Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation

Semantic segmentation is a crucial step to extract quantitative information from medical (and, specifically, radiological) images to aid the diagnostic process, clinical follow-up. and to generate biomarkers for clinical research. In recent years, machine learning algorithms have become the primary tool for this task. However, its real-world performance is heavily reliant on the comprehensiveness of training data. Dafne is the first decentralized, collaborative solution that implements continuously evolving deep learning models exploiting the collective knowledge of the users of the system. In the Dafne workflow, the result of each automated segmentation is refined by the user through an integrated interface, so that the new information is used to continuously expand the training pool via federated incremental learning. The models deployed through Dafne are able to improve their performance over time and to generalize to data types not seen in the training sets, thus becoming a viable and practical solution for real-life medical segmentation tasks.Comment: 10 pages (main body), 5 figures. Work partially presented at the 2021 RSNA conference and at the 2023 ISMRM conference In this new version: added author and change in the acknowledgmen

Risk factors for pulmonary air leak and clinical prognosis in patients with COVID-19 related acute respiratory failure: a retrospective matched control study.

Background- The role of excessive inspiratory effort in promoting alveolar and pleural rupture resulting in air leak (AL) in patients with SARS-CoV-2 induced acute respiratory failure (ARF) while on spontaneous breathing is undetermined. Methods- Among all patients with COVID-19 related ARF admitted to a respiratory intensive care unit (RICU) and receiving non-invasive respiratory support, those developing an AL were and matched 1:1 (by means of PaO2/FiO2 ratio, age, body mass index-BMI and subsequent organ failure assessment [SOFA]) with a comparable population who did not (NAL group). Esophageal pressure (ΔPes) and dynamic transpulmonary pressure (ΔPL) swings were compared between groups. Risk factors affecting AL onset were evaluated. The composite outcome of ventilator-free-days (VFD) at day 28 (including ETI, mortality, tracheostomy) was compared between groups. Results- AL and NAL groups (n=28) showed similar ΔPes, whereas AL had higher ΔPL (20 [16‐21] and 17 [11‐20], p=0.01 respectively). Higher ΔPL (OR=1.5 95%CI[1‐1.8], p=0.01), positive end‐expiratory pressure (OR=2.4 95%CI[1.2‐5.9], p=0.04) and pressure support (OR=1.8 95%CI[1.1-3.5], p=0.03), D-dimer on admission (OR=2.1 95%CI[1.3-9.8], p=0.03), and features suggestive of consolidation on computed tomography scan (OR=3.8 95%CI[1.1-15], p= 0.04) were all significantly associated with AL. A lower VFD score resulted in a higher risk (HR=3.7 95%CI [1.2-11.3], p=0.01) in the AL group compared with NAL. RICU stay and 90-day mortality were also higher in the AL group compared with NAL. Conclusions- In spontaneously breathing patients with COVID‐19 related ARF, higher levels of ΔPL, blood D‐dimer, NIV delivery pressures and a consolidative lung pattern were associated with AL onset

ATLAS Virtual Visits – Bringing the world to our detector

ATLAS collaboration is reaching classrooms and other public places worldwide thanks to Virtual Visits, service since 2010. Schools and universities, pupils, undergraduate and graduate students, science and non-science classes have the opportunity to visit CERN area and interact with scientists, without travelling to Geneva. This service is popular and uses combination of video conferencing, webcasts, and video recording to communicate with remote audiences. We present a summary of ATLAS Virtual Visit service, including the new system installed in the ATLAS Visitor Center, which will be important during Run 3. In addition, we will show the programme's reach over the last year

ATLAS Virtual Visits – Bringing the world to our detector

Thanks to Virtual Visits service, the ATLAS collaboration is reaching classrooms and other public targets worldwide since 2010. Schools and universities, pupils, undergraduate and graduate students, science and non-science classes have the opportunity to visit CERN area and interact with scientists, without travelling to Geneva. This service is fairly popular and uses combination of video conferencing, webcasts, and video recording to communicate with remote audiences. In this proceeding we will present a summary of ATLAS Virtual Visit service, including the new system installed in the ATLAS Visitor Center, which will be important during Run 3. In addition, we will show the programme reach over the last year

New insight into the mechanisms of ectopic fat deposition improvement after bariatric surgery

Non-alcoholic fatty-liver disease (NAFLD) is frequent in obese patients and represents a major risk factor for the development of diabetes and its complications. Bariatric surgery reverses the hepatic features of NAFLD. However, its mechanism of action remains elusive. We performed a comprehensive analysis of the mechanism leading to the improvement of NAFLD and insulin resistance in both obese rodents and humans following sleeve-gastrectomy (SG). SG improved insulin sensitivity and reduced hepatic and monocyte fat accumulation. Importantly, fat accumulation in monocytes was well comparable to that in hepatocytes, suggesting that Plin2 levels in monocytes might be a non-invasive marker for the diagnosis of NAFLD. Both in vitro and in vivo studies demonstrated an effective metabolic regeneration of liver function and insulin sensitivity. Specifically, SG improved NAFLD significantly by enhancing AMP-activated protein kinase (AMPK) phosphorylation and chaperone-mediated autophagy (CMA) that translate into the removal of Plin2 coating lipid droplets. This led to an increase in lipolysis and specific amelioration of hepatic insulin resistance. Elucidating the mechanism of impaired liver metabolism in obese subjects will help to design new strategies for the prevention and treatment of NAFLD

Innovative Renewable Technology Integration for Nearly Zero-Energy Buildings within the Re-COGNITION Project

With the 2010/31/EU directive, all new buildings shall be nearly zero-energy buildings (nZEB) from 2020 onward, with the aim of strongly reducing the energy consumption related to the building sector. To achieve this goal, it is not sufficient to focus on the design of the building envelope; smart and efficient energy management is necessary. Moreover, to ensure the adoption of RES systems in the built environment, innovative technologies need to be further developed in order to increase their cost-effectiveness, energy efficiency and integration capability. This paper proposes a synthesis, design and operation optimization of an integrated multi-energy system composed of traditional and innovative renewable technologies, developed within the European project Re-COGNITION. A biogas-based micro cogeneration unit, lightweight glass-free photovoltaic modules, a passive variable geometry small wind turbine optimized for an urban environment and latent heat thermal storage based on phase change materials are some of the technologies developed within the Re-COGNITION project. The optimization problem is solved to contemporarily evaluate (a) the optimal design and (b) the optimal operations of the set of technologies considering both investment and operating costs, using mixed integer non-linear programming. The optimization is applied to the four pilots that are developed during the project, in various European cities (Turin (Italy), Corby (United Kingdom), Thessaloniki (Greece), Cluj-Napoca (Romania). Simulation results show that the development and optimal exploitation of new technologies through optimization strategies provide significant benefits in terms of cost (between 11% and 42%) and emissions (between 10% and 25%), managing building import/export energy and charge/discharge storage cycles

Publisher Correction: New insight into the mechanisms of ectopic fat deposition improvement after bariatric surgery

An amendment to this paper has been published and can be accessed via a link at the top of the paper

NutricheQ Questionnaire assesses the risk of dietary imbalances in toddlers from 1 through 3 years of age

Background: Although a nutrient-poor diet may affect children's growth, especially early in life, few tools to assess dietary imbalances in 1- to 3-year-old children have been developed. Objectives: To investigate the accuracy and test–retest reliability of the NutricheQ Questionnaire in the identification of toddlers with the risk of inadequate intake of micro- and macronutrients in a sample of Italian toddlers. Design: A 3-day weighed food record was performed, and results were compared with outcomes of the NutricheQ Questionnaire in 201 toddlers (training set: 1–3 years old). The accuracy of NutricheQ in the identification of categories of nutritional risk was evaluated using the receiver operating characteristic (ROC) curves. Test–retest of the tool was estimated using the intraclass correlation coefficient (ICC) and the Cronbach's alpha statistic, in a validation set of 50 toddlers. Results: The NutricheQ Questionnaire is a valid tool for the identification of toddlers at risk for dietary imbalances. Significant differences in nutrient intake (p<0.005) were found among the three groups of risk identified by the questionnaire: toddlers included in the high-risk group had a lower intake of key nutrients such as iron, vitamin D and other vitamins, and fibre compared to those included in the low-risk group. NutricheQ is also reliable between administrations, as demonstrated by its test–retest reliability. ICC and Cronbach's alpha were 0.73 and 0.83, respectively, for Section 1 of NutricheQ, and 0.55 and 0.70 for Section 2. Conclusions: The NutricheQ Questionnaire is a reliable and consistent tool for the assessment of possible dietary risk factors in Italian toddlers. It consistently identifies toddlers with a high probability of having poor iron and vitamin D intake, and other dietary imbalances

New perspectives in glioblastoma: Nanoparticles-based approaches

Glioblastoma multiforme represents one of the most aggressive tumor of central nervous system. Current therapy includes surgery, radiation and chemotherapy. These treatments are rarely curative and glioma are associated with a poor prognosis. Nanomedicine represents the most innovative branch of medicine since many studies demonstrated great advantage in the diagnosis and therapy of several diseases. In this review we will summarize the results obtained by the use of nanoparticles and extracellular vesicles in glioblastoma. A great interest is raising from these studies that underlined the efficacy and specificity of this treatment for glioma, reducing side-effects associated with conventional therapies

Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease

PurposeQuantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms. MethodsTwenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles. ResultsThe combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about +/- 5 pp for FF and +/- 1.8 ms for wT2. ConclusionThis pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence