Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy.Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy

Risk factors for treatment delay in pulmonary tuberculosis in Recife, Brazil

BACKGROUND: Tuberculosis is still a great challenge to public health in Brazil and worldwide. Early detection followed by effective therapy is extremely important in controlling the disease. Recent studies have investigated reasons for delays in treatment, but there is no agreed definition of what constitutes an "acceptable" delay. This study investigates factors associated with total delay in treatment of tuberculosis. METHODS: A cohort of adult cases of pulmonary tuberculosis diagnosed over a two-year period was studied. Patients were interviewed on entry, reporting the duration of symptoms before the start of treatment, and sputum and blood samples were collected. It was decided that sixty days was an acceptable total delay. Associations were investigated using univariable and multivariable analysis and the population attributable fraction was estimated. RESULTS: Of 1105 patients, 62% had a delay of longer than 60 days. Age, sex, alcoholism and difficulty of access were not associated with delays, but associations were found in the case of unemployment, having given up smoking, having lost weight and being treated in two of the six health districts. The proportion attributable to: not being an ex-smoker was 31%; unemployment, 18%; weight loss, 12%, and going to the two worst health districts, 25%. CONCLUSION: In this urban area, delays seem to be related to unemployment and general attitudes towards health. Although they reflect the way health services are organized, delays are not associated with access to care

A Novel Pathogenic Mechanism of Highly Pathogenic Avian Influenza H5N1 Viruses Involves Hemagglutinin Mediated Resistance to Serum Innate Inhibitors

In this study, the effect of innate serum inhibitors on influenza virus infection was addressed. Seasonal influenza A(H1N1) and A(H3N2), 2009 pandemic A(H1N1) (H1N1pdm) and highly pathogenic avian influenza (HPAI) A(H5N1) viruses were tested with guinea pig sera negative for antibodies against all of these viruses as evaluated by hemagglutination-inhibition and microneutralization assays. In the presence of serum inhibitors, the infection by each virus was inhibited differently as measured by the amount of viral nucleoprotein produced in Madin-Darby canine kidney cells. The serum inhibitors inhibited seasonal influenza A(H3N2) virus the most, while the effect was less in seasonal influenza A(H1N1) and H1N1pdm viruses. The suppression by serum inhibitors could be reduced by heat inactivation or treatment with receptor destroying enzyme. In contrast, all H5N1 strains tested were resistant to serum inhibitors. To determine which structure (hemagglutinin (HA) and/or neuraminidase (NA)) on the virus particles that provided the resistance, reverse genetics (rg) was applied to construct chimeric recombinant viruses from A/Puerto Rico/8/1934(H1N1) (PR8) plasmid vectors. rgPR8-H5 HA and rgPR8-H5 HANA were resistant to serum inhibitors while rgPR8-H5 NA and PR8 A(H1N1) parental viruses were sensitive, suggesting that HA of HPAI H5N1 viruses bestowed viral resistance to serum inhibition. These results suggested that the ability to resist serum inhibition might enable the viremic H5N1 viruses to disseminate to distal end organs. The present study also analyzed for correlation between susceptibility to serum inhibitors and number of glycosylation sites present on the globular heads of HA and NA. H3N2 viruses, the subtype with highest susceptibility to serum inhibitors, harbored the highest number of glycosylation sites on the HA globular head. However, this positive correlation cannot be drawn for the other influenza subtypes

Rules Governing Selective Protein Carbonylation

BACKGROUND:Carbonyl derivatives are mainly formed by direct metal-catalysed oxidation (MCO) attacks on the amino-acid side chains of proline, arginine, lysine and threonine residues. For reasons unknown, only some proteins are prone to carbonylation. METHODOLOGY/PRINCIPAL FINDINGS:we used mass spectrometry analysis to identify carbonylated sites in: BSA that had undergone in vitro MCO, and 23 carbonylated proteins in Escherichia coli. The presence of a carbonylated site rendered the neighbouring carbonylatable site more prone to carbonylation. Most carbonylated sites were present within hot spots of carbonylation. These observations led us to suggest rules for identifying sites more prone to carbonylation. We used these rules to design an in silico model (available at http://www.lcb.cnrs-mrs.fr/CSPD/), allowing an effective and accurate prediction of sites and of proteins more prone to carbonylation in the E. coli proteome. CONCLUSIONS/SIGNIFICANCE:We observed that proteins evolve to either selectively maintain or lose predicted hot spots of carbonylation depending on their biological function. As our predictive model also allows efficient detection of carbonylated proteins in Bacillus subtilis, we believe that our model may be extended to direct MCO attacks in all organisms

The Secrets of a Functional Synapse – From a Computational and Experimental Viewpoint

BACKGROUND: Neuronal communication is tightly regulated in time and in space. The neuronal transmission takes place in the nerve terminal, at a specialized structure called the synapse. Following neuronal activation, an electrical signal triggers neurotransmitter (NT) release at the active zone. The process starts by the signal reaching the synapse followed by a fusion of the synaptic vesicle and diffusion of the released NT in the synaptic cleft; the NT then binds to the appropriate receptor, and as a result, a potential change at the target cell membrane is induced. The entire process lasts for only a fraction of a millisecond. An essential property of the synapse is its capacity to undergo biochemical and morphological changes, a phenomenon that is referred to as synaptic plasticity. RESULTS: In this survey, we consider the mammalian brain synapse as our model. We take a cell biological and a molecular perspective to present fundamental properties of the synapse:(i) the accurate and efficient delivery of organelles and material to and from the synapse; (ii) the coordination of gene expression that underlies a particular NT phenotype; (iii) the induction of local protein expression in a subset of stimulated synapses. We describe the computational facet and the formulation of the problem for each of these topics. CONCLUSION: Predicting the behavior of a synapse under changing conditions must incorporate genomics and proteomics information with new approaches in computational biology

Assessment of the Antiviral Properties of Recombinant Porcine SP-D against Various Influenza A Viruses In Vitro

The emergence of influenza viruses resistant to existing classes of antiviral drugs raises concern and there is a need for novel antiviral agents that could be used therapeutically or prophylacticaly. Surfactant protein D (SP-D) belongs to the family of C-type lectins which are important effector molecules of the innate immune system with activity against bacteria and viruses, including influenza viruses. In the present study we evaluated the potential of recombinant porcine SP-D as an antiviral agent against influenza A viruses (IAVs) in vitro. To determine the range of antiviral activity, thirty IAVs of the subtypes H1N1, H3N2 and H5N1 that originated from birds, pigs and humans were selected and tested for their sensitivity to recombinant SP-D. Using these viruses it was shown by hemagglutination inhibition assay, that recombinant porcine SP-D was more potent than recombinant human SP-D and that especially higher order oligomeric forms of SP-D had the strongest antiviral activity. Porcine SP-D was active against a broad range of IAV strains and neutralized a variety of H1N1 and H3N2 IAVs, including 2009 pandemic H1N1 viruses. Using tissue sections of ferret and human trachea, we demonstrated that recombinant porcine SP-D prevented attachment of human seasonal H1N1 and H3N2 virus to receptors on epithelial cells of the upper respiratory tract. It was concluded that recombinant porcine SP-D holds promise as a novel antiviral agent against influenza and further development and evaluation in vivo seems warranted

Fragile X Related Protein 1 Clusters with Ribosomes and Messenger RNAs at a Subset of Dendritic Spines in the Mouse Hippocampus

The formation and storage of memories in neuronal networks relies on new protein synthesis, which can occur locally at synapses using translational machinery present in dendrites and at spines. These new proteins support long-lasting changes in synapse strength and size in response to high levels of synaptic activity. To ensure that proteins are made at the appropriate time and location to enable these synaptic changes, messenger RNA (mRNA) translation is tightly controlled by dendritic RNA-binding proteins. Fragile X Related Protein 1 (FXR1P) is an RNA-binding protein with high homology to Fragile X Mental Retardation Protein (FMRP) and is known to repress and activate mRNA translation in non-neuronal cells. However, unlike FMRP, very little is known about the role of FXR1P in the central nervous system. To understand if FXR1P is positioned to regulate local mRNA translation in dendrites and at synapses, we investigated the expression and targeting of FXR1P in developing hippocampal neurons in vivo and in vitro. We found that FXR1P was highly expressed during hippocampal development and co-localized with ribosomes and mRNAs in the dendrite and at a subset of spines in mouse hippocampal neurons. Our data indicate that FXR1P is properly positioned to control local protein synthesis in the dendrite and at synapses in the central nervous system

Variance in brain volume with advancing age: implications for defining the limits of normality

Background: Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.e. percentile ranks, of brain tissue volume. Since it was previously unknown, the objective of the present study was to determine if this assumption was robust, i.e. whether regression models derived from mean values accurately represented the distributions and boundaries of brain tissue volume at older ages. Materials and Methods: We acquired T1-w magnetic resonance (MR) brain images of 227 normal and 219 Alzheimer’s disease (AD) subjects (aged 55-89 years) from publicly available databanks. Using nonlinear regression within both samples, we compared mean and percentile rank estimates of whole brain tissue volume by age. Results: In both the normal and AD sample, mean regression estimates of brain tissue volume often did not accurately represent percentile rank estimates (errors=-74% to 75%). In the normal sample, mean estimates generally underestimated differences in brain volume at percentile ranks below the mean. Conversely, in the AD sample, mean estimates generally underestimated differences in brain volume at percentile ranks above the mean. Differences between ages at the 5th percentile rank of normal subjects were ~39% greater than mean differences in the AD subjects. Conclusions: While more data are required to make true population inferences, our results indicate that mean regression estimates may not accurately represent the distributions of ageing brain tissue volumes. This suggests that percentile rank estimates will be required to robustly define the limits of brain tissue volume in normal ageing and neurodegenerative disease