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Background and case: The vast majority of Parkinson’s disease (PD) cases occur sporadically without any obvious etiological schema. That poses major hurdles to the discovery of effective preventative and treatment strategies. Here we are reporting the case of an 86-year-old Caucasian male, with a 21-year history of PD, who reports complete resolution of PD - related motor symptoms after treatment with antibiotics for suspected bacterial osteomyelitis.Results: After a right leg injury, the patient was prescribed a ten-day course of ciprofloxacin and a four-week course of dicloxacillin for suspected osteomyelitis. After completion of the antibiotics, the patient reported his pervasive motor symptoms as “completely resolved” which to this day have not returned.Conclusion: PD is a progressive, mostly idiopathic, neurodegenerative disorder with effective but limited treatment options and without compelling preventative strategies. Here we identify the first case indicating the resolution of PD-linked motor symptoms following an unrelated treatment course with antibiotics. This raises interesting questions about the use of antibiotics in PD and the potential of an etiological bacterial connection. The microbiome signature of PD is a current ‘hot topic’ of investigation and studies need to continue investigating bacteria as a possible causes of PD and antibiotics as effective treatment modalities.

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

We describe a bio-feedback system for rehabilitation based on an accelerometric sensor, a processing unit and an output unit. The sensory unit, which incorporates a cell with three uni-axial accelerometers and three rate gyroscopes, is mounted on the subject's back (L5 level) using a velcro belt and allows to evaluate the trunk accelerations in the anterior-posterior (AP) and medial-lateral (ML) directions. The processing unit is composed by a laptop with Arduino board, able to acquire in real time data coming from sensory unit through the data acquisition module of Max/MSP, and to produce an audio output. The output unit is a pair of headphones which produce a kind of sound whose activation level is proportional to the magnitude of patient's displacement. A set of experiments have been carried out in order to measure the performance of this system, and results show that it can significantly improve rehabilitation times.

AIM The understanding of surgical gesture, by means of a measuring apparatus, can play a key role in the evaluation of surgical performance. To this aim, a neural network classification algorithm can be helpful, since it combines good generalization performances along with a parsimonious architecture when dealing with high dimensional classification problems. We present its use as a surgical training tool for both laparoscopic and open surgery, a field of research highly underrepresented in the surgical teaching scenario. We operated a bounding box decomposition of surgeon’s hand movements analysis and gesture recognition during training of novice surgeons. This feature was applied to analyze trajectories of surgeon’s wrist and finger postures, so to recognize different hand gestures. METHODS Dataset of surgical gestures: 5 master surgeons, 5 resident surgeons and 5 attending surgeons made this tasks: interrupted stitch; running suture; knot tying exercise. Gesture measurement: we developed a data glove on the basis of acquired experiences. This glove is provided with sensors to measure movements of distal interphalangeal, proximal interphalangeal, metacarpo phalangeal, finger joints and wrist postures. Gesture classification: synthesis of an algorithm automatically assigns each gesture to a predefined class. RESULTS Operator’s training: Currently, mentors transfer their expertise to trainee via practical demonstrations and oral instructions. With recorded data of measures it is possible to reproduce such movements via avatar representation on a PC screen. It gets the important aspect that the same gesture can be represented several times always in the same manner and that it is possible to look at the gesture from all possible points of view, just rotating, translating, zooming the avatar. We developed a graphical interface capable to superimpose a “ghost” avatar of the learner upon the “guide” avatar of the expert. In this manner the trainee is capable to easily auto-evaluate her/his performance with instinctive ability. CONCLUSIONS This work, still in progress, would be an innovative, accurate and non invasive method to measure and evaluate surgical gestures. It will be useful to accelerate the in-training surgeon’s learning curve who can compare the basic level of his expertise with master surgeon’s level and verify step by step his improvement.

This study is aimed at identifying the collateral circulation in case of femoral-aorta-iliac axis obstruction, with the purpose of a more correct therapeutic indication being either medical or surgical or physiotherapeutic or combined.