Variazione dei markers di attivata coagulazione indotta dalla terapia infusionale con Infliximab in pazienti affetti da malattie infiammatorie croniche intestinali

INTRODUCTION: A relationship between inflammatory response and coagulation is suggested by many observations. In particular, pro-inflammatory cytokines, such as TNFalpha, promote the activation of coagulation and reduce the production of anticoagulant molecules. It is known that inflammatory bowel diseases show a prothrombotic state and a condition of hypercoagulability. Aim of our study was to evaluate whether anti-TNFalpha therapy induces changes in the levels of coagulation activation markers in IBD patients. MATERIALS AND METHODS: We analyzed 48 plasma samples obtained before and 1 hour after 24 infliximab infusions (5 mg/kg) in 9 IBD patients (5 men and 4 women; mean age: 47.6+17.6 years; 4 Crohn's disease, 4 Ulcerative Colitis,1 Indeterminate Colitis). F1+2 and D-dymer levels were measured in each sample using ELISA methods.The data were statistically analyzed by means of Wilcoxon matched paired test. RESULTS: Median F1+2 levels were markdely reduced 1 hour after anti-TNFα infusion (median pre-infusion levels were 247.0 pmol/L and median post-infusion levels were 185.3 pmol/L) (p<0.002). Median D-dymer levels were also significantly reduced, from 485.2 ng/mL to 427.6 ng/mL (p< 0.001). These modifications were more evident in patients naive for infliximab therapy (p<0.02 for F1+2 and p<0.02 for D-dymer) and in Crohn's disease compared with Ulcerative Colitis patients (p=0.01 for F1+2 and p<0.007 for D-dymer).CONCLUSIONS: Infusion of infliximab significantly reduces the activation of coagulation cascade in IBD patients. This effect is early enough to suggest a direct effect of infliximab on the coagulation cascade and a possible new anti-inflammatory mechanism of action of this molecule.

L'influenza del sonno sull'andamento temporale della consolidazione delle abilità procedurali

Background: It is well known, since the pioneristic observation by Jenkins and Dallenbach (Am J Psychol 1924;35:605-12), that a period of sleep provides a specific advantage for the consolidation of newly acquired informations. Recent research about the possible enhancing effect of sleep on memory consolidation has focused on procedural memory (part of non-declarative memory system, according to Squire’s taxonomy), as it appears the memory sub-system for which the available data are more consistent. The acquisition of a procedural skill follows a typical time course, consisting in a substantial practice-dependent learning followed by a slow, off-line improvement. Sleep seems to play a critical role in promoting the process of slow learning, by consolidating memory traces and making them more stable and resistant to interferences. If sleep is critical for the consolidation of a procedural skill, then an alteration of the organization of sleep should result in a less effective consolidation, and therefore in a reduced memory performance. Such alteration can be experimentally induced, as in a deprivation protocol, or it can be naturally observed in some sleep disorders as, for example, in narcolepsy. In this research, a group of narcoleptic patients, and a group of matched healthy controls, were tested in two different procedural abilities, in order to better define the size and time course of sleep contribution to memory consolidation. Experimental Procedure: A Texture Discrimination Task (Karni & Sagi, Nature 1993;365:250-2) and a Finger Tapping Task (Walker et al., Neuron 2002;35:205-11) were administered to two indipendent samples of drug-naive patients with first-diagnosed narcolepsy with cataplexy (International Classification of Sleep Disorder 2nd ed., 2005), and two samples of matched healthy controls. In the Texture Discrimination task, subjects (n=22) had to learn to recognize a complex visual array on the screen of a personal computer, while in the Finger Tapping task (n=14) they had to press a numeric sequence on a standard keyboard, as quickly and accurately as possible. Three subsequent experimental sessions were scheduled for each partecipant, namely a training session, a first retrieval session the next day, and a second retrieval session one week later. To test for possible circadian effects on learning, half of the subjects performed the training session at 11 a.m. and half at 17 p.m. Performance at training session was taken as a measure of the practice-dependent learning, while performance of subsequent sessions were taken as a measure of the consolidation level achieved respectively after one and seven nights of sleep. Between training and first retrieval session, all participants spent a night in a sleep laboratory and underwent a polygraphic recording. Results and Discussion: In both experimental tasks, while healthy controls improved their performance after one night of undisturbed sleep, narcoleptic patients showed a non statistically significant learning. Despite this, at the second retrieval session either healthy controls and narcoleptics improved their skills. Narcoleptics improved relatively more than controls between first and second retrieval session in the texture discrimination ability, while their performance remained largely lower in the motor (FTT) ability. Sleep parameters showed a grater fragmentation in the sleep of the pathological group, and a different distribution of Stage 1 and 2 NREM sleep in the two groups, being thus consistent with the hypothesis of a lower consolidation power of sleep in narcoleptic patients. Moreover, REM density of the first part of the night of healthy subjects showed a significant correlation with the amount of improvement achieved at the first retrieval session in TDT task, supporting the hypothesis that REM sleep plays an important role in the consolidation of visuo-perceptual skills. Taken together, these results speak in favor of a slower, rather than lower consolidation of procedural skills in narcoleptic patients. Finally, an explanation of the results, based on the possible role of sleep in contrasting the interference provided by task repetition is proposed.

Sistemi riconfigurabili a basso consumo per applicazioni di monitoraggio distribuito

The term Ambient Intelligence (AmI) refers to a vision on the future of the information society where smart, electronic environment are sensitive and responsive to the presence of people and their activities (Context awareness). In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices. This promotes the creation of pervasive environments improving the quality of life of the occupants and enhancing the human experience. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. Ambient intelligent systems are heterogeneous and require an excellent cooperation between several hardware/software technologies and disciplines, including signal processing, networking and protocols, embedded systems, information management, and distributed algorithms. Since a large amount of fixed and mobile sensors embedded is deployed into the environment, the Wireless Sensor Networks is one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes which can be deployed in a target area to sense physical phenomena and communicate with other nodes and base stations. These simple devices typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). WNS promises of revolutionizing the interactions between the real physical worlds and human beings. Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. To fully exploit the potential of distributed sensing approaches, a set of challengesmust be addressed. Sensor nodes are inherently resource-constrained systems with very low power consumption and small size requirements which enables than to reduce the interference on the physical phenomena sensed and to allow easy and low-cost deployment. They have limited processing speed,storage capacity and communication bandwidth that must be efficiently used to increase the degree of local ”understanding” of the observed phenomena. A particular case of sensor nodes are video sensors. This topic holds strong interest for a wide range of contexts such as military, security, robotics and most recently consumer applications. Vision sensors are extremely effective for medium to long-range sensing because vision provides rich information to human operators. However, image sensors generate a huge amount of data, whichmust be heavily processed before it is transmitted due to the scarce bandwidth capability of radio interfaces. In particular, in video-surveillance, it has been shown that source-side compression is mandatory due to limited bandwidth and delay constraints. Moreover, there is an ample opportunity for performing higher-level processing functions, such as object recognition that has the potential to drastically reduce the required bandwidth (e.g. by transmitting compressed images only when something ‘interesting‘ is detected). The energy cost of image processing must however be carefully minimized. Imaging could play and plays an important role in sensing devices for ambient intelligence. Computer vision can for instance be used for recognising persons and objects and recognising behaviour such as illness and rioting. Having a wireless camera as a camera mote opens the way for distributed scene analysis. More eyes see more than one and a camera system that can observe a scene from multiple directions would be able to overcome occlusion problems and could describe objects in their true 3D appearance. In real-time, these approaches are a recently opened field of research. In this thesis we pay attention to the realities of hardware/software technologies and the design needed to realize systems for distributed monitoring, attempting to propose solutions on open issues and filling the gap between AmI scenarios and hardware reality. The physical implementation of an individual wireless node is constrained by three important metrics which are outlined below. Despite that the design of the sensor network and its sensor nodes is strictly application dependent, a number of constraints should almost always be considered. Among them: • Small form factor to reduce nodes intrusiveness. • Low power consumption to reduce battery size and to extend nodes lifetime. • Low cost for a widespread diffusion. These limitations typically result in the adoption of low power, low cost devices such as low powermicrocontrollers with few kilobytes of RAMand tenth of kilobytes of program memory with whomonly simple data processing algorithms can be implemented. However the overall computational power of the WNS can be very large since the network presents a high degree of parallelism that can be exploited through the adoption of ad-hoc techniques. Furthermore through the fusion of information from the dense mesh of sensors even complex phenomena can be monitored. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas:Low Power Video Sensor Node and Video Processing Alghoritm and Multimodal Surveillance . Low Power Video Sensor Nodes and Video Processing Alghoritms In comparison to scalar sensors, such as temperature, pressure, humidity, velocity, and acceleration sensors, vision sensors generate much higher bandwidth data due to the two-dimensional nature of their pixel array. We have tackled all the constraints listed above and have proposed solutions to overcome the current WSNlimits for Video sensor node. We have designed and developed wireless video sensor nodes focusing on the small size and the flexibility of reuse in different applications. The video nodes target a different design point: the portability (on-board power supply, wireless communication), a scanty power budget (500mW),while still providing a prominent level of intelligence, namely sophisticated classification algorithmand high level of reconfigurability. We developed two different video sensor node: The device architecture of the first one is based on a low-cost low-power FPGA+microcontroller system-on-chip. The second one is based on ARM9 processor. Both systems designed within the above mentioned power envelope could operate in a continuous fashion with Li-Polymer battery pack and solar panel. Novel low power low cost video sensor nodes which, in contrast to sensors that just watch the world, are capable of comprehending the perceived information in order to interpret it locally, are presented. Featuring such intelligence, these nodes would be able to cope with such tasks as recognition of unattended bags in airports, persons carrying potentially dangerous objects, etc.,which normally require a human operator. Vision algorithms for object detection, acquisition like human detection with Support Vector Machine (SVM) classification and abandoned/removed object detection are implemented, described and illustrated on real world data. Multimodal surveillance: In several setup the use of wired video cameras may not be possible. For this reason building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. Energy efficiency for wireless smart camera networks is one of the major efforts in distributed monitoring and surveillance community. For this reason, building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. The Pyroelectric Infra-Red (PIR) sensors have been used to extend the lifetime of a solar-powered video sensor node by providing an energy level dependent trigger to the video camera and the wireless module. Such approach has shown to be able to extend node lifetime and possibly result in continuous operation of the node.Being low-cost, passive (thus low-power) and presenting a limited form factor, PIR sensors are well suited for WSN applications. Moreover techniques to have aggressive power management policies are essential for achieving long-termoperating on standalone distributed cameras needed to improve the power consumption. We have used an adaptive controller like Model Predictive Control (MPC) to help the system to improve the performances outperforming naive power management policies.

Sviluppo di un approccio congiunto fuzzy-Bayesiano per l'analisi e la modellizzazione degli ecosistemi: applicazione ad ecosistemi marini costieri.

Nell’attuale contesto di aumento degli impatti antropici e di “Global Climate Change” emerge la necessità di comprenderne i possibili effetti di questi sugli ecosistemi inquadrati come fruitori di servizi e funzioni imprescindibili sui quali si basano intere tessiture economiche e sociali. Lo studio previsionale degli ecosistemi si scontra con l’elevata complessità di questi ultimi in luogo di una altrettanto elevata scarsità di osservazioni integrate. L’approccio modellistico appare il più adatto all’analisi delle dinamiche complesse degli ecosistemi ed alla contestualizzazione complessa di risultati sperimentali ed osservazioni empiriche. L’approccio riduzionista-deterministico solitamente utilizzato nell’implementazione di modelli non si è però sin qui dimostrato in grado di raggiungere i livelli di complessità più elevati all’interno della struttura eco sistemica. La componente che meglio descrive la complessità ecosistemica è quella biotica in virtù dell’elevata dipendenza dalle altre componenti e dalle loro interazioni. In questo lavoro di tesi viene proposto un approccio modellistico stocastico basato sull’utilizzo di un compilatore naive Bayes operante in ambiente fuzzy. L’utilizzo congiunto di logica fuzzy e approccio naive Bayes è utile al processa mento del livello di complessità e conseguentemente incertezza insito negli ecosistemi. I modelli generativi ottenuti, chiamati Fuzzy Bayesian Ecological Model(FBEM) appaiono in grado di modellizare gli stati eco sistemici in funzione dell’ elevato numero di interazioni che entrano in gioco nella determinazione degli stati degli ecosistemi. Modelli FBEM sono stati utilizzati per comprendere il rischio ambientale per habitat intertidale di spiagge sabbiose in caso di eventi di flooding costiero previsti nell’arco di tempo 2010-2100. L’applicazione è stata effettuata all’interno del progetto EU “Theseus” per il quale i modelli FBEM sono stati utilizzati anche per una simulazione a lungo termine e per il calcolo dei tipping point specifici dell’habitat secondo eventi di flooding di diversa intensità.

Serum anti-interferon alpha antibodies in chronic hepatitis C patients treated with pegylated interferon alpha containing therapy

The development of anti-IFNα antibodies is an occurrence described in chronic hepatitis C patients during treatment with Interferonα/PEG-Interferonα. However, its relevance, especially in difficult-to treat patients, has not been defined. Methods: We retrospectively measured the serum levels of anti-IFNα antibodies (baseline and week 12) and IFNα levels (week 12) by ELISA in 76 previous non-responders, and in 14 naive patients treated with Pegylated-IFNα and Ribavirin. A group of 57 healthy donors (HD) was also assessed as control. Positivity to anti-IFNα antibodies was established on the values of HD. Results: Baseline anti-IFNα antibodies were detected in 15.5% of patients and in 7% of HD, with significantly higher concentrations in patients than HD (181.5±389.9 vs 95.9±143.0 ng mL−1, p=0.0023). All positive patients were IFNα-experienced. At week 12, the prevalence of positivity increased to 22.3 and 28.5% in experienced and naïve patients, respectively, and the levels of anti-IFNα antibodies did not differ between the two groups (391±792.3 vs 384.7±662.6 ng mL−1, respectively). IFNα concentrations were significantly lower in antibody-positive patients than in antibody-negatives (988.2±1402 vs 3462±830.8 pg mL−1, p≤0.0001) and the levels of antibodies and IFNα were inversely correlated (r=-0.405, p=0.0001). The antibody-positive population clustered in null responders (67%) and 19/21 patients (90%) did not achieve SVR. Conclusions: The development of anti-IFNα antibodies is a non-negligible occurrence in patients treated with PEG-IFNα, is stable over time, and has a relevant clinical impact when associated with low levels of circulating PEG-IFNα. It should be considered in patients undergoing treatments including PEG-IFNα.