Psychophysiological Assessment of Children with Cerebral Palsy during Robotic-Assisted Gait Training through Infrared Imaging.

Cerebral palsy (CP) is a non-progressive neurologic pathology representing a leading cause of spasticity and concerning gait impairments in children. Robotic-assisted gait training (RAGT) is widely employed to treat this pathology to improve children's gait pattern. Importantly, the effectiveness of the therapy is strictly related to the engagement of the patient in the rehabilitation process, which depends on his/her psychophysiological state. The aim of the study is to evaluate the psychophysiological condition of children with CP during RAGT through infrared thermography (IRT), which was acquired during three sessions in one month. A repeated measure ANOVA was performed (i.e., mean value, standard deviation, and sample entropy) extracted from the temperature time course collected over the nose and corrugator, which are known to be indicative of the psychophysiological state of the individual. Concerning the corrugator, significant differences were found for the sample entropy (F (1.477, 5.907) = 6.888; p = 0.033) and for the mean value (F (1.425, 5.7) = 5.88; p = 0.047). Regarding the nose tip, the sample entropy showed significant differences (F (1.134, 4.536) = 11.5; p = 0.041). The findings from this study suggests that this approach can be used to evaluate in a contactless manner the psychophysiological condition of the children with CP during RAGT, allowing to monitor their engagement to the therapy, increasing the benefits of the treatment.

The clinical and economic impact of surgical site infections after distal pancreatectomy.

BACKGROUND: The present study aimed to evaluate surgical site infections' clinical and economic impact after distal pancreatectomy. METHODS: The study was a prospective, monocentric, observational study, including all adult patients who underwent distal pancreatectomy. According to the American Centers for Disease Control and Prevention definition, the surgical site infection assessment was prospectively performed by trained personnel. The Accordion Severity Grading System was used to evaluate the clinical burden of surgical site infection. The hospitalization's total costs were calculated using the hospital expenditure report, excluding the intraoperative costs. RESULTS: During the study period, 414 distal pancreatectomies were performed. The overall incidence of surgical site infection was 26% (106 patients). Surgical site infections were associated with a higher body mass index (P = .022, odds ratio 1.2), positive preoperative rectal swab for multidrug resistant bacteria (P = .010, odds ratio 4.2), and increased operative time (P = .037, odds ratio 1.1). Using the Accordion Severity Grading System, surgical site infections contributed significantly to the total clinical burden (25.5%) and prolonged hospitalization (P < .001). Furthermore, surgical site infection doubled the costs (12.915 vs 6.888 euros, P < .001). CONCLUSION: Surgical site infection has a high clinical burden, negatively impacting the postoperative course. The costs and length of stay proportionally increased with the surgical site infection severity, doubling the hospitalization expenses.

Data-driven assessment of cardiovascular ageing through multisite photoplethysmography and electrocardiography.

The cardiovascular system is designed to distribute a steady flow through its elastic properties. With ageing, fatigue and fracture of elastin lamellae cause a loss of elasticity defined arterial stiffness. Arterial stiffness causes changes of the pulse wave propagation through the arterial tree, which volumetric counterpart can be assessed non-invasively through photoplethysmography (PPG). PPG may be employed in combination with electrocardiography (ECG). It is here reported an implementation of analysis of multisite PPG and single lead ECG relying on Deep Convolutional Neural Networks (DCNNs). DCNNs generate peculiar filters allowing for data-driven automated selection of the features of interest. The ability of a DCNN to predict subject's age from PPG (left and right brachial, radial and tibial arteries plus fingers) and ECG (Lead I) in a healthy male population (age range: 20-70 years) was investigated. A performance in age prediction of 7 years of root mean square error was obtained, which was superior to other feature-based procedures. The accuracy in age prediction of the machinery in the healthy population may serve for the generation of age-matched normal ranges for the identification of outliers suggesting cardiovascular diseases manifesting as fastened cardiovascular ageing which is recognized as a risk factor for ischemic diseases.