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SARS-CoV-2 cellular infection is mediated by the heavily glycosylated spike protein. Recombinant versions of the spike protein and the receptor-binding domain (RBD) are necessary for seropositivity assays and can potentially serve as vaccines against viral infection. RBD plays key roles in the spike protein's structure and function, and thus, comprehensive characterization of recombinant RBD is critically important for biopharmaceutical applications. Liquid chromatography coupled to mass spectrometry has been widely used to characterize post-translational modifications in proteins, including glycosylation. Most studies of RBDs were performed at the proteolytic peptide (bottom-up proteomics) or released glycan level because of the technical challenges in resolving highly heterogeneous glycans at the intact protein level. Herein, we evaluated several online separation techniques: (1) C2 reverse-phase liquid chromatography (RPLC), (2) capillary zone electrophoresis (CZE), and (3) acrylamide-based monolithic hydrophilic interaction chromatography (HILIC) to separate intact recombinant RBDs with varying combinations of glycosylations (glycoforms) for top-down mass spectrometry (MS). Within the conditions we explored, the HILIC method was superior to RPLC and CZE at separating RBD glycoforms, which differ significantly in neutral glycan groups. In addition, our top-down analysis readily captured unexpected modifications (e.g., cysteinylation and N-terminal sequence variation) and low abundance, heavily glycosylated proteoforms that may be missed by using glycopeptide data alone. The HILIC top-down MS platform holds great potential in resolving heterogeneous glycoproteins for facile comparison of biosimilars in quality control applications.

The ongoing Corona Virus Disease 2019 (COVID-19) pandemic appears to increase risk for mental illness, either directly due to inflammation caused by the virus or indirectly due to related psychosocial stress, resulting in the development of both anxious-depressive and psychotic symptoms. The purpose of the present study was to assess the frequency and characteristics of all patients with First Episodes Psychosis (FEP) without COVID-19 infection hospitalized in the first four months since lockdown in Milan. We recruited sixty-two patients hospitalized between March 8 to July 8, 2020 versus those first hospitalized in the same period in 2019. The two subgroups were compared for sociodemographic variables and clinical characteristics of the episodes. Patients with FEP in 2020 were significantly older than patients with FEP in 2021, and presented with significantly less substances abuse. Interestingly, patients presenting with FEP in 2020 were significantly older than patients with FEP in 2019. These data are compatible with the greater vulnerability to stressful factors during the pandemic, as well as with the greater concern regarding a possible COVID-19 infection producing brain damage causing the FEP.

Current approaches to study glycosylation of polyclonal human immunoglobulins G (IgG) usually imply protein digestion or glycan release. While these approaches allow in-depth characterization, they also result in a loss of valuable information regarding certain subclasses, allotypes and co-occuring post-translational modifications (PTMs). Unfortunately, the high variability of polyclonal IgGs makes their intact mass spectrometry (MS) analysis extremely challenging. We propose here a middle-up strategy for the analysis of the intact fragment crystallizable (Fc) region of human plasma IgGs, with the aim of acquiring integrated information of the N-glycosylation and other PTMs of subclasses and allotypes. Human plasma IgG was isolated using Fc-specific beads followed by an on-bead CH2 domain digestion with the enzyme IdeS. The obtained mixture of Fc subunits was analyzed by capillary electrophoresis (CE) and hydrophilic interaction liquid chromatography (HILIC) hyphenated with MS. CE-MS provided separation of different IgG-subclasses and allotypes, while HILIC-MS allowed resolution of the different glycoforms and their oxidized variants. The orthogonality of these techniques was key to reliably assign Fc allotypes. Five individual donors were analyzed using this approach. Heterozygosis was observed in all the analyzed donors resulting in a total of 12 allotypes identified. The assignments were further confirmed using recombinant monoclonal IgG allotypes as standards. While the glycosylation patterns were similar within allotypes of the same subclass, clear differences were observed between IgG subclasses and donors, highlighting the relevance of the proposed approach. In a single analysis, glycosylation levels specific for each allotype, relative abundances of subclasses and information on co-occurring modifications are obtained. This middle-up method represents an important step toward a comprehensive analysis of immunoglobulin G-Fc variants.

The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting point for developing other medical imaging applications, such as a breast CAD, currently being upgraded and optimised for use in a distributed environment with grid services, in the framework of the Instituto Nazionale di Fisicia Nucleare (INFN)-funded Medical Applications on a Grid Infrastructure Connection (MAGIC)-5 project.

Table S8. Correlations between phenotypical core features and aCGH results (positive aCGH vs negative aCGH). Statistically significant results for negative aCGH are reported in bold; statistically significant results for positive aCGH are reported in bold and italic. [MCA: multiple congenital anomalies; NDD: neurodevelopmental disorders]. (DOC 49 kb)

Table S4. Correlations between clinical and phenotypic features and aCGH results (pathogenic CNVs vs likely pathogenic CNVs vs likely benign CNVs). All statistically significant features are reported in bold. From post-hoc analysis with Bonferroni correction: *Significant in the comparison of pathogenic and likely benign; §Significant in the comparison of pathogenic and likely pathogenic; ¼Significant in the comparison of likely pathogenic and likely benign. [ADHD: Attention deficit and hyperactivity disorder; ASD: atrial septal defect; CNS: central nervous system; CTG: fetal cardiotocography; IUGR: intrauterine growth restriction; PDA: patent ductus arteriosus; PFO: patent foramen ovale; ToF: Tetralogy of Fallot; VSD: ventricular septal defect]. (DOC 194 kb)

Table S3. Correlations between clinical and phenotypic features and aCGH results (pathogenic CNVs vs VOUS). Statistically significant results for pathogenic CNVs are reported in bold and significant data for VOUS are reported in italics. [ADHD: Attention deficit and hyperactivity disorder; ASD: atrial septal defect; CNS: central nervous system; CTG: fetal cardiotocography; IUGR: intrauterine growth restriction; PDA: patent ductus arteriosus; PFO: patent foramen ovale; ToF: Tetralogy of Fallot; VSD: interventricular septal defect]. (DOC 163Â kb)

Table S6. Correlations between clinical and phenotypic features and aCGH results (likely pathogenic VOUS vs likely benign VOUS + negative aCGH). Statistically significant results for likely pathogenic VOUS are reported in bold [n/N, number of cases with positive variable/number of patients with available data on that variable; NA: not applicable; ADHD: Attention deficit and hyperactivity disorder; ASD: atrial septal defect; CNS: central nervous system; CTG: fetal cardiotocography; IUGR: intrauterine growth restriction; PDA: patent ductus arteriosus; PFO: patent foramen ovale; ToF: Tetralogy of Fallot; VSD: interventricular septal defect]. (DOC 170Â kb)

Table S2 Clinical and Phenotypic features of the analyzed sample (293 patients). Total number and percentage as compared to the number of patients for which the single data was available. [ADHD: attention deficit and hyperactivity disorder, ASD: atrial septal defect, CNS: central nervous system, IUGR: intrauterine growth retardation, PDA: patent ductus arteriosus, ToF: Tetralogy of Fallot, VSD: ventral septal defect]. (DOC 90Â kb)

O estudo teve o objetivo de testar a hipótese de que o efeito alcalinizante da solução de Ringer com lactato (SRL) pode ser maior nos animais portadores de acidose metabólica do que nos sadios, como consequência da necessidade de retorno ao equilíbrio. Seis ovelhas receberam a SRL em volume correspondente a 10% do peso corporal, administrada por infusão contínua intravenosa, durante quatro horas, em duas condições definidas: enquanto eram saudáveis e após a indução experimental de acidose láctica ruminal aguda (ALRA). Amostras de sangue venoso e de urina foram colhidas em quatro momentos: antes do início da infusão, na metade do volume infundido, ao término da infusão e duas horas após. Foram determinados valores de pH sanguíneo e urinário, de pCO2, HCO3 - e BE no sangue, de Na+, K+, Cl-, SID, AG, PPT, Atot e lactato L no plasma, e das excreções fracionadas urinárias de Na+, K+, Cl- e lactato L. A SRL provocou hemodiluição, mas não interferiu nos equilíbrios eletrolítico e acidobase das ovelhas sadias. Apesar de eficaz para reverter a desidratação, não foi capaz de corrigir a acidose metabólica presente após a indução da ALRA. The aim of this study was to investigate if the alkalinizing effect of lactated Ringer's solution (LRS) is greater in animals with metabolic acidosis than in healthy ones, as a result of the need to restore acid-base balance. LRS was intravenously infused in a volume corresponding to 10% of body weight, continously during four hours, in two definite conditions in the same six ewes: when they were healthy and after experimentally induced acute rumen lactic acidosis (ARLA). Venous blood and urine samples were taken in four moments: before the beginning, in the middle, at the end of the infusion and two hours after. Blood and urine pH, blood pCO2, HCO3 - and BE, and plasma Na+, K+, Cl-, TP and L lactate were measured. Na+, K+, Cl- and L lactate fractional clearance and plasma SID, AG, and Atot were calculated. LRS caused hemodilution but didn't change electrolyte and acid-base balances in healthy ewes. When ewes were affected by ARLA, the infusion of this solution was effective for dehydration reversion but was unable to correct metabolic acidosis.

The use of automatic systems in the analysis of medical images has proven to be very useful to radiologists, especially in the framework of screening programs, in which radiologists make their first diagnosis on the basis of images only, most of those corresponding to healthy patients, and have to distinguish pathological findings from non-pathological ones at an early stage. In particular, we are developing preprocessing methods to be applied for pulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT (computed tomography) images.

To compare the effectiveness of customized vs population-based growth charts for the prediction of adverse pregnancy outcomes.MEDLINE, ClinicalTrials.gov and The Cochrane Library were searched up to 31 May 2016 to identify interventional and observational studies comparing adverse outcomes among large- (LGA) and small- (SGA) for-gestational-age neonates, when classified according to customized vs population-based growth charts. Perinatal mortality and admission to the neonatal intensive care unit (NICU) of both SGA and LGA neonates, intrauterine fetal demise (IUFD) and neonatal mortality of SGA neonates, and neonatal shoulder dystocia and hypoglycemia as well as maternal third- and fourth-degree perineal lacerations in LGA pregnancies were evaluated.The electronic search identified 237 records that were examined based on title and abstract, of which 27 full-text articles were examined for eligibility. After excluding seven articles, 20 observational studies were included in a Bayesian meta-analysis. Neonates classified as SGA according to customized growth charts had higher risks of IUFD (odds ratio (OR), 7.8 (95% CI, 4.2-12.3)), neonatal death (OR, 3.5 (95% CI, 1.1-8.0)), perinatal death (OR, 5.8 (95% CI, 3.8-7.8)) and NICU admission (OR, 3.6 (95% CI, 2.0-5.5)) than did non-SGA cases. Neonates classified as SGA according to population-based growth charts also had increased risk for adverse outcomes, albeit the point estimates of the pooled ORs were smaller: IUFD (OR, 3.3 (95% CI, 1.9-5.0)), neonatal death (OR, 2.9 (95% CI, 1.2-4.5)), perinatal death (OR, 4.0 (95% CI, 2.8-5.1)) and NICU admission (OR, 2.4 (95% CI, 1.7-3.2)). For LGA vs non-LGA, there were no differences in pooled ORs for perinatal death, NICU admission, hypoglycemia and maternal third- and fourth-degree perineal lacerations when classified according to either the customized or the population-based approach. In contrast, both approaches indicated that LGA neonates are at increased risk for shoulder dystocia than are non-LGA ones (OR, 7.4 (95% CI, 4.9-9.8) using customized charts; OR, 8.0 (95% CI, 5.3-10.1) using population-based charts).Both customized and population-based growth charts can identify SGA neonates at risk for adverse outcomes. Although the point estimates of the pooled ORs may differ for some outcomes, the overlapping CIs and lack of direct comparisons prevent conclusions from being drawn on the superiority of one method. Future clinical trials should compare directly the two approaches in the management of fetuses of abnormal size. Copyright © 2016 ISUOG. Published by John WileySons Ltd.

This work is a part of the MAGIC-5 (Medical Applications on a Grid Infrastructure Connection) experiment of the Italian INFN (Istituto Nazionale di Fisica Nucleare). A simple CAD (Computer-Assisted Detection) system for juxta-pleural lung nodules in CT images is presented, with the purpose of comparing different 2D concavity-patching techniques and assessing the respective efficiency in locating nodules. After a short introduction on the motivation, and a review of some CAD systems for lung nodules already published by the MAGIC-5 Collaboration, the paper describes the main lines of this particular approach, giving preliminary results and comments. In our procedure, candidate nodules are identified by patching lung border concavities in a hierarchical multiscale framework. Once located, they are fed to an artificial neural network for false positive reduction. The system has a modular structure that easily allows the insertion of arbitrary border-smoothing functions for concavity detection and nodule searching. In this paper the α-hull and morphological closing are compared, proving the higher sensitivity of the former, which also appears computationally less heavy. © 2010 Elsevier B.V. All rights reserved.

Contains fulltext : 89675.pdf (Publisher’s version ) (Open Access) Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements. 01 december 2010

Multislice computed tomography (MSCT) is a valuable tool for lung cancer detection, thanks to its ability to identify noncalcified nodules of small size (from about 3 mm). Due to the large number of images generated by MSCT, there is much interest in developing computer-aided detection (CAD) systems that could assist radiologists in the lung nodule detection task. A complete multistage CAD system, including lung boundary segmentation, regions of interest (ROIs) selection, feature extraction, and false positive reduction is presented. The selection of ROIs is based on a multithreshold surface-triangulation approach. Surface triangulation is performed at different threshold values, varying from a minimum to a maximum value in a wide range. At a given threshold value, a ROI is defined as the volume inside a connected component of the triangulated isosurface. The evolution of a ROI as a function of the threshold can be represented by a treelike structure. A multithreshold ROI is defined as a path on this tree, which starts from a terminal ROI and ends on the root ROI. For each ROI, the volume, surface area, roundness, density, and moments of inertia are computed as functions of the threshold and used as input to a classification system based on artificial neural networks. The method is suitable to detect different types of nodules, including juxta-pleural nodules and nodules connected to blood vessels. A training set of 109 low-dose MSCT scans made available by the Pisa center of the Italung-CT trial and annotated by expert radiologists was used for the algorithm design and optimization. The system performance was tested on an independent set of 23 low-dose MSCT scans coming from the Pisa Italung-CT center and on 83 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. On the Italung-CT test set, for nodules having a diameter greater than or equal to 3 mm, the system achieved 84% and 71% sensitivity at false positive/scan rates of 10 and 4, respectively. For nodules having a diameter greater than or equal to 4 mm, the sensitivities were 97% and 80% at false positive/scan rates of 10 and 4, respectively. On the LIDC data set, the system achieved a 79% sensitivity at a false positive/scan rate of 4 in the detection of nodules with a diameter greater than or equal to 3 mm that have been annotated by all four radiologists.

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),more » 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 A{sub z}=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.« less

The purpose of the work here presented consists in the evaluation of the performance of CAD (Computer Aided Detection) systems for automated lung nodule identification on multislice CT examinations based on different analysis approaches and on their combination. Three different CADe systems, the CAMCAD (Channeler Ant Model), the RGVPCAD (Region Growing Volume Plateau) and the VBNACAD (Voxel Based Neural Approach) were tested on public research datasets and evaluated in terms of FROC (Free-response Receiver Operating Characteristics) curves both individually and combined.

Objectives—To determine the incidence of catheter related thrombosis and to test the eYcacy of recombinant tissue type plasminogen activator (rt-PA) in preterm infants. Study design—From January 1995 to December 1998, echocardiography was performed in the first few days of life in 76 very low birthweight (< 1500 g) infants out of a total of 147 having an umbilical catheter placed. When intracardiac thrombosis was diagnosed, rt-PA infusion was performed. Results—Four infants (5%) developed an intracardiac thrombosis during the first few days of life. In three of them, rt-PA at a dose of 0.4‐0.5 mg/kg in a 20‐30 minute bolus led to dissolution of the clot. One patient received a three hour infusion after the bolus, at a dose of 0.1 mg/kg/h, with resolution of the thrombus. No systemic eVects were observed after rt-PA infusion. Conclusions—Early thrombosis may occur as a complication of umbilical catheterisation in preterm infants; early echocardiographic detection of this disorder allows complete, safe, and rapid lysis with rt-PA. (Arch Dis Child Fetal Neonatal Ed 2001;85:F66‐F72)

A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.

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. © 2007 American Association of Physicists in Medicine.

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.

Using a new immunoradiometric assay (ELSA pS2 Cis-France), a total of 200 cytosols obtained from primary breast tumors were examined for pS2 content, which is an estrogen-regulated protein actually studied as a marker of hormone sensitivity and favorable prognostic factor in breast cancer. In our patient group, the median pS2 value corresponding to 5.3 ng/mg of cytosolic proteins was used as cutoff. pS2 content was not related to menopause status, tumor size, or nodal involvement, whereas a positive correlation was found between pS2 and ER/PgR status. Moreover, the association of pS2 with steroid receptors seems to identify subgroups of patients better than ER/PgR alone.

The aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans.The outputs of three different CADe systems developed by researchers of the Italian MAGIC-5 collaboration were combined. The systems are: the CAMCADe (based on a Channeler-Ant-Model which segments vessel tree and nodule candidates and a neural classifier), the RGVPCADe (a Region-Growing- Volume-Plateau algorithm detects nodule candidates and a neural network reduces false positives); the VBNACADe (two dedicated procedures, based respectively on a 3D dot-enhancement algorithm and on intersections of pleura surface normals, identifies internal and juxtapleural nodules, and a Voxel-Based-Neural-Approach reduces false positives. A dedicated OsiriX plugin implemented with the Cocoa environments of MacOSX allows annotating nodules and visualizing singles and combined CADe findings.The combined CADe has been tested on thin slice (lower than 2 mm) CTs of the LIDC public research database and the results have been compared with those obtained by the single systems. The FROC (Free Receiver Operating Characteristic) curves show better results than the best of the single approaches.Has been demonstrated that the combination of different approaches offers better results than each single CADe system. A clinical validation of the combined CADe as second reader is being addressed by means of the dedicated OsiriX plugin.

Background Matrix metalloproteinases (MMPs) are well-known biological targets implicated in tumour progression, homeostatic regulation, innate immunity, impaired delivery of pro-apoptotic ligands, and the release and cleavage of cell-surface receptors. With this in mind, the perception of the intimate relationships among diverse MMPs could be a solid basis for accelerated learning in designing new selective MMP inhibitors. In this regard, decrypting the latent molecular reasons in order to elucidate similarity among MMPs is a key challenge. Results We describe a pairwise variant of the non-parametric chaotic map clustering (CMC) algorithm and its application to 104 X-ray MMP structures. In this analysis electrostatic potentials are computed and used as input for the CMC algorithm. It was shown that differences between proteins reflect genuine variation of their electrostatic potentials. In addition, the analysis has been also extended to analyze the protein primary structures and the molecular shapes of the MMP co-crystallised ligands. Conclusions The CMC algorithm was shown to be a valuable tool in knowledge acquisition and transfer from MMP structures. Based on the variation of electrostatic potentials, CMC was successful in analysing the MMP target family landscape and different subsites. The first investigation resulted in rational figure interpretation of both domain organization as well as of substrate specificity classifications. The second made it possible to distinguish the MMP classes, demonstrating the high specificity of the S1' pocket, to detect both the occurrence of punctual mutations of ionisable residues and different side-chain conformations that likely account for induced-fit phenomena. In addition, CMC demonstrated a potential comparable to the most popular UPGMA (Unweighted Pair Group Method with Arithmetic mean) method that, at present, represents a standard clustering bioinformatics approach. Interestingly, CMC and UPGMA resulted in closely comparable outcomes, but often CMC produced more informative and more easy interpretable dendrograms. Finally, CMC was successful for standard pairwise analysis (i.e., Smith-Waterman algorithm) of protein sequences and was used to convincingly explain the complementarity existing between the molecular shapes of the co-crystallised ligand molecules and the accessible MMP void volumes.

A number of methods for medical image analysis will be presented and their application to real cases will be discussed. In particular, attention will be focused on computer-aided detection (CAD) systems for lung nodule diagnosis in thorax-computed tomography (CT) and breast cancer detection in mammographic images. In the first case both a region growing (RG) algorithm for lung parenchymal tissue extraction and an active contour model (ACM) for anatomic lung contour detection will be described. In the second case, we will focus on a Haralik’s textural feature extraction scheme for the characterization of the regions of interest (ROIs) of the mammogram and a supervised neural network for the classification of the ROIs.

This work focuses on the analysis of fundamental issues which have to be considered in order to obtain voice from a wireless sensor network (WSN). The latter is assumed to be constituted by MicaZ wireless sensor nodes. Problems related to the transport of an audio signal through a wireless channel and sensor nodes are thus analyzed, and a project for an audio surveillance system is presented.

3-D object segmentation is an important and challenging topic in computer vision that could be tackled with artificial life models. A Channeler Ant Model (CAM), based on the natural ant capabilities of dealing with 3-D environments through self-organization and emergent behaviours, is proposed. Ant colonies, defined in terms of moving, pheromone laying, reproduction, death and deviating behaviours rules, is able to segment artificially generated objects of different shape, intensity, background. The model depends on few parameters and provides an elegant solution for the segmentation of 3-D structures in noisy environments with unknown range of image intensities: even when there is a partial overlap between the intensity and noise range, it provides a complete segmentation with negligible contamination (i.e., fraction of segmented voxels that do not belong to the object).

Extra-adrenal pheochromocytoma, or paraganglioma, is a rare tumour arising from paraganglion chromaffin cells of the sympathetic nervous system. In adults, pheochromocytomas are often called the "10% tumor" because approximately 10% occur above the diaphragm, 10% of intraabdominal pheochromocytomas are extra-adrenal, 10% are bilateral, 10% are multiple, 10% are familial, 10% are malignant, and 10% recur postoperatively. In children, instead, this tumor occurs in ectopic sites in 30-40% of the cases. This paper reports the successful laparoscopic resection of an extra-adrenal pheochromocytoma, simulating an ovarian tumor, combined with a laparoscopic cholecystectomy for gallstones.The case of a 48-year-old woman affected by an extra-adrenal pheochromocytoma that had been unsuspected for a long time is presented. The patient had some clinical symptoms that had been taken for a climacteric syndrome given her premenopausal age. The atypical and rare location of the pheochromocytoma (parauterine) had contributed to misdiagnosing it as an ovarian tumor. Laparoscopic surgery was chosen for the removal of the tumor because it is a safe technique requiring a shorter hospital stay; a concomitant cholecystectomy was performed due to the presence of gallstones.Surgical resection is the only treatment option for extra-adrenal pheochromocytomas. With adequate preoperative adrenergic receptor blockers and vascular filling, the laparoscopic approach appears to be a valid alternative to open surgery for paragangliomas. Gynecologists should consider the possibility, although rare, of an extra-adrenal pheocromocytoma when preparing to surgically remove a pelvic mass.

In our professional activity in the field of women’s health, in a well-supplied country where the majority of women who work are educated, and act responsibly in the management of their lives, certain conditions of social equity and stability are an expected part of our daily work and are indeed taken for granted. In other parts of the world, however, where the social scene presents different requirements, the health of women and children is also different, being affected by variables that from our point of view would be considered to be extraneous to a physician’s daily task. We would like to consider here the particular features of the health of women and children. Women’s health is often identified with their reproductive capacity, while in fact this represents only one aspect of female health. In general, female reproductive health is looked upon as an asset in all cultures and in all conditions, but there is no such universal appreciation of the efforts that a society should make—even a small society like the family—in order to keep women “healthy.” The safeguard of the central role of female welfare as a requisite for the welfare of both family and children is common to all points of view. This means that women, their families, and social groups can everywhere share the value of programmes for the defence of women’s health. In the Beijing Declaration in 1995 the governments participating in the Fourth World Conference on Women declared the intention to ensure the application of the human rights of women and girls as an integral and indivisible part of human rights and of basic freedoms. This affirmation of principles led to a platform outlining strategic objectives and possible action to achieve them in a number of fields: poverty, education, health, violence against women, the economy, access to decision-making levels, and many others. In particular, in actions for women and health, it is stated that “women have the right to have the highest attainable level of physical and mental health” and that “the achievement of this right is fundamental to their life, their well-being, and their possibility of full participation in family and public life”. In national and international forums women have regularly pointed out that for the achievement of these health standards three conditions are necessary: peace, development, and equity in family and social responsibilities.

Ant Colony Models are artificial simulations of real ant colonies [1], [2]. The way real ants behave in nature inspire cooperation and competition strategies for virtual agent: the emergence of intelligent behavior and swarm-based self- organization can be used to solve difficult problems. In this work an Ant Colony Model for 3D objects reconstruction is presented. The accuracy in reconstructing 3D object is tested on artificial 3D objects and on 10 real Computed Tomography (CT) images of human lungs.

A new active contour model (ACM) algorithm for the detection of the contour of bi-dimensional regions is presented. The algorithm is based on the simulation of an elastic band glued to the contour of the region under analysis. As a result a local convex hull is obtained, where the radius of the concave regions included by the elastic band is defined by properly tuning a parameter. A dedicated application to medical images is presented. The algorithm is part of a segmentation system able to extract the lung volume from 3D CT scans. The effectiveness of the algorithm is evaluated on a database of 15 low-dose CT scans (about 320 sectional images per CT), including 26 nodules. No pathological structure is missing after the lung volume segmentation, while a reduction of the volume to analyze is obtained to about 15% of the total volume of the original CT scan, and 25% of the chest volume.

With the present paper we introduce a new Computer Assisted Detection method for Lung Cancer in CT images. The algorithm is based on several sub-modules: 3D Region Growing, Active Contour And Shape Models, Centre of Maximal Balls, but the core of our approach are Biological Models of ants known as Artificial Life models. In the first step of the algorithm images undergo a 3D region growing procedure for identifying the ribs cage; then Active Contour Models are used in order to build a confined area for the incoming ants that are deployed to make clean and accurate reconstruction of the bronchial and vascular tree, which is removed from the image just before checking for nodules.