University of Ioannina

About: University of Ioannina is a education organization based out in Ioannina, Greece. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 7654 authors who have published 20594 publications receiving 671560 citations. The organization is also known as: Panepistimio Ioanninon.

Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal

Abstract: Mounting evidence suggests that there is frequently considerable variation in the risk of the outcome of interest in clinical trial populations. These differences in risk will often cause clinically important heterogeneity in treatment effects (HTE) across the trial population, such that the balance between treatment risks and benefits may differ substantially between large identifiable patient subgroups; the "average" benefit observed in the summary result may even be non-representative of the treatment effect for a typical patient in the trial. Conventional subgroup analyses, which examine whether specific patient characteristics modify the effects of treatment, are usually unable to detect even large variations in treatment benefit (and harm) across risk groups because they do not account for the fact that patients have multiple characteristics simultaneously that affect the likelihood of treatment benefit. Based upon recent evidence on optimal statistical approaches to assessing HTE, we propose a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary subgroup analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroup analyses (performed to inform future research). A standardized and transparent approach to HTE assessment and reporting could substantially improve clinical trial utility and interpretability.

Self-healing materials: A review of advances in materials, evaluation, characterization and monitoring techniques

Abstract: Self-healing materials are attracting increasing interest of the research community, over the last decades, due to their efficiency in detecting and “autonomically” healing damage. Numerous attempts are being presented every year focusing on the development of different self-healing systems as well as their integration to large scale production with the best possible property–cost relationship. The current work aims to present the most recent breakthroughs in these attempts from many different research groups published during the last five years. The current review focuses in polymeric systems and their composites. The reviewed literature is presented in three distinct categories, based on three different scopes of interest. These categories are (i) the materials and systems employed, (ii) the experimental techniques for the evaluation of materials properties and self-healing efficiency of the materials/structures and (iii) the characterization techniques utilized in order to evaluate (off-line) and monitor (on-line) the healing efficiency of the proposed systems. Published works are presented separately in all the different categories, thus the interested reader is advised to follow the structure of the review and refer to the chapter of interest.

UV filters : From sunscreens to human body and the environment

Abstract: Recognition of the harmful effects of ultraviolet (UV) radiation on the skin has triggered development of organic chemicals (commonly referred as UV filters) that can absorb UV radiation and attenuate the negative effects of sunlight exposure. Depending on the properties and the intended degree of protection, a wide array of combinations is being marketed as delivering protection against most kinds of UV-induced skin damage. However, some UV filters have dermatological implications, so maximum applicable concentrations have been established. To monitor to what extent commercial products comply with the mandatory limits, several analytical methods have been used for their determination in cosmetics and related products. Further research on the efficacy of UV filters applied on the skin surface has brought to light a gradual attenuation of their UV-protective capacity that cannot solely be attributed to photo-induced decomposition. Investigations carried out to elucidate the reasons underlying this behaviour concluded that UV filters may be systematically absorbed through the skin surface or even released during bathing and washing activities. These observations gave rise to numerous studies aiming to investigate the magnitude and effects of skin penetration as well as accumulation in the water environment. Because of the need for more in-depth investigation into the behavior of UV filters, the initial demand for product certification has been extended to include reliable analytical methods to determine these substances at low concentration levels and in complex matrices (e.g., biological and environmental samples). Until now, most of the available methods, although designed to cover a large variety of substances, quantify them at only high-mg/L levels; however, recently, researchers have paid special attention to developing more sensitive procedures able to determine these substances in biological tissues and fluids or environmental samples at ng/L levels without matrix interferences. This article gives a comprehensive outline of the accumulated knowledge on UV-filter determination in biological and environmental samples and encourages further research in this new, challenging field of analytical, health and environmental science.

Artificial Neural Networks for Solving Ordinary and Partial Differential Equations

Abstract: We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the boundary (or initial) conditions and contains no adjustable parameters. The second part is constructed so as not to affect the boundary conditions. This part involves a feedforward neural network, containing adjustable parameters (the weights). Hence by construction the boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ODE's, to systems of coupled ODE's and also to PDE's. In this article we illustrate the method by solving a variety of model problems and present comparisons with finite elements for several cases of partial differential equations.

Interpretation of tests of heterogeneity and bias in meta-analysis.

Abstract: Statistical tests of heterogeneity and bias, in particular publication bias, are very popular in meta-analyses. These tests use statistical approaches whose limitations are often not recognized. Moreover, it is often implied with inappropriate confidence that these tests can provide reliable answers to questions that in essence are not of statistical nature. Statistical heterogeneity is only a correlate of clinical and pragmatic heterogeneity and the correlation may sometimes be weak. Similarly, statistical signals may hint to bias, but seen in isolation they cannot fully prove or disprove bias in general, let alone specific causes of bias, such as publication bias in particular. Both false-positive and false-negative signals of heterogeneity and bias can be common and their prevalence may be anticipated based on some rational considerations. Here I discuss the major common challenges and flaws that emerge in using and interpreting statistical tests of heterogeneity and bias in meta-analyses. I discuss misinterpretations that can occur at the level of statistical inference, clinical/pragmatic inference and specific cause attribution. Suggestions are made on how to avoid these flaws, use these tests properly and learn from them.