Search results for "Statistics"

Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial

2020

AbstractMyotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3’-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to trans-dysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important…

Italian young doctors’ knowledge, attitudes and practices on antibiotic use and resistance: A national cross-sectional survey

2020

Abstract Objectives Antimicrobial resistance (AMR) is one of the major health issues worldwide. Clinicians should play a central role to fight AMR, and medical training is a pivotal issue to combat it; therefore, assessing levels of knowledge, attitudes and practices among young doctors is essential for future antimicrobial stewardship (AMS) programmes. Methods A nationwide, cross-sectional, multicentre survey was conducted in Italy. A descriptive analysis of knowledge and attitudes was performed, along with a univariate and multivariate analysis of their determinants. Results Overall, 1179 young doctors accessed the survey and 1055 (89.5%) completed all sections. Regarding the knowledge se…

Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria?

2017

International audience; Bacterial typing is crucial to tackle the spread of bacterial pathogens but current methods are time-consuming and costly. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been recently integrated into the microbiology laboratory workflow for a quick and low-cost microbial species identification. Independent research groups have successfully redirected the original function of this technology from their primary purpose to discriminate subgroups within pathogen species. However, identical bacterial subgroups could be identified by unrelated peaks by independent methods, thus limiting their robustness and exportability. We…

Toward a direct and scalable identification of reduced models for categorical processes.

2017

The applicability of many computational approaches is dwelling on the identification of reduced models defined on a small set of collective variables (colvars). A methodology for scalable probability-preserving identification of reduced models and colvars directly from the data is derived—not relying on the availability of the full relation matrices at any stage of the resulting algorithm, allowing for a robust quantification of reduced model uncertainty and allowing us to impose a priori available physical information. We show two applications of the methodology: (i) to obtain a reduced dynamical model for a polypeptide dynamics in water and (ii) to identify diagnostic rules from a standar…

Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling

2016

Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on …

2020

Background Small sample sizes combined with multiple correlated endpoints pose a major challenge in the statistical analysis of preclinical neurotrauma studies. The standard approach of applying univariate tests on individual response variables has the advantage of simplicity of interpretation, but it fails to account for the covariance/correlation in the data. In contrast, multivariate statistical techniques might more adequately capture the multi-dimensional pathophysiological pattern of neurotrauma and therefore provide increased sensitivity to detect treatment effects. Results We systematically evaluated the performance of univariate ANOVA, Welch’s ANOVA and linear mixed effects models …

Lipid levels, atrial fibrillation and the impact of age:Results from the LIPIDOGRAM2015 study

2020

Background and aims: An inverse relationship between lipid levels and atrial fibrillation (AF) has been suggested, but whether the association is upheld for all age groups remains unclear. The aim of the study was to examine associations between lipid levels and AF by age groups in a nationwide study in Poland. Methods: Multivariate Poisson regression models were used to estimate prevalence ratios (PRs) for AF by lipid levels. Low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), total cholesterol (TC), non-HDL-C and LDL-C/HDL-C ratios were grouped into quartiles. Results: Of the 13,724 participants, 5.2% (n = 708) had AF. People with…

A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model.

2018

International audience; In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clust…

Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research

2015

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate glo…

Melanoma-Nevus Discrimination Based on Image Statistics in Few Spectral Channels

2016

The purpose of this paper is to offer a method for discrimination of cutaneous melanoma from benign nevus, founded on analysis of skin lesion image. At the core of method is calculation of mean and standard deviation of pixel optical density values for a few narrow spectral bands. Calculated values are compared with discriminating thresholds derived from a set of images of benign nevi and melanomas with known diagnosis. Classification is done applying weighted majority rule to results of thresholding. Verification against the available multispectral images of 32 melanomas and 94 benign nevi has shown that the method using three spectral bands provided zero false negative and four false posi…