Search results for "DATA"

Effects of quality and quantity of protein intake for type 2 Diabetes Mellitus prevention and metabolic control

2020

Purpose of Review: The aim of this review is to evaluate the ideal protein quality and quantity and the dietary composition for the prevention and metabolic control of type 2 diabetes mellitus (T2DM). Introduction: Although some reviews demonstrate the advantages of a diet with a higher protein intake, other reviews have observed that a diet high in carbohydrates, with low-glycaemic index carbohydrates and good fibre intake, is equally effective in improving insulin sensitivity. Methods: Over 2831 articles were screened, and 24 from the last 5 years were analysed and summarised for this review, using the protein, diabetes and insulin glucose metabolic keywords in Pubmed in June 2019. Result…

Farber disease: design of the first observational and cross-sectional cohort study capturing retrospective and prospective data on the natural histor…

2017

Fishing anti-inflammatories from known drugs: In silico repurposing, design, synthesis and biological evaluation of bisacodyl analogues

2017

Herein is described in silico repositioning, design, synthesis, biological evaluation and structure-activity relationship (SAR) of an original class of anti-inflammatory agents based on a polyaromatic pharmacophore structurally related to bisacodyl (BSL) drug used in therapeutic as laxative. We describe the potential of TOMOCOMD-CARDD methods to find out new anti-inflammatory drug-like agents from a diverse series of compounds using the total and local atom based bilinear indices as molecular descriptors. The models obtained were validated by biological studies, identifying BSL as the first anti-inflammatory lead-like using in silico repurposing from commercially available drugs. Several bi…

Drugs Polypharmacology by in Silico Methods: New Opportunities in Drug Discovery

2016

Background Polypharmacology, defined as the modulation of multiple proteins rather than a single target to achieve a desired therapeutic effect, has been gaining increasing attention since 1990s, when industries had to withdraw several drugs due to their adverse effects, leading to permanent injuries or death, with multi-billiondollar legal damages. Therefore, if up to then the "one drug one target" paradigm had seen many researchers interest focused on the identification of selective drugs, with the strong expectation to avoid adverse drug reactions (ADRs), very recently new research strategies resulted more appealing even as attempts to overcome the decline in productivity of the drug dis…

A deeper look into natural sciences with physics-based and data-driven measures

2021

Summary With the development of machine learning in recent years, it is possible to glean much more information from an experimental data set to study matter. In this perspective, we discuss some state-of-the-art data-driven tools to analyze latent effects in data and explain their applicability in natural science, focusing on two recently introduced, physics-motivated computationally cheap tools—latent entropy and latent dimension. We exemplify their capabilities by applying them on several examples in the natural sciences and show that they reveal so far unobserved features such as, for example, a gradient in a magnetic measurement and a latent network of glymphatic channels from the mous…

Discovering Differential Equations from Earth Observation Data

2020

Modeling and understanding the Earth system is a constant and challenging scientific endeavour. When a clear mechanistic model is unavailable, complex or uncertain, learning from data can be an alternative. While machine learning has provided excellent methods for detection and retrieval, understanding the governing equations of the system from observational data seems an elusive problem. In this paper we introduce sparse regression to uncover a set of governing equations in the form of a system of ordinary differential equations (ODEs). The presented method is used to explicitly describe variable relations by identifying the most expressive and simplest ODEs explaining data to model releva…

Inferring causation from time series in earth system sciences

2019

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

The why, the how and the when of PGS 2.0

2016

STUDY QUESTION: We wanted to probe the opinions and current practices on preimplantation genetic screening (PGS), and more specifically on PGS in its newest form: PGS 2.0? STUDY FINDING: Consensus is lacking on which patient groups, if any at all, can benefit from PGS 2.0 and, a fortiori, whether all IVF patients should be offered PGS. WHAT IS KNOWN ALREADY: It is clear from all experts that PGS 2.0 can be defined as biopsy at the blastocyst stage followed by comprehensive chromosome screening and possibly combined with vitrification. Most agree that mosaicism is less of an issue at the blastocyst stage than at the cleavage stage but whether mosaicism is no issue at all at the blastocyst st…

Graph Theoretical Framework of Brain Networks in Multiple Sclerosis: A Review of Concepts.

2019

Abstract Network science provides powerful access to essential organizational principles of the human brain. It has been applied in combination with graph theory to characterize brain connectivity patterns. In multiple sclerosis (MS), analysis of the brain networks derived from either structural or functional imaging provides new insights into pathological processes within the gray and white matter. Beyond focal lesions and diffuse tissue damage, network connectivity patterns could be important for closely tracking and predicting the disease course. In this review, we describe concepts of graph theory, highlight novel issues of tissue reorganization in acute and chronic neuroinflammation an…

Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation

2018

Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REA…