Adv Exp Med Biol
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The oral microbiota represents an important part of the human microbiota, and includes several hundred to several thousand diverse species. It is a normal part of the oral cavity and has an important function to protect against colonization of extrinsic bacteria which could affect systemic health. On the other hand, the most common oral diseases caries, gingivitis and periodontitis are based on microorganisms. ⋯ On any non-shedding surfaces of the oral cavity dental plaque starts to form, which meets all criteria for a microbial biofilm and is subject to the so-called succession. When the sensitive ecosystem turns out of balance - either by overload or weak immune system - it becomes a challenge for local or systemic health. Therefore, the most common strategy and the golden standard for the prevention of caries, gingivitis and periodontitis is the mechanical removal of this biofilms from teeth, restorations or dental prosthesis by regular toothbrushing.
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Near-infrared spectroscopy (NIRS) is a widely used noninvasive method for measuring human brain activation based on the cerebral haemodynamic response. However, systemic changes can influence the signal's parameters. Our study aimed to investigate the relationships between NIRS signals and skin blood flow (SBF) or blood pressure during dynamic movement. ⋯ The correlation coefficients for O2Hb and MAP during warm-up, 30 % VO2peak, and 50 % VO2peak were 0.725 (P < 0.01), 0.472 (P < 0.01), and 0.939 (P < 0.01), respectively. Changes in the state of the cardiovascular system influenced O2Hb signals positively during low and moderate-intensity exercise, whereas a negative relationship was observed during high-intensity exercise. These results suggest that the relationship between the O2Hb signal and systemic changes is affected by exercise intensity.
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Biological systems function via intricate cellular processes and networks in which RNAs, metabolites, proteins and other cellular compounds have a precise role and are exquisitely regulated (Kumar and Mann, FEBS Lett 583(11):1703-1712, 2009). The development of high-throughput technologies, such as the Next Generation DNA Sequencing (NGS) and DNA microarrays for sequencing genomes or metagenomes, have triggered a dramatic increase in the last few years in the amount of information stored in the GenBank and UniProt Knowledgebase (UniProtKB). GenBank release 210, reported in October 2015, contains 202,237,081,559 nucleotides corresponding to 188,372,017 sequences, whilst there are only 1,222,635,267,498 nucleotides corresponding to 309,198,943 sequences from Whole Genome Shotgun (WGS) projects. ⋯ Meanwhile, UniProtKB/TrEMBL (release 2015_12 of December 9 2015) contains 1,838,851,8871 amino acids corresponding to 555,270,679 entries. Proteomics has also improved our knowledge of proteins that are being expressed in cells at a certain time of the cell cycle. It has also allowed the identification of molecules forming part of multiprotein complexes and an increasing number of posttranslational modifications (PTMs) that are present in proteins, as well as the variants of proteins expressed.
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Cannabis is the most widely smoked illicit substance in the world. It can be smoked alone in its plant form, marijuana, but it can also be mixed with tobacco. The specific effects of smoking cannabis are difficult to assess accurately and to distinguish from the effects of tobacco; however its use may produce severe consequences. ⋯ Heavy use of cannabis on its own can cause airway obstruction. Based on immuno-histopathological and epidemiological evidence, smoking cannabis poses a potential risk for developing lung cancer. At present, however, the association between smoking cannabis and the development of lung cancer is not decisive.
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Shotgun proteomics is a high throughput technique for protein identification able to identify up to several thousand proteins from a single sample. In order to make sense of this large amount of data, proteomics analysis software is needed, aimed at making the data intuitively accessible to beginners as well as experienced scientists. This chapter provides insight on where to start when analyzing shotgun proteomics data, with a focus on explaining the most common pitfalls in protein identification analysis and how to avoid them. Finally, the move to seeing beyond the list of identified proteins and to putting the results into a bigger biological context is discussed.