Introduction Cardiomyopathy is a common cause of morbidity and death in patients with Duchenne muscular dystrophy (DMD). Methods A cross-sectional analysis of clinical data from a multi-institutional, international CINRG DMD Natural History Study of 340 DMD patients aged 2 to 28 years. Cardiomyopathy was defined as shortening fraction (SF) <28% or ejection fraction (EF) <55%. Results 231 participants reported a prior clinical echocardiogram study, and 174 had data for SF or EF. The prevalence of cardiomyopathy was 27% (47/174), and it was significantly associated with age and clinical stage. The association of cardiomyopathy with age and clinical stage was not changed by glucocorticoid use as a covariate (P>0.68). In patients with cardiomyopathy, 57 % (27/47) reported not taking any cardiac medications. Cardiac medications were used in 12% (15/127) of patients without cardiomyopathy. Discussion Echocardiograms were underutilized, and cardiomyopathy was undertreated in this DMD natural history cohort.

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Lifestyle changes to healthy diet (HD) and habitual physical activity (HPA) are recommended in type 2 diabetes mellitus (T2DM). Yet, for most people with diabetes, it may be difficult to start changing. We investigated the stage of change toward healthier lifestyles according to Prochaska's model, and the associated psychological factors in T2DM patients, as a prerequisite to improve strategies to implement behavior changes in the population. A total of 1,353 consecutive outpatients with T2DM attending 14 tertiary centers for diabetes treatment completed the validated EMME-3 questionnaire, consisting of two parallel sets of instruments to define the stage of change for HD and HPA, respectively. Logistic regression was used to determine the factors associated with stages that may hinder behavioral changes. A stage of change favoring progress to healthier behaviors was more common in the area of HD than in HPA, with higher scores in action and maintenance. Differences were observed in relation to gender, age and duration of disease. After adjustment for confounders, resistance to change toward HD was associated with higher body mass index (BMI) (odds ratio (OR) 1.05; 95 % confidence interval (CI) 1.02-1.08). Resistance to improve HPA also increased with BMI (OR 1.06; 95 % CI 1.03-1.10) and decreased with education level (OR 0.74; 95 % CI 0.64-0.92). Changing lifestyle, particularly in the area of HPA, is not perceived as an essential part of treatment by many subjects with T2DM. This evidence must be considered when planning behavioral programs, and specific interventions are needed to promote adherence to HPA.

Background Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments. Methods This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol. Results Among 199 patients subject to study (median [interquartile range] age 65 [46–78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity. Conclusion Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Background and aims: Dysfunctional eating might impact on the management and metabolic control of type 2 diabetes (T2DM), modifying adherence to healthy diet and food choices. Methods and results: In a multicenter study, we assessed the prevalence of dysfunctional eating in 895 adult outpatients with T2DM (51% males, median age 67, median BMI 30.3 kg/m 2). Sociodemographic and clinical characteristics were recorded; dysfunctional eating was tested by validated questionnaires (Eating Attitude Test-EAT-26, Binge Eating Scale-BES; Night Eating Questionnaire-NEQ); food intake and adherence to Mediterranean diet were also measured (in-house developed questionnaire and Mediterranean Diet ScoreeMDS). Obesity was present in 52% of cases (10% obesity class III), with higher rates in women; 22% had HbA1c ! 8%. The EAT-26 was positive in 19.6% of women vs. 10.2% of men; BES scores outside the normal range were recorded in 9.4% of women and 4.4% of men, with 3.0% and 1.5% suggestive of binge eating disorder, respectively. Night eating (NEQ) was only present in 3.2% of women and 0.4% of men. Critical EAT and BES values were associated with higher BMI, and all NEQ þ ve cases, but one, were clustered among BES þ ve individuals. Calorie intake increased with BES, NEQ, and BMI, and decreased with age and with higher adherence to Mediterranean diet. In multivariable logistic regression analysis, female sex, and younger age were associated with increase risk of dysfunctional eating. Conclusion: Dysfunctional eating is present across the whole spectrum of T2DM and significantly impacts on adherence to dietary restriction and food choices.