The occiput posterior (OP) position is one of the most frequent malposition during labor. During the first stage of labor, the fetal head may stay in the OP position in 30% of the cases, but of these only 5-7% remains as such at time of delivery. The diagnosis of OP position in the second stage of labor is made difficult by the presence of the caput succedaneum or scalp hair, both of which may give some problem in the identification of fetal head sutures and fontanels and their location in relationship to maternal pelvic landmarks. The capability of diagnosing a fetus in OP position by digital examination has been extremely inaccurate, whereas an ultrasound approach, transabdominal, transperineal and transvaginal, has clearly shown its superior diagnostic accuracy. This is true not only for diagnosis of malpositions, detected in both first and second stage of labor, but also in cases of marked asynclitism.

Treatment of chronic diseases, such as heart failure, requires complex protocols based on early diagnosis; self-monitoring of symptoms, vital signs and physical activity; regular medication intake; and education of patients and caregivers about relevant aspects of the disease. Smartphones and mobile health applications could be very helpful in improving the efficacy of such protocols, but several barriers make it difficult to fully exploit their technological potential and produce clear clinical evidence of their effectiveness. App suppliers do not help users distinguish between useless/dangerous apps and valid solutions. The latter are few and often characterised by rapid obsolescence, lack of interactivity and lack of authoritative information. Systematic reviews can help physicians and researchers find and assess the ‘best candidate solutions’ in a repeatable manner and pave the way for well-grounded and fruitful discussion on their clinical effectiveness. To this purpose, the authors assess 10 apps for heart failure self-care using the Intercontinental Marketing Statistics score and other criteria, discuss the clinical effectiveness of existing solutions and identify barriers to their use in practice and drivers for change.

Environmental pollution and the corresponding control measurements put in place to tackle it play a significant role in determining the actual quality of life in modern cities. Amongst the several pollutant that have to be faced on a daily basis, urban noise represent one of the most widely known for its already ascertained health-related issues. However, no systematic noise management and control activities are performed in the majority of European cities due to a series of limiting factors (e.g., expensive monitoring equipment, few available technician, scarce awareness of the problem in city managers). The recent advances in the Smart City model, which is being progressively adopted in many cities, nowadays offer multiple possibilities to improve the effectiveness in this area. The Mobile Crowd Sensing paradigm allows collecting data streams from smartphone built-in sensors on large geographical scales at no cost and without involving expert data captors, provided that an adequate IT infrastructure has been implemented to manage properly the gathered measurements. In this paper, we present an improved version of a MCS-based platform, named City Soundscape, which allows exploiting any Android-based device as a portable acoustic monitoring station and that offers city managers an effective and straightforward tool for planning Noise Reduction Interventions (NRIs) within their cities. The platform also now offers a new logical microservices architecture.