Assessment of Daily Life Mobility Levels using Wearable Inertial Sensors and Minimun Measured Input Models

Tracking activities during daily life and assessing movement parameters is essential for complementing the information gathered in confined environments such as clinical and physical activity laboratories for the assessment of mobility. Inertial measurement units (IMUs) are used as to monitor the motion of human movement for prolonged periods of time and without space limitations. The focus in this study was to provide a robust, low-cost and an unobtrusive solution for evaluating human motion using a single IMU. First part of the study focused on monitoring and classification of the daily life activities. A simple method that analyses the variations in signal was developed to distinguish two types of activity intervals: active and inactive. Neural classifier was used to classify active intervals; the angle with respect to gravity was used to classify inactive intervals. Second part of the study focused on extraction of gait parameters using a single inertial measurement unit (IMU) attached to the pelvis. Two complementary methods were proposed for gait parameters estimation. First method was a wavelet based method developed for the estimation of gait events. Second method was developed for estimating step and stride length during level walking using the estimations of the previous method. A special integration algorithm was extended to operate on each gait cycle using a specially designed Kalman filter. The developed methods were also applied on various scenarios. Activity monitoring method was used in a PRIN’07 project to assess the mobility levels of individuals living in a urban area. The same method was applied on volleyball players to analyze the fitness levels of them by monitoring their daily life activities. The methods proposed in these studies provided a simple, unobtrusive and low-cost solution for monitoring and assessing activities outside of controlled environments.

Development of original analytical methods for the therapeutic drug monitoring of CNS druges: Antipsychotics, Antidepressants and Anxiolytics-hypnotics

Great strides have been made in the last few years in the pharmacological treatment of neuropsychiatric disorders, with the introduction into the therapy of several new and more efficient agents, which have improved the quality of life of many patients. Despite these advances, a large percentage of patients is still considered “non-responder” to the therapy, not drawing any benefits from it. Moreover, these patients have a peculiar therapeutic profile, due to the very frequent application of polypharmacy, attempting to obtain satisfactory remission of the multiple aspects of psychiatric syndromes. Therapy is heavily individualised and switching from one therapeutic agent to another is quite frequent. One of the main problems of this situation is the possibility of unwanted or unexpected pharmacological interactions, which can occur both during polypharmacy and during switching. Simultaneous administration of psychiatric drugs can easily lead to interactions if one of the administered compounds influences the metabolism of the others. Impaired CYP450 function due to inhibition of the enzyme is frequent. Other metabolic pathways, such as glucuronidation, can also be influenced. The Therapeutic Drug Monitoring (TDM) of psychotropic drugs is an important tool for treatment personalisation and optimisation. It deals with the determination of parent drugs and metabolites plasma levels, in order to monitor them over time and to compare these findings with clinical data. This allows establishing chemical-clinical correlations (such as those between administered dose and therapeutic and side effects), which are essential to obtain the maximum therapeutic efficacy, while minimising side and toxic effects. It is evident the importance of developing sensitive and selective analytical methods for the determination of the administered drugs and their main metabolites, in order to obtain reliable data that can correctly support clinical decisions. During the three years of Ph.D. program, some analytical methods based on HPLC have been developed, validated and successfully applied to the TDM of psychiatric patients undergoing treatment with drugs belonging to following classes: antipsychotics, antidepressants and anxiolytic-hypnotics. The biological matrices which have been processed were: blood, plasma, serum, saliva, urine, hair and rat brain. Among antipsychotics, both atypical and classical agents have been considered, such as haloperidol, chlorpromazine, clotiapine, loxapine, risperidone (and 9-hydroxyrisperidone), clozapine (as well as N-desmethylclozapine and clozapine N-oxide) and quetiapine. While the need for an accurate TDM of schizophrenic patients is being increasingly recognized by psychiatrists, only in the last few years the same attention is being paid to the TDM of depressed patients. This is leading to the acknowledgment that depression pharmacotherapy can greatly benefit from the accurate application of TDM. For this reason, the research activity has also been focused on first and second-generation antidepressant agents, like triciclic antidepressants, trazodone and m-chlorophenylpiperazine (m-cpp), paroxetine and its three main metabolites, venlafaxine and its active metabolite, and the most recent antidepressant introduced into the market, duloxetine. Among anxiolytics-hypnotics, benzodiazepines are very often involved in the pharmacotherapy of depression for the relief of anxious components; for this reason, it is useful to monitor these drugs, especially in cases of polypharmacy. The results obtained during these three years of Ph.D. program are reliable and the developed HPLC methods are suitable for the qualitative and quantitative determination of CNS drugs in biological fluids for TDM purposes.

New synthetic bile acid analogue agonists of FXR and TGR5 receptors: Analytical methodologies for the study of their physico-chemical properties, pharmacokinetic activity and metabolism.

This thesis reports an integrated analytical approach for the study of physicochemical and biological properties of new synthetic bile acid (BA) analogues agonists of FXR and TGR5 receptors. Structure-activity data were compared with those previous obtained using the same experimental protocols on synthetic and natural occurring BA. The new synthetic BA analogues are classified in different groups according also to their potency as a FXR and TGR5 agonists: unconjugated and steroid modified BA and side chain modified BA including taurine or glycine conjugates and pseudo-conjugates (sulphonate and sulphate analogues). In order to investigate the relationship between structure and activity the synthetic analogues where admitted to a physicochemical characterization and to a preliminary screening for their pharmacokinetic and metabolism using a bile fistula rat model. Sensitive and accurate analytical methods have been developed for the quali-quantitative analysis of BA in biological fluids and sample used for physicochemical studies. Combined High Performance Liquid Chromatography Electrospray tandem mass spectrometry with efficient chromatographic separation of all studied BA and their metabolites have been optimized and validated. Analytical strategies for the identification of the BA and their minor metabolites have been developed. Taurine and glycine conjugates were identified in MS/MS by monitoring the specific ion transitions in multiple reaction monitoring (MRM) mode while all other metabolites (sulphate, glucuronic acid, dehydroxylated, decarboxylated or oxo) were monitored in a selected-ion reaction (SIR) mode with a negative ESI interface by the following ions. Accurate and precise data where achieved regarding the main physicochemical properties including solubility, detergency, lipophilicity and albumin binding . These studies have shown that minor structural modification greatly affect the pharmacokinetics and metabolism of the new analogues in respect to the natural BA and on turn their site of action, particularly where their receptor are located in the enterohepatic circulation.

Hair: a tool to evaluate the HPA axis activity

Hair cortisol is a novel marker to measure long-term secretion cortisol free from many methodological caveats associated with other matrices such as plasma, saliva, urine, milk and faeces. For decades hair analysis has been successfully used in forensic science and toxicology to evaluate the exposure to exogenous substances and assess endogenous steroid hormones. Evaluation of cortisol in hair matrix began about a decade ago and have over the past five years had a remarkable development by advancing knowledge and affirming this method as a new and efficient way to study the hypothalamic-pituitary-adrenal (HPA) axis activity over a long time period. In farm animals, certain environmental or management conditions can potentially activate the HPA axis. Given the importance of cortisol in monitoring the HPA axis activity, a first approach has involved the study on the distribution of hair cortisol concentrations (HCC) in healthy dairy cows showing a physiological range of variation of this hormone. Moreover, HCC have been significantly influenced also by changes in environmental conditions and a significant positive correlation was detected between HCC and cows clinically or physiologically compromised suggesting that these cows were subjected to repeated HPA axis activation. Additionally, Crossbreed F1 heifers showed significantly lower HCC compared to pure animals and a breed influence has been seen also on the HPA axis activity stimulated by an environmental change showing thus a higher level of resilience and a better adaptability to the environment of certain genotypes. Hair proved to be an excellent matrix also in the study of the activation of the HPA axis during the perinatal period. The use of hair analysis in research holds great promise to significantly enhance current understanding on the role of HPA axis over a long period of time.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.