1,435 research outputs found
Spotlight on dupilumab in the treatment of atopic dermatitis: design, development, and potential place in therapy
No full textAngelo Massimiliano D’Erme,1,2 Marco Romanelli,2 Andrea Chiricozzi2 1Dermatology Unit, Livorno Hospital, Livorno, 2Dermatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy Abstract: Atopic dermatitis (AD) is among the most common inflammatory skin diseases in children and adults in industrialized countries. Up to one-third of adults (probably a smaller proportion in childhood) suffer from moderate-to-severe AD, whose recommended treatment is usually based on systemic therapies. The currently available therapeutics are limited, and AD management becomes challenging in most cases. Over the last few years, new advances in the understanding of AD pathogenic mechanisms and inflammatory pathways have led to the identification of specific therapeutic targets and new molecules have been tested. Dupilumab is a fully human monoclonal antibody directed against the IL-4 receptor α subunit that is able to block the signaling of both IL-4 and IL-13 and achieve rapid and significant improvements in adults with moderate-to-severe AD. Dupilumab is ready to inaugurate a long and promising biological target treatment option for Th2 cell-mediated atopic immune response that characterizes AD. Keywords: dupilumab, atopic dermatitis, eczema, IL-4, IL-13, biologic
The world of wounds comes together
No full textWe are fast approaching the fifth World Union of Wound Healing Societies (WUWHS) conference, to be held in Florence next September 25–29 (www.wuwhs2016.com). The spirit of WUWHS was always to offer a valid opportunity to wound practitioners and other people involved to get together and exchange latest advances in the field of wound management. The scientific programme in Florence is exciting with over 300 confirmed invited speakers from all over the world and more than 1000 free abstracts, to be distributed as oral and poster presentations. The scientific sessions have been completely redesigned by the scientific committee, compared with previous WUWHS conferences and updated with brand new topics in basic and clinical science. The Rising Star concept will be introduced in Florence for the first time at a WUWHS conference and selected young scientists will be given the title Rising Star for their contribution to the scientific programme. We are also proud to announce another new event; the opportunity for the WUWHS sister societies to hold their sessions before the opening ceremony on sunday. Several new WUWHS consensus and position documents will be launched during the conference—an educational established opportunity to spread, on a global level, key messages in wound management
Machine Learning methods for privacy protection: leakage measurement and mechanism design
No full textIn recent years, there has been an increasing involvement of artificial intelligence and machine learning (ML) in countless aspects of our daily lives. In this PhD thesis, we study how notions of information theory and ML can be used to better measure and understand the information leaked by data and / or models, and to design solutions to protect the privacy of the shared information.
We first explore the application of ML to estimate the information leakage of a system. We consider a black-box scenario where the system’s internals are either unknown, or too complicated to analyze, and the only available information are pairs of input-output data samples. Previous works focused on counting the frequencies to estimate the input-output conditional probabilities (frequentist approach), however this method is not accurate when the domain of possible outputs is large. To overcome this difficulty, the estimation of the Bayes error of the ideal classifier was recently investigated using ML models and it has been shown to be more accurate thanks to the ability of those models to learn the input-output correspondence. However, the Bayes vulnerability is only suitable to describe one-try attacks. A more general and flexible measure of leakage is the g-vulnerability, which encompasses several different types of adversaries, with different goals and capabilities. We therefore propose a novel ML based approach, that relies on data preprocessing, to perform black-box estimation of the g-vulnerability, formally studying the learnability for all data distributions and evaluating performances in various experimental settings.
In the second part of this thesis, we address the problem of obfuscating sensitive information while preserving utility, and we propose a ML approach inspired by the generative adversarial networks paradigm. The idea is to set up two nets: the generator, that tries to produce an optimal obfuscation mechanism to protect the data, and the classifier, that tries to de-obfuscate the data. By letting the two nets compete against each other, the mechanism improves its degree of protection, until an equilibrium is reached. We apply our method to the case of location privacy, and we perform experiments on synthetic data and on real data from the Gowalla dataset. The performance of the obtained obfuscation mechanism is evaluated in terms of the Bayes error, which represents the strongest possible adversary.
Finally, we consider that, in classification problems, we try to predict classes observing the values of the features that represent the input samples. Classes and features’ values can be considered respectively as secret input and observable outputs of a system. Therefore, measuring the leakage of such a system is a strategy to tell the most and least informative features apart.
Information theory can be considered a useful concept for this task, as the prediction power stems from the correlation, i.e., the mutual information, between features and labels. We compare the Shannon entropy based mutual information to the Rényi min-entropy based one, both from the theoretical and experimental point of view showing that, in general, the two approaches are incomparable, in the sense that, depending on the considered dataset, sometimes the Shannon entropy based method outperforms the Rényi min-entropy based one and sometimes the opposite occurs
Biologic drugs in wound management
No full textThe role of dermatologists in chronic wound management is increasing. They play a crucial role in the differential diagnosis of an atypical wound where wound and skin biopsies are mandatory. Because of this dermatopathologists have developed specific immunostaining techniques, which offer greater choice in the armamentarium of diagnostics, while improving the understanding of the complex mechanisms of wound healing
Objective measurements of venous ulcer debridement and granulation with a skin color reflectance analyzer
No full textC. Caprino, A. M. Colini, G. Gatti, M. Pallottino, P. Romanelli, La colonna di Marco Aurelio
No full textD. S. G. M. C. Caprino, A. M. Colini, G. Gatti, M. Pallottino, P. Romanelli, La colonna di Marco Aurelio. In: Bulletin de l'Association Guillaume Budé, n°2, juin 1957. pp. 99-100
Shaping molecular excited-state properties by means of localized surface plasmon resonances
No full textControlling light-matter interactions at the nanoscale holds promise for successfully dealing with ever-increasing worldwide issues like energy consumption and shortage. In this regard, metallic nanostructures featuring light-induced localized surface plasmon resonances proved to be an efficacious way of manipulating light at the nanoscale, thus paving the way for controlling the energy flow at molecular scale. Indeed, in recent years many works have illustrated the possibility of modifying molecular properties, e.g. molecular photoluminescence, Raman scattering, energy transfer and so forth, by cleverly harnessing plasmonic effects of metallic nanostructures. Nowadays, these findings have even led to state-of-the-art experimental techniques where single-molecule imaging with sub molecular resolution is possible by using visible light, thus incredibly going beyond light diffraction limit. These complex phenomena are often affected by different system features and span various length and time scales, which makes the rationalization of experimental results an arduous task. In this respect, theory can be instrumental not only to unravel processes which are typically hidden behind experimental observables, but also to investigate new effects that could be later experimentally probed. This thesis aims at shining light on the complex and rich physico-chemical properties arising from coupling molecules with plasmonic nanostructures by combining ab initio molecular modelling with a classical or quantum description of arbitrarily shaped metallic nanostructures, the latter described as homogeneous polarizable objects. Novel methods development and applications to systems of much scientific interest are shown, ranging from plasmon-enhanced single-molecule photoluminescence, plasmon-mediated chirality to collective plasmon-molecules strong-coupling. In many of those cases, a direct comparison between theoretical simulations and state of the art experimental evidence reveals that nanostructures features, such as metal geometrical details and plasmon dynamics drastically impact on the resulting molecular properties, therefore constituting a possible control knob to further manipulate energy at the nanoscale
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