Non-invasive diagnostic tests for Helicobacter pylori infection

BACKGROUND: Helicobacter pylori (H pylori) infection has been implicated in a number of malignancies and non-malignant conditions including peptic ulcers, non-ulcer dyspepsia, recurrent peptic ulcer bleeding, unexplained iron deficiency anaemia, idiopathic thrombocytopaenia purpura, and colorectal adenomas. The confirmatory diagnosis of H pylori is by endoscopic biopsy, followed by histopathological examination using haemotoxylin and eosin (H & E) stain or special stains such as Giemsa stain and Warthin-Starry stain. Special stains are more accurate than H & E stain. There is significant uncertainty about the diagnostic accuracy of non-invasive tests for diagnosis of H pylori. OBJECTIVES: To compare the diagnostic accuracy of urea breath test, serology, and stool antigen test, used alone or in combination, for diagnosis of H pylori infection in symptomatic and asymptomatic people, so that eradication therapy for H pylori can be started. SEARCH METHODS: We searched MEDLINE, Embase, the Science Citation Index and the National Institute for Health Research Health Technology Assessment Database on 4 March 2016. We screened references in the included studies to identify additional studies. We also conducted citation searches of relevant studies, most recently on 4 December 2016. We did not restrict studies by language or publication status, or whether data were collected prospectively or retrospectively. SELECTION CRITERIA: We included diagnostic accuracy studies that evaluated at least one of the index tests (urea breath test using isotopes such as13C or14C, serology and stool antigen test) against the reference standard (histopathological examination using H & E stain, special stains or immunohistochemical stain) in people suspected of having H pylori infection. DATA COLLECTION AND ANALYSIS: Two review authors independently screened the references to identify relevant studies and independently extracted data. We assessed the methodological quality of studies using the QUADAS-2 tool. We performed meta-analysis by using the hierarchical summary receiver operating characteristic (HSROC) model to estimate and compare SROC curves. Where appropriate, we used bivariate or univariate logistic regression models to estimate summary sensitivities and specificities. MAIN RESULTS: We included 101 studies involving 11,003 participants, of which 5839 participants (53.1%) had H pylori infection. The prevalence of H pylori infection in the studies ranged from 15.2% to 94.7%, with a median prevalence of 53.7% (interquartile range 42.0% to 66.5%). Most of the studies (57%) included participants with dyspepsia and 53 studies excluded participants who recently had proton pump inhibitors or antibiotics.There was at least an unclear risk of bias or unclear applicability concern for each study.Of the 101 studies, 15 compared the accuracy of two index tests and two studies compared the accuracy of three index tests. Thirty-four studies (4242 participants) evaluated serology; 29 studies (2988 participants) evaluated stool antigen test; 34 studies (3139 participants) evaluated urea breath test-13C; 21 studies (1810 participants) evaluated urea breath test-14C; and two studies (127 participants) evaluated urea breath test but did not report the isotope used. The thresholds used to define test positivity and the staining techniques used for histopathological examination (reference standard) varied between studies. Due to sparse data for each threshold reported, it was not possible to identify the best threshold for each test.Using data from 99 studies in an indirect test comparison, there was statistical evidence of a difference in diagnostic accuracy between urea breath test-13C, urea breath test-14C, serology and stool antigen test (P = 0.024). The diagnostic odds ratios for urea breath test-13C, urea breath test-14C, serology, and stool antigen test were 153 (95% confidence interval (CI) 73.7 to 316), 105 (95% CI 74.0 to 150), 47.4 (95% CI 25.5 to 88.1) and 45.1 (95% CI 24.2 to 84.1). The sensitivity (95% CI) estimated at a fixed specificity of 0.90 (median from studies across the four tests), was 0.94 (95% CI 0.89 to 0.97) for urea breath test-13C, 0.92 (95% CI 0.89 to 0.94) for urea breath test-14C, 0.84 (95% CI 0.74 to 0.91) for serology, and 0.83 (95% CI 0.73 to 0.90) for stool antigen test. This implies that on average, given a specificity of 0.90 and prevalence of 53.7% (median specificity and prevalence in the studies), out of 1000 people tested for H pylori infection, there will be 46 false positives (people without H pylori infection who will be diagnosed as having H pylori infection). In this hypothetical cohort, urea breath test-13C, urea breath test-14C, serology, and stool antigen test will give 30 (95% CI 15 to 58), 42 (95% CI 30 to 58), 86 (95% CI 50 to 140), and 89 (95% CI 52 to 146) false negatives respectively (people with H pylori infection for whom the diagnosis of H pylori will be missed).Direct comparisons were based on few head-to-head studies. The ratios of diagnostic odds ratios (DORs) were 0.68 (95% CI 0.12 to 3.70; P = 0.56) for urea breath test-13C versus serology (seven studies), and 0.88 (95% CI 0.14 to 5.56; P = 0.84) for urea breath test-13C versus stool antigen test (seven studies). The 95% CIs of these estimates overlap with those of the ratios of DORs from the indirect comparison. Data were limited or unavailable for meta-analysis of other direct comparisons. AUTHORS' CONCLUSIONS: In people without a history of gastrectomy and those who have not recently had antibiotics or proton ,pump inhibitors, urea breath tests had high diagnostic accuracy while serology and stool antigen tests were less accurate for diagnosis of Helicobacter pylori infection.This is based on an indirect test comparison (with potential for bias due to confounding), as evidence from direct comparisons was limited or unavailable. The thresholds used for these tests were highly variable and we were unable to identify specific thresholds that might be useful in clinical practice.We need further comparative studies of high methodological quality to obtain more reliable evidence of relative accuracy between the tests. Such studies should be conducted prospectively in a representative spectrum of participants and clearly reported to ensure low risk of bias. Most importantly, studies should prespecify and clearly report thresholds used, and should avoid inappropriate exclusions

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. (C) 2014 Elsevier Ireland Ltd. All rights reserved

MVN: An R Package for Assessing Multivariate Normality

Assessing the assumption of multivariate normality is required by many parametric multivariate statistical methods, such as MANOVA, linear discriminant analysis, principal component analysis, canonical correlation, etc. It is important to assess multivariate normality in order to proceed with such statistical methods. There are many analytical methods proposed for checking multivariate normality. However, deciding which method to use is a challenging process, since each method may give different results under certain conditions. Hence, we may say that there is no best method, which is valid under any condition, for normality checking. In addition to numerical results, it is very useful to use graphical methods to decide on multivariate normality. Combining the numerical results from several methods with graphical approaches can be useful and provide more reliable decisions. Here, we present an R package, MVN, to assess multivariate normality. It contains the three most widely used multivariate normality tests, including Mardia's, Henze-Zirkler's and Royston's, and graphical approaches, including chi-square Q-Q, perspective and contour plots. It also includes two multivariate outlier detection methods, which are based on robust Mahalanobis distances. Moreover, this package offers functions to check the univariate normality of marginal distributions through both tests and plots. Furthermore, especially for non-R users, we provide a user-friendly web application of the package. This application is available at http://www.biosoft.hacettepe.edu.tr/MVN/

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

Compliance of abstracts of randomized control trials with CONSORT guidelines: A case study of Balkan journals

Background: Published reports of randomized controlled trials (RCTs) are not compliant with the CONSORT checklist as much as they should.Objective: To assess the quality, in terms of the level to which they are compliant with the CONSORT checklist, of abstracts of RCTs published in general medical journals in the Balkan region.Methods: Two observers assessed the abstracts of RCTs published in five general medical journals of the Balkan region between 2012 and 2018 to determine the level to which the abstracts were compliant with the 16-item CONSORT abstracts checklist.Results: Of the 183 studies that were identified for evaluation, 124 (67.8%) were excluded from the evaluation. The average compliance level was 44.5% (95%   CI: 41.9%–47.1%), the lowest being that for randomization (1.7%), funding (1.7%),numbers analysed (11.0%), blinding (13.6%), and trial registration (18.6%). However, the compliance level was very high for conclusions (99.2%), objectives (96.6%), interventions (95.8%), and primary outcomes (83.9%). The length of the abstract (word count) and the level of compliance were positively correlated (rs = 0.43; p = 0.001). Abstracts of trials published in journals that endorse CONSORT in their publication policies were more compliant than those published in other journals (47.5 ± 10.4 versus 40.8 ± 8.0, p = 0.024).Conclusion: The overall level of compliance with the CONSORT checklist was below 50%. To improve the quality of abstracts of RCTs, authors should be encouraged to use the CONSORT checklist, and editors should check compliance with the CONSORT guidelines as part the publishing workflow

Compliance of abstracts of randomized control trials with CONSORT guidelines: A case study of Balkan journals

Background: Published reports of randomized controlled trials (RCTs) are not compliant with the CONSORT checklist as much as they should.Objective: To assess the quality, in terms of the level to which they are compliant with the CONSORT checklist, of abstracts of RCTs published in general medical journals in the Balkan region.Methods: Two observers assessed the abstracts of RCTs published in five general medical journals of the Balkan region between 2012 and 2018 to determine the level to which the abstracts were compliant with the 16-item CONSORT abstracts checklist.Results: Of the 183 studies that were identified for evaluation, 124 (67.8%) were excluded from the evaluation. The average compliance level was 44.5% (95%   CI: 41.9%–47.1%), the lowest being that for randomization (1.7%), funding (1.7%),numbers analysed (11.0%), blinding (13.6%), and trial registration (18.6%). However, the compliance level was very high for conclusions (99.2%), objectives (96.6%), interventions (95.8%), and primary outcomes (83.9%). The length of the abstract (word count) and the level of compliance were positively correlated (rs = 0.43; p = 0.001). Abstracts of trials published in journals that endorse CONSORT in their publication policies were more compliant than those published in other journals (47.5 ± 10.4 versus 40.8 ± 8.0, p = 0.024).Conclusion: The overall level of compliance with the CONSORT checklist was below 50%. To improve the quality of abstracts of RCTs, authors should be encouraged to use the CONSORT checklist, and editors should check compliance with the CONSORT guidelines as part the publishing workflow

easyROC: An Interactive Web-tool for ROC Curve Analysis Using R Language Environment

ROC curve analysis is a fundamental tool for evaluating the performance of a marker in a number of research areas, e.g., biomedicine, bioinformatics, engineering etc., and is frequently used for discriminating cases from controls. There are a number of analysis tools which are used to guide researchers through their analysis. Some of these tools are commercial and provide basic methods for ROC curve analysis while others offer advanced analysis techniques and a command-based user interface, such as the R environment. The R environmentg includes comprehensive tools for ROC curve analysis; however, using a command-based interface might be challenging and time consuming when a quick evaluation is desired; especially for non-R users, physicians etc. Hence, a quick, comprehensive, free and easy-to-use analysis tool is required. For this purpose, we developed a user-friendly web-tool based on the R language. This tool provides ROC statistics, graphical tools, optimal cutpoint calculation, comparison of several markers, and sample size estimation to support researchers in their decisions without writing R codes. easyROC can be used via any device with an internet connection independently of the operating system. The web interface of easyROC is constructed with the R package shiny. This tool is freely available through www.biosoft.hacettepe.edu.tr/easyROC

Three-dimensional Spinal Deformity: Scoliosis [Uc Boyutlu Omurga Deformitesi: Skolyoz]

In human body, average of 33 separate vertebrae of the spinal column is sorted and connected to each other in a row. The primary task of this column is to support the head, chest and abdominal organs and form a stable and strong sheath to the spinal canal where the medulla spinal passes through. Spinal column of a new born baby is straight, but after the baby starts to hold his head, cervical lordosis is formed. After the baby starts to sit and stands up, thoracic kyphosis, lumbar lordosis and sacral kyphosis develops. Although, normal physiological curves of spine is normal, deviations from the front or rear view is considered pathological. Scoliosis is characterized as lateral deviation, the reduction in sagittal slope and axial rotation. Scoliosis can also be defined as the deviation of the normal vertical line, deviating more than 10 degrees, as seen in X-ray. All treatment methods used in the treatment of scoliosis aims to perform physically normal, balanced, painless and a stable backbone. Although, the treatment of Scolyosis is performed in several different ways, further studies are still being conducted. [Med-Science 2015; 4(1.000): 1796-808