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Öğe Amifostine Treatment of a Patient with Refractory Acute Myeloid Leukemia(Ekin Tibbi Yayincilik Ltd Sti-Ekin Medical Publ, 2009) Tekgunduz, Emre; Erikci, Alev Akyol; Ozturk, AhmetThe prognosis for the majority of acute myeloid leukemia (AML) patients without a donor is dismal whether conventional salvage chemotherapy regimens or investigational strategies are used, and most of these patients will eventually die of their disease. There is no standard chemotherapy regimen that provides durable complete remission in patients with refractory AML. Beneficial effects of amifostine, either alone or in combination with conventional chemotherapy, was demonstrated in patients with myelodysplastic syndrome and poor prognosis AML. Here we report our second experience with AML patients who were successfully treated with an amifostine containing noncytotoxic drug combination. The beneficial effects of amifostine are not limited to cytoprotectivity which enables dose-escalation for many chemotherapeutic agents, at least in some refractory AML it can also be used as a bridge to hematopoietic stem cell transplantation.Öğe MLSeq: Machine learning interface for RNA-sequencing data(Elsevier Ireland Ltd, 2019) Goksuluk, Dincer; Zararsiz, Gokmen; Korkmaz, Selcuk; Eldem, Vahap; Zararsiz, Gozde Erturk; Ozcetin, Erdener; Ozturk, AhmetBackground and Objective: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network. Methods: Classification of RNA-sequencing data is not straightforward since raw data should be preprocessed before downstream analysis. With MLSeq package, researchers can easily preprocess (normalization, filtering, transformation etc.) and classify raw RNA-sequencing data using two strategies: (i) to perform algorithms which are directly proposed for RNA-sequencing data structure or (ii) to transform RNA-sequencing data in order to bring it distributionally closer to microarray data structure, and perform algorithms which are developed for microarray data. Moreover, we proposed novel algorithms such as voom (an acronym for variance modelling at observational level) based nearest shrunken centroids (voomNSC), diagonal linear discriminant analysis (voomDLDA), etc. through MLSeq. Materials: Three real RNA-sequencing datasets (i.e cervical cancer, lung cancer and aging datasets) were used to evalute model performances. Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) were selected as algorithms based on dicrete distributions, and voomNSC, nearest shrunken centroids (NSC) and support vector machines (SVM) were selected as algorithms based on continuous distributions for model comparisons. Each algorithm is compared using classification accuracies and sparsities on an independent test set. Results: The algorithms which are based on discrete distributions performed better in cervical cancer and aging data with accuracies above 0.92. In lung cancer data, the most of algorithms performed similar with accuracies of 0.88 except that SVM achieved 0.94 of accuracy. Our voomNSC algorithm was the most sparse algorithm, and able to select 2.2% and 6.6% of all features for cervical cancer and lung cancer datasets respectively. However, in aging data, sparse classifiers were not able to select an optimal subset of all features. Conclusion: MLSeq is comprehensive and easy-to-use interface for classification of gene expression data. It allows researchers perform both preprocessing and classification tasks through single platform. With this property, MLSeq can be considered as a pipeline for the classification of RNA-sequencing data. (C) 2019 Elsevier B.V. All rights reserved.Öğe The Relationship Between Blood Pressure and Sleep Duration in Turkish Children: A Cross-Sectional Study(Galenos Yayincilik, 2018) Bal, Cengiz; Ozturk, Ahmet; Cicek, Betul; Ozdemir, Ahmet; Zararsiz, Gokmen; Unalan, Demet; Zararsiz, Gozde ErturkObjective: As in adults, hypertension is also an important risk factor for cardiovascular disease in children. We aimed to evaluate the effect of sleep duration on blood pressure in normal weight Turkish children aged between 11-17 years. Methods: This cross-sectional study was conducted in the primary and secondary schools of the two central and ten outlying districts of Kayseri, Turkey. Subjects were 2860 children and adolescents (1385 boys, 1475 girls). Systolic and diastolic blood pressures were measured according to the recommendations of the Fourth Report of the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. Sleep duration was classified as follows: <= 8 hours, 8.1-8.9 hours, 9.0-9.9 hours or >= 10 hours. Results: For short sleeper boys and girls (participants with a sleep duration <= 8 h) the prevalence of prehypertension and hypertension was 35.0% and 30.8%, respectively. In univariate binary logistic regression analyses (age-adjusted), each unit increment in sleep duration (hours) in boys and girls, decreased the prehypertension and hypertension risk by 0.89 [odds ratio (OR)] [confidance interval (CI); 0.82-0.98] and 0.88 (OR) (CI; 0.81-0.97), respectively (p<0.05). In multiple binary logistic regression analyses [age-and body mass index (BMI)-adjusted] the location of the school and sleep duration categories were shown to be the most important factors for prehypertension and hypertension in both genders, while household income was the most important factor, only in boys. Conclusions: A sleep duration <= 8 h is an independent risk factor for prehypertension and hypertension in Turkish children aged 11-17 years.Öğe voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data(Peerj Inc, 2017) Zararsiz, Gokmen; Goksuluk, Dincer; Klaus, Bernd; Korkmaz, Selcuk; Eldem, Vahap; Karabulut, Erdem; Ozturk, AhmetRNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of rnean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom's precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poissan linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for can be reused for RNA-Seq data.Öğe Weight, height and BMI references in Elazig: an east Anatolian city(Turkish J Pediatrics, 2011) Pirincci, Edibe; Mazicioglu, M. Mumtaz; Berberoglu, Ufuk; Acik, Yasemin; Durmus, Birsen; Ozturk, AhmetThe aim of this study was to produce the growth references for Elazig children aged 6-11 years. Data were collected in eight primary schools of Elazig in 2007. Age-and gender-specific height, weight and body mass index (BMI) references were produced with LMS (Lambda-Mu-Sigma) method and compared with reported values in an Anatolian and a metropolitan city. A total of 3,342 (1,634 females, 1,708 males) children aged 6-11 years from among 4,258 students were included in the study. Age-and gender-specific height, weight and BMI references were produced. The 3rd-97th percentiles were detected to be higher than the range of percentiles between 6-11-year-old children. We consider that this first local reference for Elazig will provide a useful tool for health planning and monitoring of growth and development.