Highlights a light CNN for efficient detection of COVID-19 from chest CT scans is proposed the accuracy is comparable with that of more complex CNN designs the efficiency is 10 times better than more complex CNNs using pre-processing no GPU acceleration is required and can be executed on middle class computers

Osteoporosis affects more than 200 million people worldwide: its prevalence increases with age and is actually growing due to the constant population aging. Women are at greater risk than men, but in recent years it has become increasingly evident that osteoporosis represents a significantly important problem also for men. However, osteoporosis in men is still poorly studied, underdiagnosed and inadequately treated. Materials and Methods: Materials and Methods: We conducted an observational study to identify any gender disparities in osteoporosis screening. For this purpose we observed people consecutively admitted at our Outpatient Service for the Diagnosis of Osteoporosis during the last 3 years. Patients underwent clinical and laboratory assessment and bone mineral density (BMD) measurements by dual-energy X-ray absorptiometry. Bone turnover serum markers have been evaluated and stratified according to gender. Results: Results: Out of 3,752 patients, 2,376 subjects who met the inclusion criteria were identified. As expected, the great majority (94.5%) of the screened subjects were women and only 5.4% were men. Women exhibited lower BMD compared to men (Tscore values:-2.33±1.14 vs.-1.31±1.55; p<0.001), whereas the prevalence of fractures in osteoporotic men was significantly higher (50% vs. 31%; p<0.001). Women had lower vitamin D and higher bone remodeling markers compared to men. Secondary osteoporosis was more frequent in men (66.67%) than in women (20.83%) and the calculated risk for hip fractures was higher in osteoporotic men compared to women (11.47±10.62 vs. 6.87±7.73; p<0.001). Conclusions: Conclusions: Here we highlighted that men are under-screened for osteoporosis and exhibit secondary osteoporosis more frequently than women.

Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments. Mechanical devices are often expensive, cumbersome, patient specific and hand specific, while tracking-based devices are not affected by these limitations but, especially if based on a single tracking sensor, could suffer from occlusions. In this paper, the implementation of a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, is described. The VG is calibrated and static positioning measurements are compared with those collected with an accurate spatial positioning system. The positioning error is lower than 6 mm in a cylindrical region of interest of radius 10 cm and height 21 cm. Real-time hand tracking measurements are also performed, analysed and reported. Hand tracking measurements show that VG operated in real-time (60 fps), reduced occlusions, and managed two LEAP sensors correctly, without any temporal and spatial discontinuity when skipping from one sensor to the other. A video demonstrating the good performance of VG is also collected and presented in the Supplementary Materials. Results are promising but further work must be done to allow the calculation of the forces exerted by each finger when constrained by mechanical tools (e.g., peg-boards) and for reducing occlusions when grasping these tools. Although the VG is proposed for rehabilitation purposes, it could also be used for tele-operation of tools and robots, and for other VR applications.

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