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In this thesis, different laser based methods to produce high density peptide arrays were developed. They use laser radiation to structure special micro particles with amino acids. The particles are heated and fused to a substrate (Combinatorial Laser Fusing) or they are transferred trough a shock wave to another substrate (Combinatorial Laser Transfer). This way, microarrays with up to 1 million spots per cm² are produced whereas the number of chemical coupling cycles is minimized.
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The adipokine adiponectin is very concentrated in plasma, and decreased levels of adiponectin are associated with pathological conditions such as obesity, diabetes, cardiovascular diseases, and metabolic syndrome. When produced in its full-length form, adiponectin self-associates to generate multimeric complexes. The full-length form of adiponectin can be cleaved by the globular form of elastase that is produced locally, and the resulting biological effects are exerted in a paracrine or autocrine manner. The different forms of adiponectin bind to specific receptors consisting of two G-protein-independent, seven-transmembrane-spanning receptors, called AdipoR1 and AdipoR2, while T-cadherin has been identified as a potential receptor for high molecular weight complexes of adiponectin. Adiponectin exerts a key role in cellular metabolism, regulating glucose levels as well as fatty acid breakdown. However, its biological effects are heterogeneous, involving multiple target tissues. The Special Issue “Mechanisms of Adiponectin Action” highlights the pleiotropic role of this hormone through 3 research articles and 7 reviews. These papers focus on the recent knowledge regarding adiponectin in different target tissues, both in healthy and in diseased conditions.
adiponectin --- atherosclerosis --- cholesterol efflux --- diabetes --- inflammation --- endometrium --- implantation --- transcriptome --- microarray --- adiponectin --- pig --- fertility --- adipose tissue --- reproductive tract --- adipokines --- cell signaling --- skeletal muscle --- regeneration --- adiponectin isoforms --- exercise --- training --- adiponectin --- AMPK --- BIAcore --- extracellular signal-regulated kinase (ERK) --- matricellular proteins --- neuritogenesis --- NGF? --- PC12 cells --- Secreted protein --- acidic and rich in cysteine (SPARC) --- adiponectin --- muscle --- myopathies --- adiponectin --- metabolism --- AdipoRon --- lipotoxicity --- adiponectin --- adiponectin inducer --- kojyl cinnamate ester derivative --- adipogenesis --- hair growth-related factor --- human follicular dermal papilla cell --- obesity --- adipokines --- adiponectin --- breast cancer --- ovarian cancer --- endometrial cancer --- cervix cancer --- estrogen receptor --- adiponectin --- obesity --- cancer --- adiponectin --- adipose tissue --- obesity --- endocrine cancer --- n/a
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Phages have shown a high biotechnological potential with numerous applications. The advent of high-resolution microscopy techniques aligned with omic and molecular tools have revealed innovative phage features and enabled new processes that can be further exploited for biotechnological applications in a wide variety of fields. The high-quality original articles and reviews presented in this Special Issue demonstrate the incredible potential of phages and their derived proteins in a wide range of biotechnological applications for human benefit. Considering the emergence of amazing new available bioengineering tools and the high abundance of phages and the multitude of phage proteins yet to be discovered and studied, we believe that the upcoming years will present us with many more fascinating and new previously unimagined phage-based biotechnological applications.
gene expression regulation --- molecular probe --- macromolecular interactions --- phage-host interaction --- bacteriophage --- endolysin --- Clostridium perfringens --- alpha-sheet --- cancerous tumors --- capsid dynamics --- drug delivery vehicles --- native gel electrophoresis --- neurodegenerative disease --- pathogenic viruses --- phage display --- landscape phage --- major coat protein --- nanomedicine --- diagnostics --- biosensors --- M13 bacteriophage --- biofilm --- porous structure --- filters --- self-assembly --- T7phage library --- sarcoidosis --- tuberculosis --- microarray --- immunoscreening --- R-type pyocin --- bacteriocin --- contractile injection systems --- Pseudomonas aeruginosa --- X-ray crystallography --- receptor-binding protein --- Shigella flexneri --- bacteriophage --- tailspike proteins --- O-antigen --- serotyping --- microtiter plate assay --- fluorescence sensor --- bacteriophages --- encapsulation --- niosomes --- transfersomes --- liposomes --- Staphylococcus aureus --- phage --- Enterococcus faecalis --- Streptococcus agalactiae --- culture enrichment --- bacteriophage --- diagnostics --- Listeria monocytogenes --- endolysin --- magnetic separation --- reporter phage --- endolysin --- Pal --- Cpl-1 --- safety --- toxicity --- immune response --- Streptococcus pneumoniae --- self-assembly --- nanotubular structures --- tail sheath protein --- bacteriophage vB_EcoM_FV3 --- Appelmans --- bacteriophage evolution --- bacteriophage recombination --- phage therapy --- Pseudomonas aeruginosa --- antibiotic resistance --- bacteriophages --- Myoviridae --- bacteriophage-derived lytic enzyme --- enzybiotics --- endolysin --- in vitro activity --- ESKAPE --- n/a
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The influenza virus poses a threat to human health and is responsible for global epidemics every year. In addition to seasonal infections, influenza can cause occasional pandemics of great consequence when novel viruses are introduced into humans. Despite the implementation of comprehensive vaccination programs, influenza viruses continue to pose an important and unpredictable global public health threat. They are one of the most significant causes of morbidity and mortality each year and have a significant economic impact. In recent years, research has been conducted to find alternative approaches to influenza vaccine development, including the generation of universal vaccines. Notably, significant progress in the field of influenza infection, transmission, and immunity have contributed to our understanding of influenza biology, and to expanding the technological approaches for the generation of more efficient strategies against influenza infections. Moreover, highly remarkable developments have been made in the implementation of new methodologies to evaluate the efficiency of vaccines and improve them for use on domestic animals such as poultry, horses, dogs or pigs. This enables us to decrease the exposure of humans to potentially pandemic viruses. The articles in this Special Issue will address the importance of influenza to human health and the advances in influenza research that have led to the development of better therapeutics and vaccination strategies.
influenza A virus --- infection --- hemagglutinin --- hemagglutin stalk --- memory B cells --- antibodies --- germinal centers --- original antigenic sin --- imprinting --- Influenza A virus (IAV) --- innate immunity --- single nucleotide polymorphisms (SNPs) --- Influenza vaccine --- T cell --- memory --- tissue resident --- immune response --- influenza --- CD4 T cell --- epitopes --- hemagglutinin --- influenza virus --- humoral response --- hemagglutinin (HA) of influenza virus --- broad neutralizing antibody(bnAb) --- heterosubtypic immunity of influenza --- original antigenic sin “OAS” --- “universal” influenza vaccine --- protein microarray assay --- mPLEX-Flu assay --- multiple dimensional assay (MDA) --- pediatrics --- influenza virus --- vaccines --- pregnant women --- ARDS --- lung --- morbidity --- vaccination rate --- live attenuated influenza vaccine --- influenza --- master donor virus --- virus–host interaction --- vaccine safety --- immunogenicity --- protection efficacy --- Influenza virus --- influenza vaccine --- vaccination --- pandemic --- immune response --- innate immunity --- adaptive immunity --- universal vaccines
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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.
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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