Dal Burnout allo Spettro Post Traumatico da Stress nel personale medico e paramedico della U.O. di Medicina d'Urgenza e Pronto Soccorso dell'A.O.U.P.

Esplorare la prevalenza di PTSD, conclamato e subclinico, nel personale sanitario operante nell’Emergenza. Sono stati pertanto reclutati tutti gli operatori sanitari del Pronto Soccorso e del reparto di Medicina d’Urgenza Dell’Azienda Ospedaliera Universitaria Pisana al fine di somministrare il TALS-SR lifetime version ed il WSAS

DSM-5 PTSD and posttraumatic stress spectrum in Italian emergency personnel: correlations with work and social adjustment

The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) has recently recognized a particular risk for posttraumatic stress disorder (PTSD) among first responders (criterion A4), acknowledging emergency units as stressful places of employment. Little data is yet available on DSM-5 among emergency health operators. The aim of this study was to assess DSM-5 symptomatological PTSD and posttraumatic stress spectrum, as well as their impact on work and social functioning, in the emergency staff of a major university hospital in Italy. One hundred and ten subjects (doctors, nurses, and health-care assistants) were recruited at the Emergency Unit of the Azienda Ospedaliero-Universitaria Pisana (Italy) and assessed by the Trauma and Loss Spectrum-Self Report (TALS-SR) and Work and Social Adjustment Scale (WSAS). A 15.7% DSM-5 symptomatological PTSD prevalence rate was found. Nongraduated persons reported significantly higher TALS-SR Domain IV (reaction to loss or traumatic events) scores and a significantly higher proportion of individuals presenting at least one maladaptive behavior (TALS-SR Domain VII), with respect to graduate ones. Women reported significantly higher WSAS scores. Significant correlations emerged between PTSD symptoms and WSAS total scores among health-care assistants, nongraduates and women. Our results showed emergency workers to be at risk for posttraumatic stress spectrum and related work and social impairment, particularly among women and nongraduated subjects. Keywords: posttraumatic stress disorder (PTSD), emergency, emergency care workers, work and social functioning/adjustment, maladaptive behaviors, gender, educatio

Mirror mirror on the wall... an unobtrusive intelligent multisensory mirror for well-being status self-assessment and visualization

A person’s well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos and 3D scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data

From BOLD-FMRI signals to the prediction of subjective pain perception through a regularization algorithm

Functional magnetic resonance imaging, in particular theBOLD-fMRI technique, plays a dominant role in humanbrain mapping studies, mostly because of its noninvasivenessand relatively high spatio-temporal resolution.The main goal of fMRI data analysis has been to revealthe distributed patterns of brain areas involved in specificfunctions, by applying a variety of statistical methods withmodel-based or data-driven approaches. In the last years,several studies have taken a different approach, where thedirection of analysis is reversed in order to probe whetherfMRI signals can be used to predict perceptual or cognitivestates. In this study we test the feasibility of predicting theperceived pain intensity in healthy volunteers, based on fMRIsignals collected during an experimental pain paradigm lastingseveral minutes. In particular, we introduce a methodologicalapproach based on new regularization learning algorithmsfor regression problems.Functional magnetic resonance imaging, in particular the BOLD-fMRI technique, plays a dominant role in human brain mapping studies, mostly because of its non-invasiveness and relatively high spatio-temporal resolution. The main goal of fMRI data analysis has been to reveal the distributed patterns of brain areas involved in specific functions, by applying a variety of statistical methods with model-based or data-driven approaches. In the last years, several studies have taken a different approach, where the direction of analysis is reversed in order to probe whether fMRI signals can be used to predict perceptual or cognitive states. In this study we test the feasibility of predicting the perceived pain intensityin healthy volunteers, based on fMRI signals collected during an experimental pain paradigm lasting several minutes. In particular, we introduce a methodological approach based on new regularization learning algorithms for regression problems. \ua9 EURASIP, 2009

Predicting subjective pain perception based on BOLD-fMRI signals: a new machine learning approach

Functional magnetic resonance imaging, in particular the BOLD-fMRI technique, plays a dominant role in human brain mapping studies, mostly because of its non-invasiveness, good spatial and acceptable temporal resolution in comparison with other techniques. The main goal of fMRI data analysis has been to reveal the distributed patterns of brain areas involved in specific functions and their interactions, by applying a variety of univariate or multivariate statistical methods with model-basedor data-driven approaches. In the last few years, a growing number of studies have taken a different approach, where the direction of analysis is reversed in order to probe whether fMRI signals can be used to predict perceptual or cognitive states. In this study we wished to test the feasibility of predicting the perceived pain intensity in healthy volunteers, based on fMRI signals collected during an experimental pain paradigm lasting several minutes. To this end, we tested and optimized one methodological approach based on new regularization learning algorithms on this regression problem

A regularization algorithm for decoding perceptual profiles

In this study we wished to test the feasibility of predicting the perceived pain intensity in healthy volunteers, based on fMRI signals collected during an experimental pain paradigm lasting several minutes. This model of acute prolonged (tonic) pain bears some similarities with clinically relevant conditions, such as prolonged ongoing activity in nociceptors and spontaneous fluctuations of perceived pain intensity over time.To predict individual pain profile, we tested and optimized one methodological approach based on new regularization learning algorithms on this regression problem

Morpho-functional imaging of coronary anatomy and left ventricular perfusion obtained by cardiac CT

Volumetric computed tomography (CT) angiography has become a standard non-invasive routine procedure for cardiac imaging and coronary arteries pathology detection. However, before the diagnosis process, a pre-processing task is critical for an accurate examination of the vessels. Specially, the user has to manually remove obscuring structures in order to get an accurate visualization of coronary arteries. Indeed, the coronaries are always hidden by surrounding organs of the heart such as liver, sternum, ribs and lungs which prevent the pathologist from getting a clear view of the heart surface. In this paper, we propose a fast algorithm to automatically isolate the heart anatomy in 3D CT cardiac data sets. Our work eliminates the tedious and time consuming step of the manual delineation and pro- vides a clear and well defined view of the coronary arteries. Consequently, the user can quickly identify suspicious segments on the isolated heart. So far, works related to heart segmentation have mainly focused on heart cavities delineation, which is not suited for coronaries visualization [1]. In contrast, our algorithm extracts the heart cavities, the myocardium and coronaries as a single object

Epicardial fat volume assessment in cardiac CT

Epicardial fat, as other visceral fat localizations, is correlated with car- diovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns remain about the method for measuring epi- cardial fat, its regional distribution on the myocardium, as well as the accuracy and reproducibility of such measurements. At present, dedi- cated software procedures to assess epicardial fat are lacking. On the other hand, manual fat segmentation requires a huge and tedious operator intervention, which is expected to cause inaccuracy and large observer- dependent variability. The aim of this study was twofold: (1) the devel- opment of a procedure devoted to assess the volume of epicardial fat, (2) the evaluation of the related intra and inter-observer variability in CT scans, both with and without contrast medium injection