The Role of Superb Microvascular Imaging and Shear Wave Elastography in the Prediction of Hemorrhage Complications After Renal Parenchyma Biopsy.

OBJECTIVES: The aim of study was to evaluate the diagnostic utility of the renal parenchyma elasticity with the shear wave elastography (SWE) and microvascularization with the superb microvascular imaging (SMI) technique before kidney biopsy and to predict the complication of hemorrhage before kidney biopsy. METHODS: A total of 75 patients were included in the prospective study. Before the biopsy, vascularity features of the kidney parenchyma in the area to be biopsied were assessed by SMI and parenchymal stiffness by SWE and were examined by 2 independent radiologists. RESULTS: A statistically significant difference was found in the SMI and SWE values between the groups with and without hematoma and hematuria when compared with the Student t test and Mann-Whitney U test ( P < 0.05). The SWE hardness cutoff value, which maximizes the prediction of the development of hematuria, was found to be 18.40 kPa, and the sensitivity and specificity values were 84.4% and 62.8%, respectively. In SMI vascularity index values, the cutoff value was found to be 0.247410800 kPa, and sensitivity and specificity values were 81.3% and 83.7%, respectively. The cutoff value of the SMI vascularity index values that maximized the prediction of hematoma development was 0.297009650, and the sensitivity and specificity values were 87% and 87%, respectively. CONCLUSIONS: We believe that evaluating and standardizing the microvascularization and elasticity of the kidney parenchyma before a percutaneous kidney biopsy will be potentially useful as a guiding method in the prediction of postbiopsy hemorrhage development.

An experimental study: the effect of S. boulardii on abemaciclibinduced diarrhea.

BACKGROUND: In our study, we aimed to investigate the protective effects of Saccharomyces boulardii on abemaciclib-induced diarrhea model, which is a commonly used drug in breast cancer. METHODS: Thirty rats were divided into 3 groups as control (Group 1), abemaciclib (Group 2), and abemaciclib + Saccharomyces boulardii (Group 3) groups. The clinical status, body weight, and defecation status were monitored daily. At the end of the 15-day experiment period, the rats were killed with high-dose anesthesia and the resected small intestine segments were evaluated histopathologically. Lesions were classified according to thickening of the villus, inflammation and edema of mucosa and intraepithelial leukocyte accumulation. Then, mean values of both crypt depths and villi thicknesses were calculated for each rat. Normal distribution assumption was controlled with the Shapiro-Wilk test. One-way analysis of variance for normally distributed variables in the comparisons of more than two independent groups and Kruskal-Wallis test for nonnormally distributed variables were used. The significance value was accepted as 0.05. RESULTS: : There was one death in Group 3, but none in the others. There were no findings of mucositis in Group I. There was mild diarrhea and weight loss in only one rat in Group 1. For the comparison of the severity of diarrhea (72.5%/39%) and weight loss (72.5%/45%), a decrease was found in Group 3 according to Group 2 (p < 0.01). Histopathological findings such as edema, inflammation, and intraepithelial leukocyte accumulation also showed a decrease in Group 3 compared to Group 2 (p < 0.01). DISCUSSION: Saccharomyces boulardii should be considered as a treatment option in abaemaciclib (chemotherapy)-induced diarrhea. Further comparative studies and in vivo human randomized controlled studies can be conducted in the future.

Prediction of a 10-year risk of type 2 diabetes mellitus in the Turkish population: A cross-sectional study.

ABSTRACT: According to the International Diabetes Federation, Turkey will be among the top 10 countries in the world with the highest prevalence of diabetes mellitus (DM) by 2045, with a speculated number of cases of 10.4 million.This study aimed to predict the 10-year risk of type 2 DM in a Turkish population, assess potential factors of the 10-year risk of DM, and assess the outcomes of Turkey's 2015 to 2020 program for DM.Individuals aged 20-64 years were categorized and stratified according to age (in ranges of 5 years), sex, and populations of family medicine centers to reflect the whole population. The Finnish Diabetes Risk Score, sociodemographic characteristics, body fat, muscle, bone ratio, blood pressure, and waist-to-height ratio were evaluated.We found that 9.5% (n = 71) of the population aged 20 to 64 years will have DM within the next 10 years. Low levels of education (odds ratio [OR]: 2.054; 95% confidence interval [CI]: 1.011-4.174), smoking cessation (OR: 2.636; 95% CI: 1.260-5.513), a waist-to-height ratio >0.5 (OR: 6.885; 95% CI: 2.301-20.602), body fat percentage (OR: 1.187; 95% CI: 1.130-1.247), high systolic blood pressure (OR: 1.025; 95% CI: 1.009-1.041), and alcohol consumption (beta-estimation: -0.690; OR: 0.501; 95% CI: 0.275-0.914) affect the 10-year risk of type 2 DM.Individuals at risk for DM can be easily identified using risk assessment tools in primary care; however, there is no active screening program in the healthcare system, and only proposals exist. In addition to screening, preventive measures should focus on raising awareness of DM, reducing body fat percentage and systolic blood pressure, and decreasing the waist-to-height ratio to <0.5.

Quantitative Assessment of Salivary Gland Parenchymal Vascularization Using Power Doppler Ultrasound and Superb Microvascular Imaging: A Potential Tool in the Diagnosis of Sjögren's Syndrome

Background: Primary Sjögren's syndrome is a chronic inflammatory autoimmune disease. Minor salivary gland biopsy is the gold standard for the diagnosis of primary Sjögren's syndrome. Superb microvascular imaging, power Doppler ultrasound, and color Doppler of the salivary glands represent non-invasive, non-irradiating modality for evaluating the vascularity of the salivary glands in the diagnosis and follow-up of primary Sjögren's syndrome. Aims: To evaluate the efficacy of superb microvascular imaging and vascularity index in salivary glands for the sonographic diagnosis of primary Sjögren's syndrome. Study Design: Prospective case-control study. Methods: Twenty participants with primary Sjögren's syndrome and 20 healthy subjects were included in the study. Both parotid glands and submandibular glands were evaluated by superb microvascular imaging, power Doppler ultrasound, and color Doppler. The diagnostic accuracy of superb microvascular imaging was compared using these techniques. Results: In the patient group, the vascularity index values of superb microvascular imaging in parotid glands and submandibular glands were 3.5±1.66, 5.06±1.94, respectively. While the same values were 1.0±0.98 and 2.44±1.34 in the control group (p≤0.001). In the patient group, the vascularity index values of power Doppler ultrasound in parotid glands and submandibular glands were 1.3±1.20 and 2.59±1.82, respectively. While the same values were 0.3±0.32 and 0.85±0.68 in the control group (p≤0.001). The superb microvascular imaging vascularity index cut-off value for the diagnosis of primary Sjögren's syndrome in parotid glands that maximizes the accuracy was 1.85 (area under the curve: 0.906; 95% confidence interval: 0.844, 0.968), and its sensitivity and specificity were 87.5% and 72.5%, respectively. While the superb microvascular imaging vascularity index cut-off value for the diagnosis of primary Sjögren's syndrome in submandibular gland that maximizes the accuracy was 3.35 (area under the curve: 0.873; 95% confidence interval: 0.800, 0.946), its sensitivity and specificity were 82.5% and 70%, respectively. Conclusion: Superb microvascular imaging with high reproducibility of the vascularity index has a higher sensitivity and specificity than the power Doppler ultrasound in the diagnosis of primary Sjögren's syndrome. It can be a noninvasive technique in the diagnosis of primary Sjögren's syndrome when used with clinical, laboratory and other imaging methods.

The evaluation of the distribution of CD133, CXCR1 and the tumor associated macrophages in different molecular subtypes of breast cancer.

Breast cancer has different molecular subtypes, which determine the prognosis and response to the treatment. CD133 is a marker for cancer stem cells in tumor microenvironment with diagnostic/therapeutic importance. The tumor associated macrophages (TAMs) interact with the cancer stem cells through the CXCR1 receptor. In this study, we wanted to investigate the expression of these markers in patients with different molecular subtypes, in order to detect pathophysiological mechanisms and new molecular targets for the prospective targeted therapies. In this study we hypothesized a difference in expression of these antigens among different subtypes. We investigated expression of antigens in breast cancer patients with luminal A (LA), luminal B (LB), HER2 overexpressing (HER2OE), triple negative (TN) subtypes (n=70) and control patients (n=10) without cancer diagnosis. We applied indirect immunohistochemistry and evaluated immunostaining. CD133 expression was at the periphery and CXCR1 expression was at the central area of the tumor. The cytoplasmic CXCR1, CD133 expressions and nuclear CD133 expression, which is prominent in the TN subtype, were observed in patients. There was a statistically significant difference between the groups for CD133 (p=0.004), CXCR1 (p=0.002) H-Score values and M2 macrophages/whole TAM ratios (p=0.022). Between the CD133 and CXCR1 H-scores, there was a weak positive correlation (r=0.249, p=0.035). This study showed the compartment specific expression of the CD133 and CXCR1 antigens in neoplastic cells. The use of CD133 as a stem cell marker may be limited to TN subtype, due to its heterogeneous expression.

MLSeq: Machine learning interface for RNA-sequencing data.

BACKGROUND 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.

Biological variation of peripheral blood T-lymphocytes.

BACKGROUND: Flow cytometric analysis of the lymphocyte subsets has become one of the most commonly used techniques in the routine clinical laboratory. It is frequently used in monitoring lymphocyte recovery after hematopoietic stem cell transplantation (HSCT), as well as diagnosis and treatment of acquired immunodeficiency syndrome (AIDS). Reliable biological variation (BV) data is needed for safe clinical application of these tests. In this study, similar preanalytical and analytical protocols to the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) checklist were followed and a stringent statistical approach was applied to define BV of T-lymphocytes. METHODS: During the 10 weeks study period, weekly blood samples were obtained from 30 healthy individuals (20 females, 10 males) and analyzed with Facs Canto (BD Biosciences, San Jose, CA, USA) analyzer using 4-colour BD Multitest CD3/CD8/CD45/CD4 reagents. Data were assessed in terms of normality, tendencies, outliers and variance homogeneity prior to applying coefficient of variance (CV)- analysis of variance (ANOVA) test. Sex-stratified within-individual (CVI) and between-individual (CVG) BV estimates of CD3+, CD3 + CD4+, CD3 + CD8+, and CD3 + CD4 + CD8+ T lymphocytes were calculated. RESULTS: No difference was found between males and females. Except for the CD3 + CD4 + CD8+ subset, stable BV was found for CD3+, CD3 + CD4+, and CD3 + CD8+ subsets. CONCLUSSION: Instead of using the conventional reference ranges of CD3+, CD3 + CD4+ and CD3 + CD8+ counts for monitoring HIV positive or post-HSCT patients, RCV should be used. Because individualityis characteristic of lymphocytes subsets RCVs should be used instead of RIs for patient monitoring.

Retinal ganglion cell complex and peripapillary retinal nerve fiber layer thicknesses following carotid endarterectomy.

PURPOSE: To examine changes in retinal ganglion cell complex (GCC) and peripapillary retinal nerve fiber layer (RNFL) thicknesses by optical coherence tomography (OCT) in contralateral and ipsilatateral eyes of carotid artery stenosis (CAS) patients before and after carotid endarterectomy (CEA). METHODS: Forty-two consecutive patients diagnosed with CAS (70-99% stenosis rate) who underwent CEA were included in this prospective cross-sectional study. The indication for CEA was based on the Asymptomatic Carotid Atherosclerosis Study. Doppler ultrasonography and computed tomography angiography were performed to calculate CAS. All the subjects underwent an ophthalmological examination, including best corrected visual acuity (BCVA), intraocular pressure (IOP) measurements, biomicroscopy, fundoscopy, and OCT before and after the surgery. RESULTS: The mean preoperative intraocular pressure was 15.2 ± 2.1 mmHg in the ipsilateral eye and 15.8 ± 2.7 in the contralateral eye. The mean postoperative intraocular pressure in the ipsilateral and contralateral eye was 18.6 ± 3.0 and 19.3 ± 3.8, respectively. The intraocular pressure was significantly higher in postoperative eyes (p = 0.0001). There was a statistically significant decrease in peripapillary RNFL thickness in superior quadrants postoperatively in ipsilateral eyes. The retinal GCC layer thickness was not significantly different before and after CEA in ipsilateral and contralateral eyes. CONCLUSIONS: Carotid endarterectomy results in thinning of the superior peripapillary RNFL thickness. To the best of our knowledge, this is the first study to examine peripapillary RNFL and GCC thicknesses before and after CEA.

voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data.

RNA-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 mean 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 Poisson 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 microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.

geneSurv: An interactive web-based tool for survival analysis in genomics research.

Survival analysis methods are often used in cancer studies. It has been shown that the combination of clinical data with genomics increases the predictive performance of survival analysis methods. But, this leads to a high-dimensional data problem. Fortunately, new methods have been developed in the last decade to overcome this problem. However, there is a strong need for easily accessible, user-friendly and interactive tool to perform survival analysis in the presence of genomics data. We developed an open-source and freely available web-based tool for survival analysis methods that can deal with high-dimensional data. This tool includes classical methods, such as Kaplan-Meier, Cox proportional hazards regression, and advanced methods, such as penalized Cox regression and Random Survival Forests. It also offers an optimal cutoff determination method based on maximizing several test statistics. The tool has a simple and interactive interface, and it can handle high dimensional data through feature selection and ensemble methods. To dichotomize gene expressions, geneSurv can identify optimal cutoff points. Users can upload their microarray, RNA-Seq, chip-Seq, proteomics, metabolomics or clinical data as a nxp dimensional data matrix, where n refers to samples and p refers to genes. This tool is available free at www.biosoft.hacettepe.edu.tr/geneSurv. All source code is available at https://github.com/selcukorkmaz/geneSurv under the GPL-3 license.

Drug/nondrug classification using Support Vector Machines with various feature selection strategies.

In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM-Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.