Mean weight loss obtained with lifestyle interventions is confirmed to be rather small and a more accurate selection of patients to be enrolled in lifestyle intervention programs is needed. The present study provides some intriguing information on predictors of weight loss, which could be useful for the identification of patients with a higher chance of succeeding with lifestyle programs for the treatment of obesity.

Insulin resistance is a clinical condition shared by many diseases besides type 2 diabetes (T2DM) such as obesity, polycystic ovary syndrome (PCOS) and non-alcoholic fatty liver disease (NAFLD). Experimental evidence, produced over the years, suggests that metformin has many benefits in the treatment of these diseases. Metformin is a first-line drug in the treatment of overweight and obese type 2 diabetic patients, offering a selective pathophysiological approach by its effect on insulin resistance. Moreover, a number of studies have established the favorable effect of metformin on body weight, not only when evaluating BMI, but also if body mass composition is considered, through the reduction of fat mass. In addition, it reduces insulin resistance, hyperinsulinemia, lipid parameters, arterial hypertension and endothelial dysfunction. In particular, a new formulation of metformin extended-release (ER) is now available with different formulation in different countries. Metformin ER delivers the active drug through hydrated polymers which expand safe uptake of fluid, prolonging gastric transit and delaying drug absorption in the upper gastrointestinal tract. In addition, Metformin ER causes a small, but statistically significant decrease in BMI, when added to a lifestyle intervention program in obese adolescents. Because of the suggested benefits for the treatment of insulin resistance in many clinical conditions, besides type 2 diabetes, the prospective exists that more indications for metformin treatment are becoming a reality.

The Strategyst epidemiology forecast model has allowed to assess the current and future prevalence of obesity and its relative co-morbidities like HG in Italy. Data emerged from this evaluation suggest that obesity and HG prevalence will increase in Italian population and confirm the complex link between adipose tissue and male T levels.

Vehicle loop detectors or other equipment installed on cross-sections are commonly used for monitoring traffic flow conditions on road network. For operational analysis it is crucial to distinguish between low level of service related to oversaturated conditions and generated by extraordinary events as incidents. In case of incident it is fundamental to have a prompt response in order to activate any requested countermeasure, such as rescue activation and traffic detour. This paper introduces a control system which recognizes incidents from vehicle loop detectors data (system control), and identifies the optimal position of loop detectors (system design).The system was developed using fuzzy logic concepts and calibrated using data from micro simulation experiments. Micro simulation approach is justified from the impossibility to get the requested data from on-field observations. The analysis has been focused on a two-way four-lane freeway basic segment; traffic flow variables (Density, Space Mean Speed and Flow Rate) were estimated with reference to the set of consecutive time intervals (one-minute long) belonging to the whole observation time period (3 hours). Simulated data were obtained running the model several times (10 runs) for each traffic volume class adopted in the analysis (1,000, 2,000, 3,000, 3,500 vehicles/hour), with different random number seeds. Calibration dataset was used to determine the knowledge base of each FIS using the open-source software FisPro, and the remaining data (validation dataset) to evaluate the performance of the system. The main finding of the study is that the detection system, despite its simplicity, shows excellent False Alarm Rate and satisfactory Mean Time To Detection