From fall-risk assessment to fall detection: inertial sensors in the clinical routine and daily life

Falls are caused by complex interaction between multiple risk factors which may be modified by age, disease and environment. A variety of methods and tools for fall risk assessment have been proposed, but none of which is universally accepted. Existing tools are generally not capable of providing a quantitative predictive assessment of fall risk. The need for objective, cost-effective and clinically applicable methods would enable quantitative assessment of fall risk on a subject-specific basis. Tracking objectively falls risk could provide timely feedback about the effectiveness of administered interventions enabling intervention strategies to be modified or changed if found to be ineffective. Moreover, some of the fundamental factors leading to falls and what actually happens during a fall remain unclear. Objectively documented and measured falls are needed to improve knowledge of fall in order to develop more effective prevention strategies and prolong independent living. In the last decade, several research groups have developed sensor-based automatic or semi-automatic fall risk assessment tools using wearable inertial sensors. This approach may also serve to detect falls. At the moment, i) several fall-risk assessment studies based on inertial sensors, even if promising, lack of a biomechanical model-based approach which could provide accurate and more detailed measurements of interests (e.g., joint moments, forces) and ii) the number of published real-world fall data of older people in a real-world environment is minimal since most authors have used simulations with healthy volunteers as a surrogate for real-world falls. With these limitations in mind, this thesis aims i) to suggest a novel method for the kinematics and dynamics evaluation of functional motor tasks, often used in clinics for the fall-risk evaluation, through a body sensor network and a biomechanical approach and ii) to define the guidelines for a fall detection algorithm based on a real-world fall database availability.

“Clinical and biological role of adjuvant dendritic cells vaccination in newly glioblastoma patients”

Background. Glioblastoma (GBM) is the most common primary tumor of central nervous system and it has a poor prognosis. Standard first line treatment, which includes surgery followed by adjuvant radio-chemotherapy,produces only modest benefits to survival. The interest for immunotherapy in this field derives from the development of new drugs and effective therapies as immune-check points inhibitors, adoptive T-cell approaches or dendritic cell (DC) based vaccines or a combinations of these. GBM is described as a typical “immune-deserted” cancer exhibiting a number of systemic and environmental immunosuppressive factors. Considering the role of microenvironment, and above all the lower tumor load and depletion of immunosuppressive cells in GBM, our hypothesis is that DC vaccine may induce an immune response. Main aims and study design. The main aim of this project is to study the role of immune system in GBM, including identification of potential prognostic and predictive markers of outcome and response to dendritic cell vaccine. Firstly, we performed a retrospective analysis on blood samples. Then, we analyzed the immuno-component in tissues samples of enrolled patients; and compared that with blood results. Then, the last part of the project is based on a prospective clinical trial on patients enrolled in DC-based vaccination produced at IRST Cell Factory and actually used for patients with melanoma and other tumors. The enrollment is still ongoing. Expected results. The project will i) develop an immune-panel of prognostic and predictive markers to help clinicians to improve the therapeutic strategy for GBM patients; ii) provide preliminary results on the effectiveness of immunotherapy on GBM patients.