Biomedical engineering for healthy ageing. Predictive tools for falls

Falls are common and burdensome accidents among the elderly. About one third of the population aged 65 years or more experience at least one fall each year. Fall risk assessment is believed to be beneficial for fall prevention. This thesis is about prognostic tools for falls for community-dwelling older adults. We provide an overview of the state of the art. We then take different approaches: we propose a theoretical probabilistic model to investigate some properties of prognostic tools for falls; we present a tool whose parameters were derived from data of the literature; we train and test a data-driven prognostic tool. Finally, we present some preliminary results on prediction of falls through features extracted from wearable inertial sensors. Heterogeneity in validation results are expected from theoretical considerations and are observed from empirical data. Differences in studies design hinder comparability and collaborative research. According to the multifactorial etiology of falls, assessment on multiple risk factors is needed in order to achieve good predictive accuracy.

La distribuzione del potere tra le Istituzioni Europee e la procedura di co-decisione. Il caso della riforma della Politica Agricola Comune

Questa ricerca analizza le implicazioni derivanti dall’introduzione della procedura di co-decisione come procedura legislativa ordinaria nel processo di riforma della politica agricola comune. La diversa distribuzione dei poteri tra le istituzioni europee modifica gli assetti istituzionali e fornisce al Parlamento il ruolo di colegislatore in materia agricola. La forma assunta dalla nuova politica agricola europea scaturisce dalla configurazione dei poteri di contrattazione che ciascun attore ha mostrato nella sede dei triloghi negoziali. La ricerca tenta di verificare la accuratezza predittiva di diversi modelli di contrattazione legislativa attraverso il confronto e la verifica degli errori di predizione sui risultati finali di alcune questioni salienti della riforma della Politica Agricola Comune e allo stesso tempo, cerca di identificare il peso del potere del Parlamento europeo in veste di co-legislatore nel processo di riforma della PAC post-2013.

Identification of outcome-oriented cut-offs for copy number alterations in multiple myeloma: predictive biomarkers with improved prognostic accuracy

Aim of the present study was to develop a statistical approach to define the best cut-off Copy number alterations (CNAs) calling from genomic data provided by high throughput experiments, able to predict a specific clinical end-point (early relapse, 18 months) in the context of Multiple Myeloma (MM). 743 newly diagnosed MM patients with SNPs array-derived genomic and clinical data were included in the study. CNAs were called both by a conventional (classic, CL) and an outcome-oriented (OO) method, and Progression Free Survival (PFS) hazard ratios of CNAs called by the two approaches were compared. The OO approach successfully identified patients at higher risk of relapse and the univariate survival analysis showed stronger prognostic effects for OO-defined high-risk alterations, as compared to that defined by CL approach, statistically significant for 12 CNAs. Overall, 155/743 patients relapsed within 18 months from the therapy start. A small number of OO-defined CNAs were significantly recurrent in early-relapsed patients (ER-CNAs) - amp1q, amp2p, del2p, del12p, del17p, del19p -. Two groups of patients were identified either carrying or not ≥1 ER-CNAs (249 vs. 494, respectively), the first one with significantly shorter PFS and overall survivals (OS) (PFS HR 2.15, p<0001; OS HR 2.37, p<0.0001). The risk of relapse defined by the presence of ≥1 ER-CNAs was independent from those conferred both by R-IIS 3 (HR=1.51; p=0.01) and by low quality (< stable disease) clinical response (HR=2.59 p=0.004). Notably, the type of induction therapy was not descriptive, suggesting that ER is strongly related to patients’ baseline genomic architecture. In conclusion, the OO- approach employed allowed to define CNAs-specific dynamic clonality cut-offs, improving the CNAs calls’ accuracy to identify MM patients with the highest probability to ER. As being outcome-dependent, the OO-approach is dynamic and might be adjusted according to the selected outcome variable of interest.

Accuracy of liver stiffness measurement using fibroscan ® to predict the response to antiviral therapy in patients with chronic hepatitis c viral infection

Introduction: Antiviral therapy can prevent disease progression in patients with chronic hepatitis C . Transient Elastografy (TE; Fibroscan) is an accurate surrogate marker to liver fibrosis, by measuring liver stiffness (LS). LS decrease has been associated with sustained virologic response (SVR). Aim: to assess the changes of LS measurments in CHC patients during and one year after Interferon (IFN)-based antiviral therapy (IFN/ribavirin) or (telaprevir+IFN/ribavirin). Methods: consecutive 69 CHC patients (53.6% females, mean age 57.9 ± 11.4) who underwent antiviral therapy for at least 20 weeks were enrolled. LS was measured using FibroScan at baseline, after three months, at the end of treatment and one year after treatment discontinuation. Fibrosis was graded using METAVIR score. Results: twenty patients treated with triple therapy and 49 with IFN/ribavirin. Fifty patients had SVR and 19 were non-responders. SVR patients: F0-F1, F2 and F3 patients (39.1%, 7.2% and 17.4%; respectively) showed no significant LS decrease (P= 0.186, 0.068 and 0.075; respectively). Conversely, in F4 patients (36.2%) LS was significantly decreased (P=0.015) after one year of treatment completion. In all patients with no SVR, no significant decrease in LS was observed. Interestingly, all Patients with F4 fibrosis (even non-responders) showed an initial significant decrease in LS (P=0.024) at 3 months after the start of treatment. However, this decrease was not predictive of SVR; area under the ROC curve 0.369 (CI %: 0.145-0.592) P= 0.265. Conclusion: Our study showed that initial decrease in LSM, especially in patients with higher baseline fibrosis score is unlikely to predict an SVR. In addition no significant association was found between clinical or virological parameters and fibrosis improvement. Further studies are needed to delineate the most appropriate clinical scenarios for the LSM by Fibroscan in chronic hepatitis C and its role in monitoring the response to antiviral treatment.

The predictive role of the human gut microbiota in covid-19: implications for disease severity and post-covid syndrome

The COVID-19 pandemic, sparked by the SARS-CoV-2 virus, stirred global comparisons to historical pandemics. Initially presenting a high mortality rate, it later stabilized globally at around 0.5-3%. Patients manifest a spectrum of symptoms, necessitating efficient triaging for appropriate treatment strategies, ranging from symptomatic relief to antivirals or monoclonal antibodies. Beyond traditional approaches, emerging research suggests a potential link between COVID-19 severity and alterations in gut microbiota composition, impacting inflammatory responses. However, most studies focus on severe hospitalized cases without standardized criteria for severity. Addressing this gap, the first study in this thesis spans diverse COVID-19 severity levels, utilizing 16S rRNA amplicon sequencing on fecal samples from 315 subjects. The findings highlight significant microbiota differences correlated with severity. Machine learning classifiers, including a multi-layer convoluted neural network, demonstrated the potential of microbiota compositional data to predict patient severity, achieving an 84.2% mean balanced accuracy starting one week post-symptom onset. These preliminary results underscore the gut microbiota's potential as a biomarker in clinical decision-making for COVID-19. The second study delves into mild COVID-19 cases, exploring their implications for ‘long COVID’ or Post-Acute COVID-19 Syndrome (PACS). Employing longitudinal analysis, the study unveils dynamic shifts in microbial composition during the acute phase, akin to severe cases. Innovative techniques, including network approaches and spline-based longitudinal analysis, were deployed to assess microbiota dynamics and potential associations with PACS. The research suggests that even in mild cases, similar mechanisms to hospitalized patients are established regarding changes in intestinal microbiota during the acute phase of the infection. These findings lay the foundation for potential microbiota-targeted therapies to mitigate inflammation, potentially preventing long COVID symptoms in the broader population. In essence, these studies offer valuable insights into the intricate relationships between COVID-19 severity, gut microbiota, and the potential for innovative clinical applications.