The Timed Up and Go (TUG) test is a clinical test to assess mobility in Parkinson's disease (PD). It consists of rising from a chair, walking, turning, and sitting. Its total duration is the traditional clinical outcome. In this study an instrumented TUG (iTUG) was used to supplement the quantitative information about the TUG performance of PD subjects: a single accelerometer, worn at the lower back, was used to record the acceleration signals during the test and acceleration-derived measures were extracted from the recorded signals. The aim was to select reliable measures to identify and quantify the differences between the motor patterns of healthy and PD subjects; in order to do so, besides comparing each measure individually to find significant group differences, feature selection and classification were used to identify the distinctive motor pattern of PD subjects. A subset of three features (two from Turning, one from the Sit-to-Walk component), combined with an easily-interpretable classifier (Linear Discriminant Analysis), was found to have the best accuracy in discriminating between healthy and early-mild PD subjects. These results suggest that the proposed iTUG can characterize PD motor impairment and, hence, may be used for evaluation, and, prospectively, follow-up, and monitoring of disease progression.

This work presents a mathematical model of the human respiratory control system, based on physiological knowledge. It includes three compartments for gas storage and exchange (lungs, brain tissue and other body tissues), and various kinds of feedback mechanisms. These comprehend peripheral chemoreceptors in the carotid body, central chemoreceptors in the medulla and a central ventilatory depression. The latter acts by reducing the response of the central neural system to the afferent peripheral chemoreceptor activity during prolonged hypoxia of the brain tissue. Furthermore, the model considers local blood flow adjustments in response to O2 and CO2 arterial pressure changes. In this study, the model has been validated by simulating the response to square changes in alveolar PCO2, performed at different constant levels of alveolar PO2. A good agreement with data reported in the literature has been checked. Subsequently, a sensitivity analysis on the role of the main feedback mechanisms on ventilation response to CO2 has been performed. The results suggest that the ventilatory response to CO2 challenges during hyperoxia can be almost completely ascribed to the central chemoreflex, while, during normoxia, the peripheral chemoreceptors provide a modest contribution too. By contrast, the response to hypercapnic stimuli during hypoxia involves a complex superimposition among different factors with disparate dynamics. Hence, results suggest that the ventilatory response to hypercapnia during hypoxia is more complex than that provided by simple empirical models, and that discrimination between the central and peripheral components based on time constants may be misleading.

A mathematical model of solute kinetics oriented to the simulation of hemodialysis is presented. It includes a three-compartment model of body fluids (plasma, interstitial and intracellular), a two-compartment description of the main solutes (K+, Na+, Cl-, urea, HCO3-, H+), and acid-base equilibrium through two buffer systems (bicarbonate and noncarbonic buffers). Tentative values for the main model parameters can be given a priori, on the basis of body weight and plasma concentration values measured before beginning the session. The model allows computation of the amount of sodium removed during hemodialysis, and may enable the prediction of plasma volume and osmolarity changes induced by a given sodium concentration profile in the dialysate and by a given ultrafiltration profile. Model predictions are compared with clinical data obtained during 11 different profiled hemodialysis sessions, both with all parameters assigned a priori, and after individual estimation of dialysances and mass-transfer coefficients. In most cases, the agreement between the time pattern of model solute concentrations in plasma and clinical data was satisfactory. In two sessions, blood volume changes were directly measured in the patient, and in both cases the agreement with model predictions was acceptable. The present model can be used to improve the dialysis session taking some characteristics of individual patients into account, in order to minimize intradialytic unbalances (such as hypotension or disequilibrium syndrome).