases.

Autism Spectrum Disorders: From genotypes to phenotypes

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196807 Year: Pages: 93 DOI: 10.3389/978-2-88919-680-7 Language: English
Publisher: Frontiers Media SA
Subject: Neurology --- Science (General)
Added to DOAB on : 2016-04-07 11:22:02
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This Research Topic covers the pathogenetic processes in Autism Spectrum Disorder (ASD) that underpin the translation of genetic vulnerability to clinically significant symptoms. Available research data in ASD suggests that it is a neural connectivity disorder and that the social communication and related neurobehavioural symptoms result from reduced synchronization between key "social brain" regions. These interconnected neural systems can be understood through the relationship between functionally relevant anatomic areas and neurochemical pathways, the programming of which are genetically modulated during neurodevelopment and mediated through a range of epigenetic and environmental modulators. Elucidating the underlying molecular mechanisms can provide an invaluable window for understanding the neural wiring that regulates higher brain functions and consequent clinical phenotypes. In keeping with the multi modal and diverse origins of ASD, this Research Topic explores the genetic underpinnings and environmental modulation in the aetiology; neural substrates, biomarkers and endophenotypes that underlie clinical characteristics; as well as neurochemical pathways and pathophysiological mechanisms that pave the way for therapeutic interventions. Furthermore, since genetically mediated deficits and consequent functional impairments involve activity-dependent synapse development that depends on postnatal learning and experience, the trajectory towards the final clinical expression could be modulated by early interventions that exploit the neuronal maturation and brain plasticity. However, identifying these diverse pathogenetic processes and tailoring interventions would require subtyping ASD into homogeneous subgroups. In this regard, this topic covers the current state of evidence in the literature through topic reviews as well as ongoing original work that provides tangible hypotheses and directions for future research.

Advances in Prevention of Foodborne Pathogens of Public Health Concern during Manufacturing

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ISBN: 9783039219322 / 9783039219339 Year: Pages: 168 DOI: 10.3390/books978-3-03921-933-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General) --- Therapeutics
Added to DOAB on : 2019-12-09 11:49:16
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According to a report from the U.S. Centers for Disease Control and Prevention (CDC), achieving safe and healthier foods was one of the top ten achievements of public health in the 20th century. However, considerable persisting challenges currently exist in developed nations and developing economies for further assuring the safety and security of the food supplies. According to CDC estimates, as many as 3000 American adults, as an example, and based on a recent epidemiological estimate of the World Health Organization, around 420,000 individuals around the globe, lose their lives annually due to foodborne diseases. This emphasizes the need for innovative and emerging interventions, for further prevention or mitigation of the risk of foodborne microbial pathogens during food processing and manufacturing. The current publication discusses recent advancements and progress in the elimination and decontamination of microbial pathogens during various stages of manufacturing and production. Special emphasis is placed on hurdle validation studies, investigating decontamination of non-typhoidal Salmonella enterica serovars, various serogroups of Shiga toxin-producing Escherichia coli, public health-significant serotypes of Listeria monocytogenes, and pathogenic species of Cronobacter.

Application of Bioinformatics in Cancers

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ISBN: 9783039217885 / 9783039217892 Year: Pages: 418 DOI: 10.3390/books978-3-03921-789-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Biotechnology
Added to DOAB on : 2019-12-09 11:49:16
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This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.

Keywords

comorbidity score --- mortality --- locoregionally advanced --- HNSCC --- curative surgery --- traditional Chinese medicine --- health strengthening herb --- cancer treatment --- network pharmacology --- network target --- high-throughput analysis --- brain metastases --- colorectal cancer --- KRAS mutation --- PD-L1 --- tumor infiltrating lymphocytes --- drug resistance --- gefitinib --- erlotinib --- biostatistics --- bioinformatics --- Bufadienolide-like chemicals --- molecular mechanism --- anti-cancer --- bioinformatics --- cancer --- brain --- pathophysiology --- imaging --- machine learning --- extreme learning --- deep learning --- neurological disorders --- pancreatic cancer --- TCGA --- curation --- DNA --- RNA --- protein --- single-biomarkers --- multiple-biomarkers --- cancer-related pathways --- colorectal cancer --- DNA sequence profile --- Monte Carlo --- mixture of normal distributions --- somatic mutation --- tumor --- mutable motif --- activation induced deaminase --- AID/APOBEC --- transcriptional signatures --- copy number variation --- copy number aberration --- TCGA mining --- cancer CRISPR --- firehose --- gene signature extraction --- gene loss biomarkers --- gene inactivation biomarkers --- biomarker discovery --- chemotherapy --- microarray --- ovarian cancer --- predictive model --- machine learning --- overall survival --- observed survival interval --- skin cutaneous melanoma --- The Cancer Genome Atlas --- omics --- breast cancer prognosis --- artificial intelligence --- machine learning --- decision support systems --- cancer prognosis --- independent prognostic power --- omics profiles --- histopathological imaging features --- cancer --- intratumor heterogeneity --- genomic instability --- epigenetics --- mitochondrial metabolism --- miRNAs --- cancer biomarkers --- breast cancer detection --- machine learning --- feature selection --- classification --- denoising autoencoders --- breast cancer --- feature extraction and interpretation --- concatenated deep feature --- cancer modeling --- interaction --- histopathological imaging --- clinical/environmental factors --- oral cancer --- miRNA --- bioinformatics --- datasets --- biomarkers --- TCGA --- GEO DataSets --- hormone sensitive cancers --- breast cancer --- StAR --- estrogen --- steroidogenic enzymes --- hTERT --- telomerase --- telomeres --- alternative splicing --- network analysis --- hierarchical clustering analysis --- differential gene expression analysis --- cancer biomarker --- diseases genes --- variable selection --- false discovery rate --- knockoffs --- bioinformatics --- copy number variation --- cell-free DNA --- methylation --- mutation --- next generation sequencing --- self-organizing map --- head and neck cancer --- treatment de-escalation --- HP --- molecular subtypes --- tumor microenvironment --- Bioinformatics tool --- R package --- machine learning --- meta-analysis --- biomarker signature --- gene expression analysis --- survival analysis --- functional analysis --- bioinformatics --- machine learning --- artificial intelligence --- Network Analysis --- single-cell sequencing --- circulating tumor DNA (ctDNA) --- Neoantigen Prediction --- precision medicine --- Computational Immunology

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