Identifying potential gene biomarkers for Parkinson's disease through an information entropy based approach.

Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease and represents the most common disease of this type, after Alzheimer's dementia. It is characterized by motor and nonmotor features and by a long prodromal stage that lasts many years. Genetic research has shown that PD is a complex and multisystem disorder. To capture the molecular complexity of this disease we used a complex network approach. We maximized the information entropy of the gene co-expression matrix betweenness to obtain a gene adjacency matrix; then we used a fast greedy algorithm to detect communities. Finally we applied principal component analysis on the detected gene communities, with the ultimate purpose of discriminating between PD patients and healthy controls by means of a random forests classifier. We used a publicly available substantia nigra microarray dataset, GSE20163, from NCBI GEO database, containing gene expression profiles for 10 PD patients and 18 normal controls. With this methodology we identified two gene communities that discriminated between the two groups with mean accuracy of 0.88 ± 0.03 and 0.84 ± 0.03, respectively, and validated our results on an independent microarray experiment. The two gene communities presented a considerable reduction in size, over 100 times, compared to the initial network and were stable within a range of tested parameters. Further research focusing on the restricted number of genes belonging to the selected communities may reveal essential mechanisms responsible for PD at a network level and could contribute to the discovery of new biomarkers for PD.

Time Dependence of the Electron and Positron Components of the Cosmic Radiation Measured by the PAMELA Experiment between July 2006 and December 2015.

Cosmic-ray electrons and positrons are a unique probe of the propagation of cosmic rays as well as of the nature and distribution of particle sources in our Galaxy. Recent measurements of these particles are challenging our basic understanding of the mechanisms of production, acceleration, and propagation of cosmic rays. Particularly striking are the differences between the low energy results collected by the space-borne PAMELA and AMS-02 experiments and older measurements pointing to sign-charge dependence of the solar modulation of cosmic-ray spectra. The PAMELA experiment has been measuring the time variation of the positron and electron intensity at Earth from July 2006 to December 2015 covering the period for the minimum of solar cycle 23 (2006-2009) until the middle of the maximum of solar cycle 24, through the polarity reversal of the heliospheric magnetic field which took place between 2013 and 2014. The positron to electron ratio measured in this time period clearly shows a sign-charge dependence of the solar modulation introduced by particle drifts. These results provide the first clear and continuous observation of how drift effects on solar modulation have unfolded with time from solar minimum to solar maximum and their dependence on the particle rigidity and the cyclic polarity of the solar magnetic field.

New upper limit on strange quark matter abundance in cosmic rays with the PAMELA space experiment.

In this work we present results of a direct search for strange quark matter (SQM) in cosmic rays with the PAMELA space spectrometer. If this state of matter exists it may be present in cosmic rays as particles, called strangelets, having a high density and an anomalously high mass-to-charge (A/Z) ratio. A direct search in space is complementary to those from ground-based spectrometers. Furthermore, it has the advantage of being potentially capable of directly identifying these particles, without any assumption on their interaction model with Earth's atmosphere and the long-term stability in terrestrial and lunar rocks. In the rigidity range from 1.0 to ∼1.0×10^{3} GV, no such particles were found in the data collected by PAMELA between 2006 and 2009. An upper limit on the strangelet flux in cosmic rays was therefore set for particles with charge 1≤Z≤8 and mass 4≤A≤1.2×10^{5}. This limit as a function of mass and as a function of magnetic rigidity allows us to constrain models of SQM production and propagation in the Galaxy.

An anomalous positron abundance in cosmic rays with energies 1.5-100 GeV.

Antiparticles account for a small fraction of cosmic rays and are known to be produced in interactions between cosmic-ray nuclei and atoms in the interstellar medium, which is referred to as a 'secondary source'. Positrons might also originate in objects such as pulsars and microquasars or through dark matter annihilation, which would be 'primary sources'. Previous statistically limited measurements of the ratio of positron and electron fluxes have been interpreted as evidence for a primary source for the positrons, as has an increase in the total electron+positron flux at energies between 300 and 600 GeV (ref. 8). Here we report a measurement of the positron fraction in the energy range 1.5-100 GeV. We find that the positron fraction increases sharply over much of that range, in a way that appears to be completely inconsistent with secondary sources. We therefore conclude that a primary source, be it an astrophysical object or dark matter annihilation, is necessary.

Cosmic-ray positron energy spectrum measured by PAMELA.

Precision measurements of the positron component in the cosmic radiation provide important information about the propagation of cosmic rays and the nature of particle sources in our Galaxy. The satellite-borne experiment PAMELA has been used to make a new measurement of the cosmic-ray positron flux and fraction that extends previously published measurements up to 300 GeV in kinetic energy. The combined measurements of the cosmic-ray positron energy spectrum and fraction provide a unique tool to constrain interpretation models. During the recent solar minimum activity period from July 2006 to December 2009, approximately 24,500 positrons were observed. The results cannot be easily reconciled with purely secondary production, and additional sources of either astrophysical or exotic origin may be required.

Imaging the ultrasmall-angle x-ray scattering distribution with grating interferometry.

X-ray imaging with grating interferometry has previously been regarded as a technique providing information only in direct space. It delivers absorption, phase, and dark-field contrast, which can be viewed as parameters of the underlying but unresolved scattering distribution. Here, we present a method that provides the ultrasmall-angle x-ray scattering distribution and, thus, allows simultaneous access to direct and reciprocal space information.

Cosmic-ray electron flux measured by the PAMELA experiment between 1 and 625 GeV.

Precision measurements of the electron component in the cosmic radiation provide important information about the origin and propagation of cosmic rays in the Galaxy. Here we present new results regarding negatively charged electrons between 1 and 625 GeV performed by the satellite-borne experiment PAMELA. This is the first time that cosmic-ray e⁻ have been identified above 50 GeV. The electron spectrum can be described with a single power-law energy dependence with spectral index -3.18 ± 0.05 above the energy region influenced by the solar wind (> 30 GeV). No significant spectral features are observed and the data can be interpreted in terms of conventional diffusive propagation models. However, the data are also consistent with models including new cosmic-ray sources that could explain the rise in the positron fraction.

Management of corrosive injuries of the upper gastrointestinal tract. Our experience in 58 patients.

BACKGROUND: The ingestion of caustic substances is one of the most difficult conditions to be treated in Emergency Department. PATIENTS AND METHODS: The medical records of patients with caustic ingestion and hospitalized from 2003 to 2008 at the Division of General Emergency Surgery with Polyspecialistic Observation of AORN "A. Cardarelli "in Naples, have been revalued. RESULTS: From 2003 to 2008, 58 patients with caustic ingestion were admitted to our Division. Ten of these patients (17.24%) underwent surgery. Six patients underwent oesophageal and gastric resection with cervical esophagostomy and alimentary digiunostomy in emergency; two underwent exploratory laparotomy, two had gastroenteroanastomosis for antropyloric stenosis. One patient underwent new operation for a complication. In total, three reconstructions of oesophagus with colon were performed . Of the six patients undergoing esofagogastrectomy, two died in the first postoperative day, but four have passed the acute phase. CONCLUSIONS: There is no universally accepted diagnostic and therapeutic procedure for the management of these patients, who are often left - as it appears in literature - to the personal experience of the surgeon who is dealing with this situation.

PAMELA results on the cosmic-ray antiproton flux from 60 MeV to 180 GeV in kinetic energy.

The satellite-borne experiment PAMELA has been used to make a new measurement of the cosmic-ray antiproton flux and the antiproton-to-proton flux ratio which extends previously published measurements down to 60 MeV and up to 180 GeV in kinetic energy. During 850 days of data acquisition approximately 1500 antiprotons were observed. The measurements are consistent with purely secondary production of antiprotons in the Galaxy. More precise secondary production models are required for a complete interpretation of the results.

Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system.

BACKGROUND: Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. METHODS: In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. RESULTS: The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78% with 2.87 false positives per image. CONCLUSIONS: Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.

[Jejunal perforation from metastatic choriocarcinoma. Case report and review of the literature]. / Perforazione digiunale da coriocarcinoma metastatico. Caso clinico e revisione della letteratura.

Choriocarcinoma is a rare malignant genital tract tumor, arising in the uterus or in the testis. Primary or metastatic choriocarcinomas of the gastrointestinal tract are infrequent. We report a case of a testis choriocarcinoma presenting as jejunal metastasis with perforation. Histology revealed the origin of this metastatic tumor, allowing us to recognize the primary neoplasm of the testis. A review of literature with PubMed since 1964 and of the references of the papers retrieved was performed. Since 1933 only 30 cases of jejunal choriocarcinomas have been described in literature. In 13 cases jejunal choriocarcinoma presented gastrointestinal hemorrhage, in 4 intestinal intussusception and in 1 case upper abdominal pain and vomiting. Only 5 cases of jejunal perforation have been described. The case presented is the first in literature of jejunal perforation from a metastatic choriocarcinoma of the testis.

Trial latencies estimation of event-related potentials in EEG by means of genetic algorithms.

OBJECTIVE: Event-related potentials (ERPs) are usually obtained by averaging thus neglecting the trial-to-trial latency variability in cognitive electroencephalography (EEG) responses. As a consequence the shape and the peak amplitude of the averaged ERP are smeared and reduced, respectively, when the single-trial latencies show a relevant variability. To date, the majority of the methodologies for single-trial latencies inference are iterative schemes providing suboptimal solutions, the most commonly used being the Woody's algorithm. APPROACH: In this study, a global approach is developed by introducing a fitness function whose global maximum corresponds to the set of latencies which renders the trial signals most aligned as possible. A suitable genetic algorithm has been implemented to solve the optimization problem, characterized by new genetic operators tailored to the present problem. MAIN RESULTS: The results, on simulated trials, showed that the proposed algorithm performs better than Woody's algorithm in all conditions, at the cost of an increased computational complexity (justified by the improved quality of the solution). Application of the proposed approach on real data trials, resulted in an increased correlation between latencies and reaction times w.r.t. the output from RIDE method. SIGNIFICANCE: The above mentioned results on simulated and real data indicate that the proposed method, providing a better estimate of single-trial latencies, will open the way to more accurate study of neural responses as well as to the issue of relating the variability of latencies to the proper cognitive and behavioural correlates.

A Gradient-Based Approach for Breast DCE-MRI Analysis.

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model.

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

[Eosinophilic colitis: an unusual cause of acute abdomen. Case report and review of literature]. / Colite eosinofila: una causa rara di addome acuto. Presentazione di un caso e revisione della letteratura.

The Authors report a case of acute abdomen caused by a colic stenosis due to eosinophilic gastroenteritis. It is a not frequent disease, often clinically unclear: in the medical literature the Authors have found only 346 cases in publications concerning the period January 1990 - December 2005. The clinical presentation is very changeable, related to the involved site of alimentary tract and to the level of eosinophilic infiltration of the wall. There are no diagnosis criteria universally acknowledged and the most efficient procedure is thought being the intestinal biopsy which enables showing the infiltration of the digestive wall. The cure is above all medical with administration of corticosteroid: surgery surely has a less important role, reserved to the cases with acute onset.

A completely automated CAD system for mass detection in a large mammographic database.

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

Hybrid x-space: a new approach for MPI reconstruction.

Magnetic particle imaging (MPI) is a new medical imaging technique capable of recovering the distribution of superparamagnetic particles from their measured induced signals. In literature there are two main MPI reconstruction techniques: measurement-based (MB) and x-space (XS). The MB method is expensive because it requires a long calibration procedure as well as a reconstruction phase that can be numerically costly. On the other side, the XS method is simpler than MB but the exact knowledge of the field free point (FFP) motion is essential for its implementation. Our simulation work focuses on the implementation of a new approach for MPI reconstruction: it is called hybrid x-space (HXS), representing a combination of the previous methods. Specifically, our approach is based on XS reconstruction because it requires the knowledge of the FFP position and velocity at each time instant. The difference with respect to the original XS formulation is how the FFP velocity is computed: we estimate it from the experimental measurements of the calibration scans, typical of the MB approach. Moreover, a compressive sensing technique is applied in order to reduce the calibration time, setting a fewer number of sampling positions. Simulations highlight that HXS and XS methods give similar results. Furthermore, an appropriate use of compressive sensing is crucial for obtaining a good balance between time reduction and reconstructed image quality. Our proposal is suitable for open geometry configurations of human size devices, where incidental factors could make the currents, the fields and the FFP trajectory irregular.

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool.

In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. HUMAN is accurate and achieves state-of-the-art performance (Dice[Formula: see text] and Dice[Formula: see text]). It is also a robust method that remains stable when applied to the whole hippocampus or to sub-regions (patches). HUMAN also compares favorably with a basic multi-atlas approach and a benchmark segmentation tool such as FreeSurfer.

Multiple RF classifier for the hippocampus segmentation: Method and validation on EADC-ADNI Harmonized Hippocampal Protocol.

The hippocampus has a key role in a number of neurodegenerative diseases, such as Alzheimer's Disease. Here we present a novel method for the automated segmentation of the hippocampus from structural magnetic resonance images (MRI), based on a combination of multiple classifiers. The method is validated on a cohort of 50 T1 MRI scans, comprehending healthy control, mild cognitive impairment, and Alzheimer's Disease subjects. The preliminary release of the EADC-ADNI Harmonized Protocol training labels is used as gold standard. The fully automated pipeline consists of a registration using an affine transformation, the extraction of a local bounding box, and the classification of each voxel in two classes (background and hippocampus). The classification is performed slice-by-slice along each of the three orthogonal directions of the 3D-MRI using a Random Forest (RF) classifier, followed by a fusion of the three full segmentations. Dice coefficients obtained by multiple RF (0.87 ± 0.03) are larger than those obtained by a single monolithic RF applied to the entire bounding box, and are comparable to state-of-the-art. A test on an external cohort of 50 T1 MRI scans shows that the presented method is robust and reliable. Additionally, a comparison of local changes in the morphology of the hippocampi between the three subject groups is performed. Our work showed that a multiple classification approach can be implemented for the segmentation for the measurement of volume and shape changes of the hippocampus with diagnostic purposes.

Detection of subclinical brain electrical activity changes in Huntington's disease using artificial neural networks.

OBJECTIVE: The aim of this study was to analyze EEG background activity in Huntington's disease (HD) patients and relatives at risk, in relation to CAG repeat size and clinical state, in order to detect an electrophysiological marker of early disease. METHODS: We selected 13 patients and 7 subjects at risk. Thirteen normal subjects, sex- and age-matched, were also evaluated. Artifact-free epochs were selected and analyzed through Fast-Fourier Transform. EEG background activity was tested using both linear analysis and artificial neural network (ANN) classifier in order to evaluate whether EEG abnormalities were linked to functional changes preceding the onset of the disease. RESULTS: The most important EEG classification pattern was the absolute alpha power not correlated with cognitive decline. The ANN correctly classified 11/13 patients and 12/13 normals. Moreover, the neural scores for subjects at risk seemed to be correlated to the expected time before the onset of the disease. CONCLUSIONS: ANN is a very powerful method to discriminate between normals and patients. It could be used as an automatic diagnostic tool. EEG changes in positive gene-carriers for HD confirm an early functional impairment which should be taken into account in the genetic counseling and in the management of the early stages of the disease.