The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89–92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.

Background: After a stroke, up to three-quarters of acute and subacute stroke survivors exhibit cognitive impairment, with a significant impact on functional recovery, quality of life, and social engagement. Robotic therapy has shown its effectiveness on motor recovery, but its effectiveness on cognitive recovery has not fully investigated. Objective: This study aims to assess the impact of a technological rehabilitation intervention on cognitive functions in patients with stroke, using a set of three robots and one sensor-based device for upper limb rehabilitation. Methods: This is a pilot study in which 51 patients were enrolled. An upper limb rehabilitation program was performed using three robots and one sensor-based device. The intervention comprised motor/cognitive exercises, especially selected among the available ones to train also cognitive functions. Patients underwent 30 rehabilitation sessions, each session lasting 45 minutes, 5 days a week. Patients were assessed before and after the treatment with several cognitive tests (Oxford Cognitive Scale, Symbol Digit Modalities Test, Digit Span, Rey-Osterrieth Complex Figure, Tower of London, and Stroop test). In addition, motor (Fugl-Meyer Assessment and Motricity Index) and disability (modified Barthel Index) scales were used. Results: According to the Oxford Cognitive Scale domains, a significant percentage of patients exhibited cognitive deficits. Excluding perception (with only one patient impaired), the domain with the lowest percentage of patients showing a pathological score was praxis (about 25%), while the highest percentage of impaired patients was found in calculation (about 70%). After the treatment, patients improved in all the investigated cognitive domains, as measured by the selected cognitive assessment scales. Moreover, motor and disability scales confirmed the efficacy of robotics on upper limb rehabilitation in patients with stroke. Conclusions: This explorative study suggests that robotic technology can be used to combine motor and cognitive exercises in a unique treatment session.

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Cognitive decline is often present in stroke survivors, with a significant impact on motor recovery. However, how specific cognitive domains could impact motor recovery after robotic rehabilitation in patients with stroke is still not well understood. In this study, we analyzed the relationship between cognitive impairment and the outcome of a robot-mediated upper limb rehabilitation intervention in a sample of 51 subacute stroke patients. Participants were enrolled and treated with a set of robotic and sensor-based devices. Before the intervention, patients underwent a cognitive assessment by means of the Oxford Cognitive Screen. To assess the effect of the 30-session rehabilitation intervention, patients were assessed twice with the following outcome measures: the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), to evaluate motor function; the Upper limb Motricity Index (MI), to evaluate upper limb muscle strength; the Modified Barthel Index (mBI), to evaluate activities of daily living and mobility. We found that deficits in spatial attention and executive functions impacted the mBI improvement, while language, number processing, and spatial attention deficits reduced the gains in the FMA-UE. These results suggest the importance to evaluate the cognitive functions using an adequate tool in patients with stroke undergoing a robotic rehabilitation intervention.

Lung transplantation (LT) increases the life expectancy of patients affected by end stage pulmonary disease; specifically, its ultimate aims are to improve survival and health related quality of life (HRQoL). The aim of the present longitudinal study was to determine the HRQoL trajectory and changes in functional capacity from time of entry in the waiting list for LT to 2 year after LT. The study included sixty-nine outpatients enrolled in a single medical center when they entered the waiting list for LT and who subsequently received it. They were then followed up over 2 years after LT. HRQoL was assessed by the physical and mental component summary (PCS and MCS) scores of the 36-item Short Form Health Survey (SF-36) and Saint George's Respiratory Questionnaire (SGRQ). Psychological distress was evaluated with the General Health Questionnaire (GHQ), and functional capacity was investigated using the six-minute walk test (6MWT) and forced expiratory volume (FEV1). Patients showed low SF-36 PCS (30.5±7.8) and SGRQ total (61.8±17.5) scores at entry in the waiting list, but exhibited significant changes over time after LT (p<0.001). Furthermore, patients who showed an increase of at least 50% in SF36 PCS and SGRQ scores at 6 months survived longer. Both FEV1 and 6MWT distance as well as GHQ scores significantly changed over time, with improvements occurring in the first 6 months after LT but no major changes thereafter. Out of the 69 patients enrolled, 32 died over a median follow-up of 51 months. Although mortality tended to be slightly higher for patients with lower HRQoL at the baseline assessment, this difference was not statistically significant. HRQoL evaluations appear critical in the follow-up of LT candidates, in particularly SGRQ, because of its specificity in targeting respiratory symptoms and functional wellbeing.