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Design of Experiments (DoE) statistical methodology permits the simultaneous evaluation of the effects of different factors on experimental performance and the analysis of their interactions in order to identify their optimal combinations. Compared to classical approaches based on changing only one factor at a time (OFAT), DoE facilitates the exploration of a broader range of parameters combinations, as well as providing the possibility to select a limited number of combinations covering the whole frame. The advantage of DoE is to maximise the amount of information provided and to save both time and money. DoE has been primarily used in industry to maximise process robustness, but recently it has also been applied in biomedical research to different types of multivariable analyses, from determination of the best cell media composition to the optimisation of entire multi-step laboratory protocols such as cell transfection.

The Design of Experiment (DoE) is a statistical methodology, very useful in the case of multivariable assays, that permits to evaluate simultaneously the influence of different factors on a specific output and to analyse the interactions among them in order to identify their optimal combinations. Moreover, DoE allows to select from the huge numbers of combinations only a limited number, in order to cover the whole frame, allowing to save both time and money. DoE was mostly used in industrial field in order to maximize robust processes, but recently it has been also applied in biomedical research field. Different studies have demonstrated the advantages of using a DoE approach compared to the classical one (a single parameter is tested in each assay) in the context of automated experiments, determination of cell media [1] compositions or HPLC tuning [2]. In the present study, we utilize for the first time the DoE methodology to optimize the transfection protocol of neural cells, as an example of DoE application to a laboratory procedure. Neural precursor cells are hard to transfect and refractory to lipidic reagents [3], for this reason we choose as transfectant reagent the cationic not-lipidic Poliethylenimmine (PEI). The DoE approach allowed us to identify the main variables (factors) affecting the transfection efficiency and to discover their optimal combinations, developing a protocol that let us to triplicate the transfection efficiency respect to the initial conditions. Moreover, the covariance analysis unmasked significant variable interactions impossible to calculate in one factor-variation tests used in normal laboratory practices. Our results indicate that DoE might be very helpful also in research for the identification of the better experimental conditions and the analysis of interactions between different variables.

Movement analysis is a powerful tool for the diagnosis of neurological conditions, as well as patient assessment and follow-up during rehabilitation programs. In spite of the available systems allowing a quantitative analysis of a subject's movement control performances, the clinical assessment and diagnostic approach still relies mostly on non-quantitative exams, such as clinical scales. Further, studying balance control, gait and activities of daily living poses relevant technical challenges, which greatly limit the availability of testing facilities. The goal of our project was therefore to develop a new system based on wearable sensors for movement analysis and scoring of performances. A prototype 3-sensors system was tested on a group of 4 normal subjects while carrying out a set of full body movement exercises drawn by clinical scales for the assessment of movement and balance control.

The study of postural sway during quiet stance has proved to be a useful approach to investigate the function of the balance system. Recent studies have suggested that providing information on postural sway to vestibular patients through biofeedback may improve their balance awareness and therefore reduce their risk of falling. One drawback common to these approaches is related to timing: informing a patient about current balance conditions may not allow enough time to react and avoid a fall. Here we propose a new technique for predicting relevant balance related events based on the recording of inertial information on trunk and thigh movement using wearable devices. We have developed a regressive model for the prediction of quiet stance dynamics of the center of body mass (CM), based on these sensory data. Our preliminary results show that, with careful signal processing, such approach may allow to learn quiet stance dynamics based on the inverted pendulum model and use it in predicting critical balance conditions with a few hundreds of milliseconds advance. When these predictions are then used for event-detection the system provides accurate results and is thus promising for the development of a fall prevention device.