Noninvasive evaluation of nicotinic acetylcholine receptor availability in mouse brain using single-photon emission computed tomography with [(123)I]5IA.

INTRODUCTION: Nicotinic acetylcholine receptors (nAChRs) are of great interest because they are implicated in higher brain functions. Nuclear medical imaging is one of the useful techniques for noninvasive evaluation of physiological and pathological function in living subjects. Recent progress in nuclear medical imaging modalities enables the clear visualization of the organs of small rodents. Thus, translational research using nuclear medical imaging in transgenic mice has become possible and helps to elucidate human disease pathology. However, imaging of α4β2 nAChRs in the mouse brain has not yet been performed. The purpose of this study was to assess the feasibility of single-photon emission computed tomography (SPECT) with 5-[(123)I]iodo-3-[2(S)-azetidinylmethoxy]pyridine ([(123)I]5IA) for evaluating α4β2 nAChR availability in the mouse brain. METHODS: A 60-min dynamic SPECT imaging session of α4β2 nAChRs in the mouse brain was performed. The regional distribution of radioactivity in the SPECT images was compared to the density of α4β2 nAChRs measured in an identical mouse. Alteration of nAChR density in the brains of Tg2576 mice was also evaluated. RESULTS: The mouse brain was clearly visualized by [(123)I]5IA-SPECT and probe accumulation was significantly inhibited by pretreatment with (-)-nicotine. The regional distribution of radioactivity in SPECT images showed a significant positive correlation with α4β2 nAChR density measured in an identical mouse brain. Moreover, [(123)I]5IA-SPECT was able to detect the up-regulation of α4β2 nAChRs in the brains of Tg2576 transgenic mice. CONCLUSIONS: [(123)I]5IA-SPECT imaging would be a promising tool for evaluating α4β2 nAChR availability in the mouse brain and may be useful in translational research focused on nAChR-related diseases

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Health Figures: An Open Source JavaScript Library for Health Data Visualization

The way we look at data has a great impact on how we can understand it, particularly when the data is related to health and wellness. Due to the increased use of self-tracking devices and the ongoing shift towards preventive medicine, better understanding of our health data is an important part of improving the general welfare of the citizens. Electronic Health Records, self-tracking devices and mobile applications provide a rich variety of data but it often becomes difficult to understand. We implemented the hFigures library inspired on the hGraph visualization with additional improvements. The purpose of the library is to provide a visual representation of the evolution of health measurements in a complete and useful manner. We researched the usefulness and usability of the library by building an application for health data visualization in a health coaching program. We performed a user evaluation with Heuristic Evaluation, Controlled User Testing and Usability Questionnaires. In the Heuristics Evaluation the average response was 6.3 out of 7 points and the Cognitive Walkthrough done by usability experts indicated no design or mismatch errors. In the CSUQ usability test the system obtained an average score of 6.13 out of 7, and in the ASQ usability test the overall satisfaction score was 6.64 out of 7. We developed hFigures, an open source library for visualizing a complete, accurate and normalized graphical representation of health data. The idea is based on the concept of the hGraph but it provides additional key features, including a comparison of multiple health measurements over time. We conducted a usability evaluation of the library as a key component of an application for health and wellness monitoring. The results indicate that the data visualization library was helpful in assisting users in understanding health data and its evolution over time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016

2D vs. 3D pain visualization: User preferences in a spinal cord injury cohort

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 Springer VerlagResearch on pain experienced after Spinal Cord Injury (SCI) has revealed that not only are there several types of pain present in the same individual with this kind of trauma, but also that people who suffer such an injury can describe the characteristics of the same type of pain in different ways. Making it possible, therefore, to more precisely describe pain experience could prove to be vital for an increased quality of life. Accordingly, fifteen individuals with pain after SCI were asked to describe their pain experience using a 3 Dimensional (3D) model of the human body that could be used as an aid in communicating their pain. The results of this study suggest that the consensus of the participants approved the ability of the 3D model to more accurately describe their pain, an encouraging outcome towards the use of 3D technology in support of post SCI pain rehabilitation