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The physiological loss of muscle mass and strength with aging is referred to as “sarcopenia”, whose combined effect with osteoporosis is a serious threat to the elderly, accounting for decreased mobility and increased risk of falls with consequent fractures. In previous studies, we observed a high degree of inter-individual variability in paraspinal muscle fatty infiltration, one of the most relevant indices of muscle wasting. This aspect led us to develop a computerized method to quantitatively characterize muscle fatty infiltration in aging and diseases. Magnetic resonance images of paraspinal muscles from 58 women of different ages (age range of 23–85 years) and physio-pathological status (healthy young, pre-menopause, menopause, and osteoporosis) were used to set up a method based on fractal-derived texture analysis of lean muscle area (contractile muscle) to estimate muscle fatty infiltration. In particular, lacunarity was computed by parameter β from the GBA (gliding box algorithm) curvilinear plot fitted by our hyperbola model function. Succolarity was estimated by parameter µ, for the four main directions through an algorithm implemented with this purpose. The results show that lacunarity, by quantifying muscle fatty infiltration, can discriminate between osteoporosis and healthy aging, while succolarity can separate the other three groups showing similar lacunarity. Therefore, fractal-derived features of contractile muscle, by measuring fatty infiltration, can represent good indices of sarcopenia in aging and disease.

Clinical diagnosis of influenza has low sensitivity in infants and children. Signs and symptoms are non-specific and similar to those of other respiratory viruses. Rapid influenza diagnostic tests (RIDTs) with adequate sensitivity and specificity used at the point of care can be useful for an etiologic diagnosis of influenza in primary care. This should have an impact on better management of these patients. We conducted a study during three consecutive influenza seasons (2016–2017, 2017–2018, and 2018–2019) in pediatric primary care settings collecting data from influenza point-of-care tests (POCTs)-confirmed ≤ 6-year-old patients. During the first two influenza seasons, antibiotic prescriptions and additional visits from influenza POCT-confirmed patients (Group_1) were compared to patients with influenza-like illness (ILI) (Group_2), or fever (2016 ICD-10 code R50) with no other signs of influenza (Group_3). Group_1 had 0.19 (2016–2017) and 0.23 (2017–2018) additional visits compared to 0.48 (2016–2017) and 0.49 (2017–2018) Group_2 p < 0.001 and 1.01 (2016–2017) and 0.80 (2017–2018) Group_3 p < 0.001. Antibiotic prescription was lower in Group_1 (10.2%) vs. Group_3 (17.2%) p < 0.002, difference statistically significant only for the 2017–2018 season. During the third season (2018–2019), RIDTs results were transmitted in real time to the reference laboratory ia the cloud, which strengthens the monitoring of circulating influenza viruses in the community. In our experience, the use of POCTs has a great potential in primary care specially in infants and young children in which the diagnosis maybe missed due to non-specific signs and symptoms.

This paper presents the results obtained by the elaboration of an artificial neuronal network for the creation of a rockfall susceptibility map. The analysis was carried out by analysing the predisposing and triggering factors of the rockfall phenomenon. The parameters considered for this study and representing the input data of the artificial neural network are factors such as: gradient, soil use, lithology, rockfall source areas and kinetic energy values obtained by considering the probable pathways of the blocks through simulations with dedicated softwares, DEMs and niches of the rockfalls that have already occurred in the past. The processing of this data (required in a versatile dedicated software for the realization of the artificial neural network in ASCII format) is done using GIS softwares, useful tools for the creation of hazard maps. An important step is the realization of the rockfall inventory map: it allows to identify the training set (consisting of 50% of the pixels relative to the rockfall niches) for the network training and the testing set (considering the remaining 50% of the pixels relative to the rockfall niches) to assess the network accuracy by overlaying the rockfall niches belonging to the testing set with the obtained susceptibility map.