Surgical treatment of retrosternal extraosseous Ewing Sarcoma in a 6-years old female: a clamshell approach with hemysternectomy and application of a non-crosslinked extracellular matrix

Background Ewing Sarcoma (ES) and Neuroblastoma (NB) belong to a family of tumours of primitive neuroectodermal origin (PNET) that occurs in both bone and soft tissue. Notwithstanding ES and NB are two distinct malignant tumours, sometimes there could be a link between them. Case report We describe a case of an extraosseous ES localized in the retrosternal region and the upper lobe of the right lung, which had been previously treated for NB in a 6 years old female. We treated this case with a clamshell approach which allows, in a one-step surgery, a complete excision of the mass reconstructing the hemysternectomy with a non-crosslinked matrix. Conclusion the clamshell approach is therefore useful to achieve the retrosternal space and the lung with a single surgical access. According to our experience, we consider appropriate to use a non-crosslinked matrix for sternal reconstruction

Impact of stain normalization and patch selection on the performance of convolutional neural networks in histological breast and prostate cancer classification

Background Recently, deep learning has rapidly become the methodology of choice in digital pathology image analysis. However, due to the current challenges of digital pathology (color stain variability, large images, etc.), specific pre-processing steps are required to train a reliable deep learning model. Method In this work, there are two main goals: i) present a fully automated pre-processing algorithm for a smart patch selection within histopathological images, and ii) evaluate the impact of the proposed strategy within a deep learning framework for the detection of prostate and breast cancer. The proposed algorithm is specifically designed to extract patches only on informative regions (i.e., high density of nuclei), most likely representative of where cancer can be detected. Results Our strategy was developed and tested on 1000 hematoxylin and eosin (H&E) stained images of prostate and breast tissue. By combining a stain normalization step and a segmentation-driven patch extraction, the proposed approach is capable of increasing the performance of a computer-aided diagnosis (CAD) system for the detection of prostate cancer (18.61% accuracy improvement) and breast cancer (17.72% accuracy improvement). Conclusion We strongly believe that the integration of the proposed pre-processing steps within deep learning frameworks will allow the achievement of robust and reliable CAD systems. Being based on nuclei detection, this strategy can be easily extended to other glandular tissues (e.g., colon, thyroid, pancreas, etc.) or staining methods (e.g., PAS)

Development and In Vivo Evaluation of Multidrug Ultradeformable Vesicles for the Treatment of Skin Inflammation

The aim of this work was to evaluate the effect of two chemically different edge activators, i.e., Tween® 80 and sodium deoxycholate, on (i) the physical, mechanical, and biological properties of ultradeformable vesicles, and (ii) the administration of naproxen sodium-loaded multidrug ultradeformable vesicles for the transdermal route in order to obtain therapeutically meaningful drug concentrations in the target tissues and to potentiate its anti-inflammatory effect by association with the antioxidant drug idebenone. The results obtained in this investigation highlighted a synergistic action between naproxen and idebenone in the treatment of inflammatory disease with a more pronounced anti-inflammatory effect in multidrug ultradeformable vesicles compared to the commercial formulation of Naprosyn® gel. Systems made up of Tween® 80 appeared to be the most suitable in terms of percutaneous permeation and anti-inflammatory activity due to the greater deformability of these vesicles compared to multidrug ultradeformable vesicles with sodium deoxycholate. Our findings are very encouraging and suggest the use of these carriers in the topical treatment of inflammatory diseases

ODINet - Online Data Integration Network

Along with the expansion of Open Data and according to the latest EU directives for open access, the attention of public administration, research bodies and business is on web publishing of data in open format. However, a specialized search engine on the datasets, with similar role to that of Google for web pages, is not yet widespread. This article presents the Online Data Integration Network (ODINet) project, which aims to define a new technological framework for access to and online dissemination of structured and heterogeneous data through innovative methods of cataloging, searching and display of data on the web. In this article, we focus on the semantic component of our platform, emphasizing how we built and used ontologies. We further describe the Social Network Analysis (SNA) techniques we exploited to analyze it and to retrieve the required information. The testing phase of the project, that is still in progress, has already demonstrated the validity of the ODINet approach

Italian Report: EMCDDA Project (CC.09.EPI.002)

Although there are definitions and diagnostic criteria to identify cannabis abuse and dependence, there is no a shared concept of cannabis use related problems. In recent times a number of indicators have been discussed and implemented (use severity and consumption patterns, treatment demands etc) and there are different screening instruments used to measure negative consequences of cannabis use. Short screening scales to assess dependence and other problems related to the use of cannabis seem to be usefull instruments, easy to be administered, to estimate prevalence of cannabis related negative consequences and to identify at risk-persons. The need to standardize also in Italy a set of instruments which allow to monitor problematic cannabis use patterns or addiction development, has been highlighted. The three scales Cannabis Abuse Screening Test (CAST), Severity of Dependence Scale (SDS) and Munich Composite International Diagnostic Interview (M-CIDI) are included in the questionnaire used in ESPAD-Italia? 2009 survey. The present work considers the validation process of these instruments in high school population.not availabl

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments