Gradybinderup3371

001). Other factors significantly associated with delayed or no isolation on univariate analyses were older age, admission route (emergency room vs. other), admitting department, negative acid-fast bacilli (AFB) stain, and negative MTB PCR. On multivariate analysis, admission through an outpatient clinic, admission to a department other than infectious diseases or pulmonology, an atypical chest X-ray finding, and negative sputum AFB stains were risk factors for isolation failure. CONCLUSIONS Delayed or no isolation of patients with pulmonary TB was attributed mainly to atypical radiologic findings and negative findings of direct TB diagnostic tests. Extracellular vesicle (EV) is a unified terminology of membrane-enclosed vesicular species ubiquitously secreted by almost every cell type and present in all body fluids. They carry a cargo of lipids, metabolites, nucleic acids and proteins for their clearance from cells as well as for cell-to-cell communications. The exact composition of EVs and their specific functions are not well understood due to the underdevelopment of the separation protocols, especially those from the central nervous system including animal and human brain tissues as well as cerebrospinal fluids, and the low yield of proteins in the separated EVs. To understand their exact molecular composition and their functional roles, development of the reliable protocols for EV separation is necessary. Here we report the methods for EV separation from human and mouse unfixed frozen brain tissues by a sucrose step gradient ultracentrifugation method, and from human cerebrospinal fluids by an affinity capture method. The separated EVs were assessed for morphological, biophysical and proteomic properties of separated EVs by nanoparticle tracking analysis, transmission electron microscopy, and labeled and label-free mass spectrometry for protein profiling with step-by-step protocols for each assessment. Mammals and higher vertebrates including humans have only three members of the carotenoid cleavage dioxygenase family of enzymes. This review focuses on the two that function as carotenoid oxygenases. β-Carotene 15,15'-dioxygenase (BCO1) catalyzes the oxidative cleavage of the central 15,15' carbon-carbon double of β-carotene bond by addition of molecular oxygen. The product of the reaction is retinaldehyde (retinal or β-apo-15-carotenal). Thus, BCO1 is the enzyme responsible for the conversion of provitamin A carotenoids to vitamin A. It also cleaves the 15,15' bond of β-apocarotenals to yield retinal and of lycopene to yield apo-15-lycopenal. WP1130 in vivo β-Carotene 9',10'-dioxygenase (BCO2) catalyzes the cleavage of the 9,10 and 9',10' double bonds of a wider variety of carotenoids, including both provitamin A and non-provitamin A carotenoids, as well as the xanthophylls, lutein and zeaxanthin. Indeed, the enzyme shows a marked preference for utilization of these xanthophylls and other substrates with hydroxylated terminal rings. Studies of the phenotypes of BCO1 null, BCO2 null, and BCO1/2 double knockout mice and of humans with polymorphisms in the enzymes, has clarified the role of these enzymes in whole body carotenoid and vitamin A homeostasis. These studies also demonstrate the relationship between enzyme expression and whole body lipid and energy metabolism and oxidative stress. In addition, relationships between BCO1 and BCO2 and the development or risk of metabolic diseases, eye diseases and cancer have been observed. While the precise roles of the enzymes in the pathophysiology of most of these diseases is not presently clear, these gaps in knowledge provide fertile ground for rigorous future investigations. This article is part of a Special Issue entitled Carotenoids Recent Advances in Cell and Molecular Biology edited by Johannes von Lintig and Loredana Quadro. To better understand the potential function of carotenoids in the chemoprevention of cancers, mechanistic understanding of carotenoid action on genetic and epigenetic signaling pathways is critically needed for human studies. The use of appropriate animal models is the most justifiable approach to resolve mechanistic issues regarding protective effects of carotenoids at specific organs and tissue sites. While the initial impetus for studying the benefits of carotenoids in cancer prevention was their antioxidant capacity and pro-vitamin A activity, significant advances have been made in the understanding of the action of carotenoids with regards to other mechanisms. This review will focus on two common carotenoids, provitamin A carotenoid β-cryptoxanthin and non-provitamin A carotenoid lycopene, as promising chemopreventive agents or chemotherapeutic compounds against cancer development and progression. We reviewed animal studies demonstrating that β-cryptoxanthin and lycopene effectively prevent the development or progression of various cancers and the potential mechanisms involved. We highlight recent research that the biological functions of β-cryptoxanthin and lycopene are mediated, partially via their oxidative metabolites, through their effects on key molecular targeting events, such as NF-κB signaling pathway, RAR/PPARs signaling, SIRT1 signaling pathway, and p53 tumor suppressor pathways. The molecular targets by β-cryptoxanthin and lycopene, offer new opportunities to further our understanding of common and distinct mechanisms that involve carotenoids in cancer prevention. This article is part of a Special Issue entitled Carotenoids recent advances in cell and molecular biology edited by Johannes von Lintig and Loredana Quadro. Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI.