Ordering Quantiles through Confidence Statements

Ranking variables according to their relevance to predict an outcome is an important task in biomedicine. For instance, such ranking can be used for selecting a smaller number of genes for then applying other sophisticated experiments only on genes identified as important. A nonparametric method called Quor is designed to provide a confidence value for the order of arbitrary quantiles of different populations using independent samples. This confidence may provide insights about possible differences among groups and yields a ranking of importance for the variables. Computations are efficient and use exact distributions with no need for asymptotic considerations. Experiments with simulated data and with multiple real -omics data sets are performed, and they show advantages and disadvantages of the method. Quor has no assumptions but independence of samples, thus it might be a better option when assumptions of other methods cannot be asserted. The software is publicly available on CRAN

Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

Abstract Micro-computed tomography (μCT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on μCT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-μCT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-μCT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-μCT medical images

a new clinicobiological scoring system for the prediction of infection related mortality and survival after allogeneic hematopoietic stem cell transplantation

Abstract Infection-related mortality (IRM) is a substantial component of nonrelapse mortality (NRM) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). No scores have been developed to predict IRM before transplantation. Pretransplantation clinical and biochemical data were collected from a study cohort of 607 adult patients undergoing allo-HSCT between January 2009 and February 2017. In a training set of 273 patients, multivariate analysis revealed that age >60 years ( P  = .003), cytomegalovirus host/donor serostatus different from negative/negative ( P P  = .004), and pretransplantation IgM level P  = .028) were independent predictors of increased IRM. Based on these results, we developed and subsequently validated a 3-tiered weighted prognostic index for IRM in a retrospective set of patients (n = 219) and a prospective set of patients (n = 115). Patients were assigned to 3 different IRM risk classes based on this index score. The score significantly predicted IRM in the training set, retrospective validation set, and prospective validation set ( P P P

Bayesian network data imputation with application to survival tree analysis

\u3cp\u3eRetrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation-maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).\u3c/p\u3

First independent evaluation of QuantiFERON-TB Plus performance

Tuberculosis elimination requires an effective strategy to diagnose and treat people infected with Mycobacterium tuberculosis who would otherwise be at high risk of developing and transmitting active disease [1, 2]. The diagnostic tools for latent tuberculosis infection (LTBI) are the tuberculin skin test (TST) and the T-cell interferon-γ release assays (IGRAs). Two IGRAs are commercially available, QuantiFERON-TB Gold In-Tube (QFT-GIT) (Qiagen, Hilden, Germany) and T-SPOT.TB (Oxford Immunotec, Abingdon, UK). Compared to the TST, IGRAs offer operational advantages and higher specificity in the bacille Calmette–Guérin (BCG)-vaccinated population [3], and they are at least as sensitive for LTBI [4]. However, IGRAs have limitations: reduced sensitivity in children and immunocompromised subjects, including HIV-infected individuals [3, 4]; failure to discriminate between active tuberculosis and LTBI; and poor correlation with the risk of progression to active disease [3]

Immunogenetics features and genomic lesions in splenic marginal zone lymphoma

Splenic marginal zone lymphomas (MZL) express mutated (M)) or unmutated (U)) immunoglobulin heavy chain (IGHV) genes. To investigate the IGHV mutational status impact on genetic lesions, this study combined single nucleotide polymorphism-arrays and IGHV sequencing in 83 cases. Clinical features and outcome were similar between U- and M-IGHV cases. Recurrent lesions frequency was higher in U-IGHV cases, including poor prognosticators. Frequencies differed among cases bearing individual IGHV genes or lambda light chains. In conclusion, SMZL comprises subgroups based on genetic abnormalities and immunogenetic status. Genomic lesion frequency differed and was higher in U-IGHV cases, possibly affecting the outcome