Valutazione non invasiva dell'epatopatia Fontan indotta

Introduzione: L’intervento di Fontan comporta un aumento istantaneo della pressione venosa centrale che causa, nel medio-lungo termine, una forma di epatopatia specifica detta FALD. Il monitoraggio della FALD è complesso ma potrebbe consentire di bloccarne o rallentarne l’insorgenza. Lo studio ha valutato l’efficacia delle modalità di monitoraggio non invasivo. Materiale e metodi: Sei pazienti (età media 24 anni) operati presso l’IRCCS Azienda Ospedaliero Universitaria di Bologna sono stati sottoposti a RMN 4D-Flow e ad Ecodoppler epatico. Sono stati raccolti i dati anagrafici, morfologici, anamnestici e i markers sierologici per il calcolo degli scores MELD-XI, APRI, FIB4, i valori di Shear Stress assiale e circonferenziale e gli indici di pulsatilità e resistenza delle arterie epatica e renale. Risultati: Il tempo trascorso tra la Fontan e lo studio è stato di 17,8 anni. Età media alla Fontan 6,8 anni. Tutti i pazienti avevano un quadro compatibile con epatopatia. I markers sierologici e gli scores MELD-XI,APRI e FIB4 si sono dimostrati di scarsa utilità. All’ecografia tutti i pazienti avevano ecostruttura irregolare, splenomegalia e valori elevati di pulsatilità e resistenza dell’arteria epatica e splenica. La rigidità epatica media è stata di 12,4 Kpa. Alla RMN 4DF lo Shear stress assiale è stato massimo a livello del condotto (0,16 Pa) e minimo a livello delle vene sovra epatiche (0,05 Pa). Lo Shear Stress si è mostrato massimo nei pazienti con emodinamica sfavorevole e peggior quadro ecografico addominale, evidenziando aree di inefficienza energetica. Conclusioni: La combinazione delle diagnostiche di imaging non invasive potrebbe rivelarsi adeguata per il monitoraggio della FALD. In particolare, la RMN 4D Flow potrebbe rivelare aree di inefficienza energetica predisponenti alla FALD. Questo potrebbe indirizzare in modo specifico la terapia dei pazienti operati o addirittura indurre la modifica del disegno della Fontan verso forme più efficienti.

Metabolic activation pattern of distinct hippocampal subregions during spatial learning and memory retrieval.

Activation dynamics of hippocampal subregions during spatial learning and their interplay with neocortical regions is an important dimension in the understanding of hippocampal function. Using the (14C)-2-deoxyglucose autoradiographic method, we have characterized the metabolic changes occurring in hippocampal subregions in mice while learning an eight-arm radial maze task. Autoradiogram densitometry revealed a heterogeneous and evolving pattern of enhanced metabolic activity throughout the hippocampus during the training period and on recall. In the early stages of training, activity was enhanced in the CA1 area from the intermediate portion to the posterior end as well as in the CA3 area within the intermediate portion of the hippocampus. At later stages, CA1 and CA3 activations spread over the entire longitudinal axis, while dentate gyrus (DG) activation occurred from the anterior to the intermediate zone. Activation of the retrosplenial cortex but not the amygdala was also observed during the learning process. On recall, only DG activation was observed in the same anterior part of the hippocampus. These results suggest the existence of a functional segmentation of the hippocampus, each subregion being dynamically but also differentially recruited along the acquisition, consolidation, and retrieval process in parallel with some neocortical sites.

Experiencing flow while teaching and learning in individual tuition settings

Conferência anual da ISME

E-Learning as a shared service in shared services centers

Book Subtitle International Conference, CENTERIS 2010, Viana do Castelo, Portugal, October 20-22, 2010, Proceedings, Part II

Development of a semi-automatic self learning pattern recognition system for the control of electronic boards

This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway - Mayo Institute of Technology and an industrial company, Tyco/Mallinckrodt Galway. The project aimed to develop a semi - automatic, self - learning pattern recognition system capable of detecting defects on the printed circuits boards such as component vacancy, component misalignment, component orientation, component error, and component weld. The research was conducted in three directions: image acquisition, image filtering/recognition and software development. Image acquisition studied the process of forming and digitizing images and some fundamental aspects regarding the human visual perception. The importance of choosing the right camera and illumination system for a certain type of problem has been highlighted. Probably the most important step towards image recognition is image filtering, The filters are used to correct and enhance images in order to prepare them for recognition. Convolution, histogram equalisation, filters based on Boolean mathematics, noise reduction, edge detection, geometrical filters, cross-correlation filters and image compression are some examples of the filters that have been studied and successfully implemented in the software application. The software application developed during the research is customized in order to meet the requirements of the industrial partner. The application is able to analyze pictures, perform the filtering, build libraries, process images and generate log files. It incorporates most of the filters studied and together with the illumination system and the camera it provides a fully integrated framework able to analyze defects on printed circuit boards.

Open video tools to support the production of online collaborative audiovisual projects : how to promote collective creation in e-learning

This paper analyses the use of open video editing tools to support the creation and production of online collaborative audiovisual projects for higher education. It focuses on the possibilities offered by these tools to promote collective creation in virtual environments.

A clinical pattern-based etiological diagnostic strategy for sensory neuronopathies: a French collaborative study.

Sensory neuronopathies (SNNs) encompass paraneoplastic, infectious, dysimmune, toxic, inherited, and idiopathic disorders. Recently described diagnostic criteria allow SNN to be differentiated from other forms of sensory neuropathy, but there is no validated strategy based on routine clinical investigations for the etiological diagnosis of SNN. In a multicenter study, the clinical, biological, and electrophysiological characteristics of 148 patients with SNN were analyzed. Multiple correspondence analysis and logistic regression were used to identify patterns differentiating between forms of SNNs with different etiologies. Models were constructed using a study population of 88 patients and checked using a test population of 60 cases. Four patterns were identified. Pattern A, with an acute or subacute onset in the four limbs or arms, early pain, and frequently affecting males over 60 years of age, identified mainly paraneoplastic, toxic, and infectious SNN. Pattern B identified patients with progressive SNN and was divided into patterns C and D, the former corresponding to patients with inherited or slowly progressive idiopathic SNN with severe ataxia and electrophysiological abnormalities and the latter to patients with idiopathic, dysimmune, and sometimes paraneoplastic SNN with a more rapid course than in pattern C. The diagnostic strategy based on these patterns correctly identified 84/88 and 58/60 patients in the study and test populations, respectively.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Sleep pattern and learning in knockdown mice with reduced cholinergic neurotransmission

Impaired cholinergic neurotransmission can affect memory formation and influence sleep-wake cycles (SWC). In the present study, we describe the SWC in mice with a deficient vesicular acetylcholine transporter (VAChT) system, previously characterized as presenting reduced acetylcholine release and cognitive and behavioral dysfunctions. Continuous, chronic ECoG and EMG recordings were used to evaluate the SWC pattern during light and dark phases in VAChT knockdown heterozygous (VAChT-KDHET, n=7) and wild-type (WT, n=7) mice. SWC were evaluated for sleep efficiency, total amount and mean duration of slow-wave, intermediate and paradoxical sleep, as well as the number of awakenings from sleep. After recording SWC, contextual fear-conditioning tests were used as an acetylcholine-dependent learning paradigm. The results showed that sleep efficiency in VAChT-KDHET animals was similar to that of WT mice, but that the SWC was more fragmented. Fragmentation was characterized by an increase in the number of awakenings, mainly during intermediate sleep. VAChT-KDHET animals performed poorly in the contextual fear-conditioning paradigm (mean freezing time: 34.4±3.1 and 44.5±3.3 s for WT and VAChT-KDHET animals, respectively), which was followed by a 45% reduction in the number of paradoxical sleep episodes after the training session. Taken together, the results show that reduced cholinergic transmission led to sleep fragmentation and learning impairment. We discuss the results on the basis of cholinergic plasticity and its relevance to sleep homeostasis. We suggest that VAChT-KDHET mice could be a useful model to test cholinergic drugs used to treat sleep dysfunction in neurodegenerative disorders.

Collaborative reflection : supporting one practitioner's development of online learning communities /

This is a study of one participant's reflective practice as she worked to develop online communities in a face-to-face science course. Her process of reflective practice was examined in order to address factors that influenced her learning path, and the benefits and challenges of collaborative action research. These research goals were pursued using a collaborative action research methodology, initially chosen for its close match with Schon's (1983) model of reflective practice. The participant's learning fit vnth Mezirow's (1991) model of transformative learning. She began with beliefs that matched her goals, and she demonstrated significant learning in three areas. First, she demonstrated instrumental learning around the constraints of workload and time, and achieving online learning community indicators. Second, she demonstrated communicative learning that helped her to see her own needs for feedback and communication more clearly, and how other process partners had been a support to her. Third, her emancipatory learning saw her revisiting and questioning her goals. It was through the reflective conversation during the planned meetings and the researcher's reframing and interrogation of that reflection that the participant was able to clarify and extend her thinking, and in so doing, critically reflect on her practice as she worked to develop online learning communities. In this way, the collaborative action research methodology was an embodiment of co-constructivism through collaborative reflective practice. Schon's (1983) model of reflective practice positions a lone practitioners moving through cycles ofplan-act-observe-reflect. The results fi"om this study suggest that collaboration is an important piece of the reflective practice model.

Didactics–promoting collaborative work and personal learning environment. Critical, creative and autonomous roles in a competitive society

School has evolved from a place where knowledge is provided to a place where learners are helped to develop their professional and social skills. Consequently, education must evolve through big challenges in order to face the changes of society in the XXIst century

Evidence for a reversal with age in the pattern of near-wall blood flow around aortic branches

The changes that occur with age in the distribution of atherosclerotic lesions around arterial branch points challenge accepted theories relating disease to haemodynamic stresses. We investigated whether flow near branch points changes with age in a way that can account for the different lesion distributions. Flow around 20 branches from immature and mature aortas was investigated by examining the length:width ratio and orientation of endothelial nuclei; these properties depend on the magnitude and direction of near-wall flows, respectively. There were significant changes in the pattern of nuclear shape with age, consistent with a reversal in the pattern of shear around branches. In control regions away from branches, there were no such changes. The role of haemodynamic stresses in atherogenesis may require re-evaluation in the light of these results. (C) 2003 Elsevier Ireland Ltd. All rights reserved.

Evidence for a reversal with age in the pattern of near-wall blood flow around aortic branches

The changes that occur with age in the distribution of atherosclerotic lesions around arterial branch points challenge accepted theories relating disease to haemodynamic stresses. We investigated whether flow near branch points changes with age in a way that can account for the different lesion distributions. Flow around 20 branches from immature and mature aortas was investigated by examining the length:width ratio and orientation of endothelial nuclei; these properties depend on the magnitude and direction of near-wall flows, respectively. There were significant changes in the pattern of nuclear shape with age, consistent with a reversal in the pattern of shear around branches. In control regions away from branches, there were no such changes. The role of haemodynamic stresses in atherogenesis may require re-evaluation in the light of these results.