37 research outputs found
Beam test calibration of the balloon-borne imaging calorimeter for the CREAM experiment
CREAM (Cosmic Ray Energetics And Mass) is a multi-flight balloon mission
designed to collect direct data on the elemental composition and individual
energy spectra of cosmic rays. Two instrument suites have been built to be
flown alternately on a yearly base. The tungsten/Sci-Fi imaging calorimeter for
the second flight, scheduled for December 2005, was calibrated with electron
and proton beams at CERN. A calibration procedure based on the study of the
longitudinal shower profile is described and preliminary results of the beam
test are presented.Comment: 4 pages, 4 figures. To be published in the Proceedings of 29th
International Cosmic Ray Conference (ICRC 2005), Pune, India, August 3-10,
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Effects of Aβ exposure on longterm associative memory and its neuronal mechanisms in a defined neuronal network
Amyloid beta (Aβ ) induced neuronal death has been linked to memory loss, perhaps the most devastating symptom of Alzheimer’s disease (AD). Although Aβ -induced impairment of synaptic or intrinsic plasticity is known to occur before any cell death, the links between these neurophysiological changes and the loss of specific types of behavioral memory are not fully understood. Here we used a behaviorally and physiologically tractable animal model to investigate Aβ -induced memory loss and electrophysiological changes in the absence of neuronal death in a defined network underlying associative memory. We found similar behavioral but different neurophysiological effects for Aβ 25-35 and Aβ 1-42 in the feeding circuitry of the snail Lymnaea stagnalis. Importantly, we also established that both the behavioral and neuronal effects were dependent upon the animals having been classically conditioned prior to treatment, since Aβ application before training caused neither memory impairment nor underlying neuronal changes over a comparable period of time following treatment
A novel dynamic multicellular co-culture system for studying individual blood-brain barrier cell types in brain diseases and cytotoxicity testing
Blood brain barrier (BBB) cells play key roles in the physiology and pathology of the central nervous system (CNS). BBB dysfunction is implicated in many neurodegenerative diseases, including Alzheimer’s disease (AD). The BBB consists of capillary endothelial cells, pericytes encircling the endothelium and surrounding astrocytes extending their processes towards it. Although there have been many attempts to develop in vitro BBB models, the complex interaction between these celltypes makes it extremely difficult to determine their individual contribution to neurotoxicity in vivo. Thus, we developed and optimised an in vitro multicellular co-culture model within the Kirkstall Quasi Vivo System. The main aim was to determine the optimal environment to culture human brain primary endothelial cells, pericytes and astrocytes whilst maintaining cellular communication without formation of a barrier in order to assess the contribution of each cell type to the overall response. As a proof of concept for the present system, the effects of amyloid-beta 25-35 peptide (Aβ25-35), a hall mark of AD, were explored. This multicellular system will be a valuable tool for future studies on the specific roles of individual BBB cell type (while making connection with each other through medium) in CNS disorders as well as in cytotoxicity tests
A model to explain specific cellular communications and cellular harmony:- a hypothesis of coupled cells and interactive coupling molecules
Apparent solubility and dissolution profile at non-sink conditions as predictive parameters for pharmaceutical Quality by Design.
Edge detection in Atomic Magnetometer Imaging
We present the results of an edge detection algorithm applied on Electromagnetic Induction Imaging provided by an Atomic radio-frequency Magnetometer operating in an unshielded environment and at room temperature. Atomic Magnetometers have been already used for Imaging Techniques in the last few years, but the image reconstruction and the object pattern recognition lacks nowadays in terms of quality: the effect of scattering of e.m. signals at low-frequency provides blurred images, and does not allow for a clean ray – optics response, as in the case of X rays. Our algorithm, based on solved Gaussian Noise Recognition, demonstrates excellent spatial resolution achieved despite low Signal-to-Noise-Ratio
A proof-of-principle apparatus for scattering suppression in digital mammography
A low-noise CCD coupled to a structured CsI(Tl) scintillator via a fiber optic plate was operated as X-ray detector for digital mammography. The imaging capabilities of the device were measured in terms both of spatial resolution (MTF) and of noise properties (DQE, NPS). The detector was characterized using a standard mammographic tube with and without the coupling to a pair of anti-scatter projective grids. Breast phantom images were collected and compared to a MonteCarlo simulation of the apparatus. Contrast enhancement was achieved by using the anti-scatter grids
A proof-of-principle apparatus for scattering suppression in digital mammography
A low-noise CCD coupled to a structured CsI(Tl) scintillator via a fiber optic plate was operated as X-ray detector for digital mammography. The imaging capabilities of the device were measured in terms both of spatial resolution (MTF) and of noise properties (DQE, NPS). The detector was characterized using a standard mammographic tube with and without the coupling to a pair of anti-scatter projective grids. Breast phantom images were collected and compared to a MonteCarlo simulation of the apparatus. Contrast enhancement was achieved by using the anti-scatter grids
AKUImg: A database of cartilage images of Alkaptonuria patients
ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis forpatients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. We present anApreciseKUre plugin, called AKUImg, dedicated to the storage and analysis of AKU histopathological slides,in order to create a Precision Medicine Ecosystem (PME), where images can be shared among registeredresearchers and clinicians to extend the AKU knowledge network. AKUImg includes a new set of AKU imagestaken from cartilage tissues acquired by means of a microscopic technique. The repository, in accordanceto ethical policies, is publicly available after a registration request, to give to scientists the opportunity tostudy, investigate and compare such precious resources. AKUImg is also integrated with a preliminary butaccurate predictive system able to discriminate the presence/absence of AKU by comparing histopatologicalaffected/control images. The algorithm is based on a standard image processing approach, namely histogramcomparison, resulting to be particularly effective in performing image classification, and constitutes a usefulguide for non-AKU researchers and clinician
Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease
Alkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2-dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum amyloid A (SAA) levels, which are linked with inflammation and oxidative stress and might contribute to disease progression and patients' poor quality of life. Recently, we reported that inflammatory markers (SAA and chitotriosidase) and oxidative stress markers (protein thiolation index) might be disease activity markers in AKU. Thanks to an international network, we collected genotypic, phenotypic, and clinical data from more than 200 patients with AKU. These data are currently stored in our AKU database, named ApreciseKUre. In this work, we developed an algorithm able to make predictions about the oxidative status trend of each patient with AKU based on 55 predictors, namely circulating HGA, body mass index, total cholesterol, SAA, and chitotriosidase. Our general aim is to integrate the data of apparently heterogeneous patients with AKUAKU by using specific bioinformatics tools, in order to identify pivotal mechanisms involved in AKU for a preventive, predictive, and personalized medicine approach to AKU.-Cicaloni, V., Spiga, O., Dimitri, G. M., Maiocchi, R., Millucci, L., Giustarini, D., Bernardini, G., Bernini, A., Marzocchi, B., Braconi, D., Santucci, A. Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease