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β-Glucosidase (β-D-glucoside glucohydrolase, EC 3.2.1.21) is a catalytic enzyme present in both prokaryotes and eukaryotes that selectively catalyzes either the linkage between two glycone residues or between glycone and aryl or alkyl aglycone residue. Growing edible mushrooms in the soil with increased cellulose content can lead to the production of glucose, which is a process dependent on β-glucosidase. In this study, β-glucosidase was isolated from Agaricus bisporus (white button mushroom) using ammonium sulfate precipitation and hydrophobic interaction chromatography, giving 10.12-fold purification. Biochemical properties of the enzyme were investigated and complete characterization was performed. The enzyme is a dimer with two subunits of approximately 46 and 62 kDa. Optimum pH for the enzyme is 4.0, while the optimum temperature is 55 °C. The enzyme was found to be exceptionally thermostable. The most suitable commercial substrate for this enzyme is p-NPGlu with Km and Vmax values of 1.751 mM and 833 U/mg, respectively. Enzyme was inhibited in a competitive manner by both glucose and δ-gluconolactone with IC50 values of 19.185 and 0.39 mM, respectively and Ki values of 9.402 mM and 7.2 µM, respectively. Heavy metal ions that were found to inhibit β-glucosidase activity are I(-), Zn(2+), Fe(3+), Ag(+), and Cu(2+). This is the first study giving complete biochemical characterization of A. bisporus β-glucosidase.

Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence, clinical history, and imaging (Magnetic resonance imaging MRI / computed tomography CT) findings. The implementation of deep learning has rapidly propagated in almost every field in recent years, particularly in the medical images’ evaluation. This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. The purpose of this review paper is to include a detailed summary by first providing a succinct guide to the types of pediatric brain tumors and pediatric brain tumor imaging techniques. Then, we will present the research carried out by summarizing the scientific contributions to the field of pediatric brain tumor imaging processing and analysis. Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.

No abstract

Human Y-chromosomal haplogroups are an important tool used in population genetics and forensic genetics. A conventional method used for Y haplogroup assignment is based on a set of Y-single nucleotide polymorphism (SNP) markers deployed, which exploits the low mutation rate nature of these markers. Y chromosome haplogroups can be successfully predicted from Y-short tandem repeat (STR) markers using different software packages, and this method gained much attention recently due to its labor-, time-, and cost-effectiveness. The present study was based on the analysis of a total of 480 adult male buccal swab samples collected from different regions of Bosnia and Herzegovina. Y haplogroup prediction was performed using Whit Athey’s Haplogroup Predictor, based on haplotype data on 23 Y-STR markers contained within the PowerPlex® Y23 kit. The results revealed the existence of 14 different haplogroups, with I2a, R1a, and E1b1b being the most prevalent with frequencies of 43.13, 14.79, and 14.58%, respectively. Compared to the previously published studies on Bosnian-Herzegovinian population based on Y-SNP and Y-STR data, this study represents an upgrade of molecular genetic data with a significantly larger number of samples, thus offering more accurate results and higher probability of detecting rare haplogroups.