University of Modena and Reggio Emilia

About: University of Modena and Reggio Emilia is a education organization based out in Modena, Italy. It is known for research contribution in the topics: Population & Transplantation. The organization has 8179 authors who have published 22418 publications receiving 671337 citations. The organization is also known as: Università degli Studi di Modena e Reggio Emilia & Universita degli Studi di Modena e Reggio Emilia.

Distributed control of multi-robot systems with global connectivity maintenance

Abstract: This study introduces a control algorithm that, exploiting a completely decentralized estimation strategy for the algebraic connectivity of the graph, ensures the connectivity maintenance property for multi robot systems, in the presence of a generic (bounded) additional control term. This result is obtained by driving the robots along the negative gradient of an appropriately defined function of the algebraic connectivity. The proposed strategy is then enhanced with the introduction of the concept of critical robots, that is robots for which the loss of a single communication link might cause the disconnection of the communication graph. Limiting the control action to critical robots will be shown to reduce the control effort that is introduced by the proposed connectivity maintenance control law and to mitigate its effect on the additional (desired) control term.

Prevalence and Clinical Picture of Celiac Disease in Italian Down Syndrome Patients: A Multicenter Study

Abstract: Background: A multicenter research study of Down syndrome patients was carried out to estimate the prevalence of celiac disease in patients with Down syndrome and to show clinical characteristics and laboratory data of Down syndrome patients. Methods: The authors studied 1,202 Down syndrome patients. Fifty-five celiac disease patients (group I ) were compared with 55 immunoglobulin A antigliadin-positive antiendomysium antibodies-negative patients (group 2) and with 57 immunoglobulin A antigliadin-negative antiendomysium antibodies-negative patients (group 3). Results: Celiac disease was diagnosed in 55 of 1,202 Down syndrome patients (4.6%). In group 1, weight and height percentiles were shifted to the left, whereas these parameters were normally distributed in groups 2 and 3. In celiac patients, diarrhea, vomiting, failure to thrive, anorexia, constipation, and abdominal distension were higher than in the other two groups. Low levels of hemoglobinemia, serum iron, and calcium were observed more frequently in group 1. The diagnosis of celiac disease was made after a mean period of 3.8 years from the initiation of symptoms. Sixty-nine percent of patients showed a classic presentation, 11% had atypical symptoms, and 20% had silent celiac disease. Autoimmune disorders were more frequent (30.9%) in group I than in the other two groups examined (15%; P<0.05). Conclusions: This study reconfirms a high prevalence of celiac disease in Down syndrome. However, the diagnostic delay, the detection of atypical symptoms or silent form in one third of the cases, and the increased incidence of autoimmune disorders suggest the need for the screening of celiac disease in all Down syndrome patients.

Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.

Abstract: Background and aims There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. Methods and results Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6–14.7 for age ≥85 vs 18–44 y); HR = 4.7; 2.9–7.7 for estimated glomerular filtration rate levels Conclusions Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.