In vitro Antimicrobial Susceptibility of Urinary Tract Infection Pathogens in Children

Aim:Urinary tract infection (UTI) is one of the most common bacterial infections in children. Empirical treatment is commenced according to the patient’s characteristics and the antimicrobial susceptibility patterns in the region. Therefore, a determination of antimicrobial resistance patterns has a great importance in effective treatment. The aim of this study was to determine the pathogens which cause UTIs in patients admitted to a university hospital in Izmir and to determine their antimicrobial susceptibility pattern.Materials and Methods:The files of patients aged between 0-18 years, followed up with a diagnosis of UTI, vesicoureteral reflux and neurogenic bladder in Ege University Faculty of Medicine Paediatric Nephrology Unit between February, 2013 and November, 2018 were retrospectively reviewed.Results:A total of 1,126 positive urine cultures from 729 patients (65% female) were included in this study. Gram-negative pathogens constituted 88.2% of the cultures. Escherichia coli (E. coli) was the most commonly isolated bacteria with a prevalence of 59.1%, followed by Klebsiella pneumonia with 17.9%, and Enterococcus faecalis with 8.3% (n=93). Ampicillin, cefuroxime and trimethoprim-sulfamethoxazole with susceptibility rates of 18.6%, 39.6%, 49.0% respectively, constituted the highest resistant antimicrobials to Enterobacteriaceae. Enterococcus spp. showed the highest resistance to gentamycin with 50% resistance in tested cases. Pseudomonas spp. with 64.3% susceptibility showed the highest resistance to piperacillin-tazobactam.Conclusion:This study revealed that bacterial resistance to commonly used antimicrobials in UTI is an important and challenging problem which requires planning

ZnO-Based Ultraviolet Photodetectors

Ultraviolet (UV) photodetection has drawn a great deal of attention in recent years due to a wide range of civil and military applications. Because of its wide band gap, low cost, strong radiation hardness and high chemical stability, ZnO are regarded as one of the most promising candidates for UV photodetectors. Additionally, doping in ZnO with Mg elements can adjust the bandgap largely and make it feasible to prepare UV photodetectors with different cut-off wavelengths. ZnO-based photoconductors, Schottky photodiodes, metal–semiconductor–metal photodiodes and p–n junction photodetectors have been developed. In this work, it mainly focuses on the ZnO and ZnMgO films photodetectors. We analyze the performance of ZnO-based photodetectors, discussing recent achievements, and comparing the characteristics of the various photodetector structures developed to date

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Application of deep learning technique in next generation sequence experiments

Abstract In recent years, the widespread utilization of biological data processing technology has been driven by its cost-effectiveness. Consequently, next-generation sequencing (NGS) has become an integral component of biological research. NGS technologies enable the sequencing of billions of nucleotides in the entire genome, transcriptome, or specific target regions. This sequencing generates vast data matrices. Consequently, there is a growing demand for deep learning (DL) approaches, which employ multilayer artificial neural networks and systems capable of extracting meaningful information from these extensive data structures. In this study, the aim was to obtain optimized parameters and assess the prediction performance of deep learning and machine learning (ML) algorithms for binary classification in real and simulated whole genome data using a cloud-based system. The ART-simulated data and paired-end NGS (whole genome) data of Ch22, which includes ethnicity information, were evaluated using XGBoost, LightGBM, and DL algorithms. When the learning rate was set to 0.01 and 0.001, and the epoch values were updated to 500, 1000, and 2000 in the deep learning model for the ART simulated dataset, the median accuracy values of the ART models were as follows: 0.6320, 0.6800, and 0.7340 for epoch 0.01; and 0.6920, 0.7220, and 0.8020 for epoch 0.001, respectively. In comparison, the median accuracy values of the XGBoost and LightGBM models were 0.6990 and 0.6250 respectively. When the same process is repeated for Chr 22, the results are as follows: the median accuracy values of the DL models were 0.5290, 0.5420 and 0.5820 for epoch 0.01; and 0.5510, 0.5830 and 0.6040 for epoch 0.001, respectively. Additionally, the median accuracy values of the XGBoost and LightGBM models were 0.5760 and 0.5250, respectively. While the best classification estimates were obtained at 2000 epochs and a learning rate (LR) value of 0.001 for both real and simulated data, the XGBoost algorithm showed higher performance when the epoch value was 500 and the LR was 0.01. When dealing with class imbalance, the DL algorithm yielded similar and high Recall and Precision values. Conclusively, this study serves as a timely resource for genomic scientists, providing guidance on why, when, and how to effectively utilize deep learning/machine learning methods for the analysis of human genomic data

Whole Genome microRNA Expression Data in Childhood Acute Lymphoblastic Leukemia and Evaluation of microRNA Pathways Using Fuzzy C-means

Objective: Hard clustering approaches may cause some of the relationships to be overlooked due to their nature of algorithms especially in genetic datasets. But hidden relationships can be revealed by fuzzy approaches. Purpose of this study was evaluating effect of microRNAs (miRNA) on children with acute lymphoblastic leukaemia (ALL) by using miRNA expression data obtained from bone marrow samples with sets containing different numbers of elements of fuzzy Cmeans (FCM). Material and Methods: miRNA expression levels of 43 newly diagnosed ALL patients and 14 healthy subjects were analysed via FCM. Clusters containing different numbers of miRNAs were evaluated, common properties in messenger RNA (mRNA) pathways were investigated and new pathways associated with ALL and cancer were described via miRNA target prediction tools. Results: Significant miRNA profile was compared to control cases. Only 46 out of 108 miRNAs were found to be significantly upregulated or downregulated. Of forty six miRNAs: 8 miRNAs were labelled as tumour suppressor (17.4%), 17 miRNAs were labelled as onco-miR (37.0%) and 21 miRNAs could not be labelled (45.6%) for hematological malignancy. Fourteen (%30.4) miRNAs were found to be apoptosis-related, 27 miRNAs were in leukemia-related (58.7%) and 15 labelled miRNAs were related with cancer pathways (32.6%). hsa-miR-181b, hsa-miR- 146a, hsa-miR-155, hsa-miR-181c-5p, hsa-miR-7-1-3p, hsa-miR-708- 5p onco-miRs constituted a set. These miRNAs targeted 801 common mRNAs (p0.05). When this sub-cluster was searched in the literature and miRNA target prediction tools system, it was found to be involved in cancer-related pathways except ALL. Conclusion: Hidden relationships can be defined by fuzzy approaches and those pathways may provide guidance to open up new horizons in the field of miRNA studies

Blood Pressure Percentiles in Turkish Children and Adolescents

Aim: Pediatric hypertension, a public health concern, is now commonly known worldwide to be an early risk factor for cardiovascular and renal morbidity and mortality. Early detection of hypertension is of the utmost importance to help reduce serious complications. Several distributions of country-specific blood pressure (BP) percentiles have been established worldwide. the aim of this study is to determine BP percentiles in healthy Turkish children aged 2 to 18 years. Materials and Methods: in this cross-sectional study, BP was measured in 4,984 randomly selected children and adolescents aged 2-17 years. the 50th, 90th and 95th percentile of BP percentiles were determined for gender, age and height with the use a polynomial regression model. BP percentiles at median height were compared with the US Fourth Report references. Results: the normative values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) increased with age for both genders and varied by gender. At median height, the age-specific differences at the 90th percentile of SBP tended to be higher in boys than in girls at all ages. DBP values in girls were higher than in boys until the age of 9 years, after which boys demonstrated higher values compared to girls. Conclusion: the age and height specific reference BP values determined in this study is a novel reference for Turkish children and adolescents. Turkish BP values are lower than existing US reference values

Protecting Personal Information in Enterprise Applications Kurumsal Uygulamalarda Kişisel Verilerin Korunmasi

© 2020 IEEE.In the digital environment, personal information gets stored by various service providers and in some situations can be used out of its purpose and without permission. These violations led to various legislation in the world and The Law for Protecting Personal Information (KVKK) in Turkey. Software that collects personal information needs to comply with the legislation as well. However, a model and sets of requirements for transforming the software development process for protecting personal information do not exist for the use of software developers and analysts. In this work, we report the experience we had while preparing a guide for software developers that includes a transformation model and a set of requirements based on KVKK. The relevant guide to this study is used as an input to the Turkish Presidency Digital Transformation Office Information and Communication Security Guide