SERUM EXPRESSION OF MICRORNA-142 IN A COHORT OF BULGARIAN PATIENTS WITH INFLAMMATORY BOWEL DISEASES

The focus of the research was to investigate the anatomical location of the rabbit liver. Thus, we applied a topographic algorithm, using dorsal frozen cuts and CT algorithm with coronary slices. The used animals were 13 matured, healthy clinically white New Zealand rabbits. We measured the metric CT parameters – transverse and craniocaudal sizes. At the level of the dorsal plane, located 15 mm ventrally from the spine, dorsal part of lobus hepatis sinister was found, and on the right and laterally - lobus hepatis dexter. At the level of the dorsal plane, located 30 mm ventrally from the spine, lobus hepatis dexter was located cranially relative to lobus hepatis sinister medialis and reached caudally to pars pylorica. Lobus hepatis sinister lateralis remained caudal to lobus hepatis sinister medialis and touched corpus ventriculi. Lobus hepatis sinister lateralis was found cranially to corpus ventriculi and pars pylorica. Lobus caudatus caudally touched the right kidney. At the level of the dorsal plane, located 45 mm ventrally from the spine, lobus hepatis dexter was found to be in the same dorsal plane with the left lobe of the liver. CT normodense heterogeneous anatomical image of lobus hepatis dexter was parallel to that of lobus hepatis sinister, which determined the transverse location of the organ. The obtained imaging analysis of the liver’s anatomical parts and their proximity to other organ structures were interpreted depending on their attenuation profile. The transverse size of the organ at 15 mm ventrally from the spine showed a value of 76.16 mm, and at 30 mm ventrally, this parameter reached a value of 81.48 mm. The highest values were obtained at 45 mm ventrally - 85.21 mm. CT anatomical study added and confirmed the results of the topographic investigation.

The impact of commercially available ale and lager yeast strains on the fermentative diversity of beers

Yeasts from the species Saccharomyces cerevisiae (ale yeast) and Saccharomyces pastorianus (lager yeast) are the main component of beer fermentation. It is known that different beer categories depend on the use of specific ale or lager strains, where the yeast imprint its distinctive fermentative profile to the beer. Despite this, there are no studies reporting how diverse, rich, and homogeneous the beer categories are in terms of commercially available brewing yeast strains. In this work, the diversity, richness, and evenness of different beer categories and commercial yeast strains available for brewing were evaluated by applying quantitative concepts of ecology analysis in a sample of 121,528 beer recipes. For this purpose, the frequency of ale or lager and dry or liquid yeast formulations usage was accessed and its influence in the fermentation temperature, attenuation profile, and number of recipes for a beer category were analyzed. The results indicated that many beer categories are preferentially fermented with dry yeast strains formulations instead of liquid yeasts, despite considering the high number of available liquid yeast formulations. Moreover, ale dry strains are preferentially used for lager brewing. The preferential use of specific yeast formulations drives the diversity, richness, and evenness of a beer category, showing that many yeast strains are potentially and industrially underexplored.

Material Discrimination Based on K-edge Characteristics

Spectral/multienergy CT employing the state-of-the-art energy-discriminative photon-counting detector can identify absorption features in the multiple ranges of photon energies and has the potential to distinguish different materials based on K-edge characteristics. K-edge characteristics involve the sudden attenuation increase in the attenuation profile of a relatively high atomic number material. Hence, spectral CT can utilize material K-edge characteristics (sudden attenuation increase) to capture images in available energy bins (levels/windows) to distinguish different material components. In this paper, we propose an imaging model based on K-edge characteristics for maximum material discrimination with spectral CT. The wider the energy bin width is, the lower the noise level is, but the poorer the reconstructed image contrast is. Here, we introduce the contrast-to-noise ratio (CNR) criterion to optimize the energy bin width after the K-edge jump for the maximum CNR. In the simulation, we analyze the reconstructed image quality in different energy bins and demonstrate that our proposed optimization approach can maximize CNR between target region and background region in reconstructed image.