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 outcome and biological characteristics of Chronic Myeloid Leukemia patients treated with nilotinib front-line

The present work reports the outcome of the GIMEMA CML WP study CML0811, an independent trial investigating nilotinib as front-line treatment in chronic phase chronic myeloid leukemia (CML). Moreover, the results of the proteomic analysis of the CD34+ cells collected at CML diagnosis, compared to the counterpart from healthy donors, are reported. Our study confirmed that nilotinib is highly effective in the prevention of the progression to accelerated/blast phase, a condition that today is still associated with high mortality rates. Despite the relatively short follow-up, cardiovascular issues, particularly atherosclerotic adverse events (AE), have emerged, and the frequency of these AEs may counterbalance the anti-leukemic efficacy. The deep molecular response rates in our study compare favorably to those obtained with imatinib, in historic cohorts, and confirm the findings of the Company-sponsored ENESTnd study. Considering the increasing rates of deep MR over time we observed, a significant proportion of patients will be candidate to treatment discontinuation in the next years, with higher probability of remaining disease-free in the long term. The presence of the additional and complex changes we found at the proteomic level in CML CD34+ cells should be taken into account for the investigation on novel targeted therapies, aimed at the eradication of the disease.