Search results for "Tributi"

A naturally occuring triterpene saponin ardisiacrispin B displayed cytotoxic effects in multi-factorial drug resistant cancer cells via ferroptotic a…

2018

WOS: 000432722700010

Environmental factors influencing the distribution and prevalence ofSchistosoma haematobiumin school attenders of ILembe and uThungulu Health Distric…

2017

Schistosoma haematobium infection is reported to facilitate the development of urogenital diseases. Its symptoms include haematuria, dysuria and tiredness, and it may cause cognitive decline in chi...

2021

Despite recent advancements in tumor therapy, metastasis and tumor relapse remain major complications hindering the complete recovery of many cancer patients. Dormant tumor cells, which reside in the body, possess the ability to re-enter the cell cycle after therapy. This phenomenon has been attributed to therapy-induced senescence. We show that these cells could be targeted by the use of zinc oxide nanoparticles (ZnO NPs). In the present study, the properties of tumor cells after survival of 16 Gy gamma-irradiation were investigated in detail. Analysis of morphological features, proliferation, cell cycle distribution, and protein expression revealed classical hallmarks of senescent cells a…

Diagnostic and Prognostic Relevance of Red Blood Cell Distribution Width for Vascular Aging and Cardiovascular Diseases.

2019

Evidence suggests association of red blood cell distribution width (RDW) with cardiovascular diseases (CVDs). On the contrary, we underline that the sole RDW values cannot represent a valid CVD biomarker. High RDW values are expression of biological effects of a lot of both endogenous and exogenous factors (i.e., age, sex, genetic background, inflammation, hormones, drugs, diet, exercise, hematological analyzers, and ranges of values), modulating the biology and physiology of erythrocytes. Thus, the singular monitoring of RDW cannot be used to predict cardiovascular disorders. Accordingly, we have reviewed the evidence for potential relationship of RDW values with alterations in the cardiov…

SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations

2016

Various approaches to calling single-nucleotide variants (SNVs) or insertion-or-deletion (indel) mutations have been developed based on next-generation sequencing (NGS). However, most of them are dedicated to a particular type of mutation, e.g. germline SNVs in normal cells, somatic SNVs in cancer/tumor cells, or indels only. In the literature, efficient and integrated callers for both germline and somatic SNVs/indels have not yet been extensively investigated. We present SNVSniffer, an efficient and integrated caller identifying both germline and somatic SNVs/indels from NGS data. In this algorithm, we propose the use of Bayesian probabilistic models to identify SNVs and investigate a mult…

Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony

2016

In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relati…

Two-Stage Bayesian Approach for GWAS With Known Genealogy

2019

Genome-wide association studies (GWAS) aim to assess relationships between single nucleotide polymorphisms (SNPs) and diseases. They are one of the most popular problems in genetics, and have some peculiarities given the large number of SNPs compared to the number of subjects in the study. Individuals might not be independent, especially in animal breeding studies or genetic diseases in isolated populations with highly inbred individuals. We propose a family-based GWAS model in a two-stage approach comprising a dimension reduction and a subsequent model selection. The first stage, in which the genetic relatedness between the subjects is taken into account, selects the promising SNPs. The se…

Partitioned learning of deep Boltzmann machines for SNP data.

2016

Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…

L1-Penalized Censored Gaussian Graphical Model

2018

Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithm…

Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

2016

Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l 1-penalized maximum likelihood, imposing a further constraint on the absolute value…