Diagnostic model based on Raman spectra of normal, hyperplasia and prostate adenocarcinoma tissues in vitro

This study evaluated the use of Raman spectroscopy to identify the spectral differences between normal (N), benign hyperplasia (BPH) and adenocarcinoma (CaP) in fragments of prostate biopsies in vitro with the aim of developing a spectral diagnostic model for tissue classification. A dispersive Raman spectrometer was used with 830 nm wavelength and 80 mW excitation. Following Raman data collection and tissue histopathology (48 fragments diagnosed as N, 43 as BPH and 14 as CaP), two diagnostic models were developed in order to extract diagnostic information: the first using PCA and Mahalanobis analysis techniques and the second one a simplified biochemical model based on spectral features of cholesterol, collagen, smooth muscle cell and adipocyte. Spectral differences between N, BPH and CaP tissues, were observed mainly in the Raman bands associated with proteins, lipids, nucleic and amino acids. The PCA diagnostic model showed a sensitivity and specificity of 100%, which indicates the ability of PCA and Mahalanobis distance techniques to classify tissue changes in vitro. Also, it was found that the relative amount of collagen decreased while the amount of cholesterol and adipocyte increased with severity of the disease. Smooth muscle cell increased in BPH tissue. These characteristics were used for diagnostic purposes.

Association Between Polymorphisms in GRIK2 Gene and Obsessive-Compulsive Disorder: A Family-Based Study

Several studies support a genetic influence on obsessive-compulsive disorder (OCD) etiology. The role of glutamate as an important neurotransmitter affecting OCD pathophysiology has been supported by neuroimaging, animal model, medication, and initial candidate gene studies. Genes involved in glutamatergic pathways, such as the glutamate receptor, ionotropic, kainate 2 (GRIK2), have been associated with OCD in previous studies. This study examines GRIK2 as a candidate gene for OCD susceptibility in a family-based approach. Probands had full DSM-IV diagnostic criteria for OCD. Forty-seven OCD probands and their parents were recruited from tertiary care OCD specialty clinics from France and USA. Genotypes of single nucleotide polymorphism (SNP) markers and related haplotypes were analyzed using Haploview and FBAT software. The polymorphism at rs1556995 (P = 0.0027; permuted P-value = 0.03) was significantly associated with the presence of OCD. Also, the two marker haplotype rs1556995/rs1417182, was significantly associated with OCD (P = 0.0019, permuted P-value = 0.01). This study supports previously reported findings of association between proximal GRIK2 SNPs and OCD in a comprehensive evaluation of the gene. Further study with independent samples and larger sample sizes is required.

Concurreny based transition refinement for the verification of distributed algorithms

We suggest a new notion of behaviour preserving transition refinement based on partial order semantics. This notion is called transition refinement. We introduced transition refinement for elementary (low-level) Petri Nets earlier. For modelling and verifying complex distributed algorithms, high-level (Algebraic) Petri nets are usually used. In this paper, we define transition refinement for Algebraic Petri Nets. This notion is more powerful than transition refinement for elementary Petri nets because it corresponds to the simultaneous refinement of several transitions in an elementary Petri net. Transition refinement is particularly suitable for refinement steps that increase the degree of distribution of an algorithm, e.g. when synchronous communication is replaced by asynchronous message passing. We study how to prove that a replacement of a transition is a transition refinement.

Reliability assessment of Cobb angle measurements using manual and digital methods

BACKGROUND CONTEXT: The vertebral spine angle in the frontal plane is an important parameter in the assessment of scoliosis and may be obtained from panoramic X-ray images. Technological advances have allowed for an increased use of digital X-ray images in clinical practice. PURPOSE: In this context, the objective of this study is to assess the reliability of computer-assisted Cobb angle measurements taken from digital X-ray images. STUDY DESIGN/SETTING: Clinical investigation quantifying scoliotic deformity with Cobb method to evaluate the intra- and interobserver variability using manual and digital techniques. PATIENT SAMPLE: Forty-nine patients diagnosed with idiopathic scoliosis were chosen based on convenience, without predilection for gender, age, type, location, or magnitude of the curvature. OUTCOME MEASURES: Images were examined to evaluate Cobb angle variability, end plate selection, as well as intra- and interobserver errors. METHODS: Specific software was developed to digitally reproduce the Cobb method and calculate semiautomatically the degree of scoliotic deformity. During the study, three observers estimated the Cobb angle using both the digital and the traditional manual methods. RESULTS: The results showed that Cobb angle measurements may be reproduced in the computer as reliably as with the traditional manual method, in similar conditions to those found in clinical practice. CONCLUSIONS: The computer-assisted method (digital method) is clinically advantageous and appropriate to assess the scoliotic curvature in the frontal plane using Cobb method. (C) 2010 Elsevier Inc. All rights reserved.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Evaluation of computational methods for the reconstruction of HLA haplotypes

Human leukocyte antigen (HLA) haplotypes are frequently evaluated for population history inferences and association studies. However, the available typing techniques for the main HLA loci usually do not allow the determination of the allele phase and the constitution of a haplotype, which may be obtained by a very time-consuming and expensive family-based segregation study. Without the family-based study, computational inference by probabilistic models is necessary to obtain haplotypes. Several authors have used the expectation-maximization (EM) algorithm to determine HLA haplotypes, but high levels of erroneous inferences are expected because of the genetic distance among the main HLA loci and the presence of several recombination hotspots. In order to evaluate the efficiency of computational inference methods, 763 unrelated individuals stratified into three different datasets had their haplotypes manually defined in a family-based study of HLA-A, -B, -DRB1 and -DQB1 segregation, and these haplotypes were compared with the data obtained by the following three methods: the Expectation-Maximization (EM) and Excoffier-Laval-Balding (ELB) algorithms using the arlequin 3.11 software, and the PHASE method. When comparing the methods, we observed that all algorithms showed a poor performance for haplotype reconstruction with distant loci, estimating incorrect haplotypes for 38%-57% of the samples considering all algorithms and datasets. We suggest that computational haplotype inferences involving low-resolution HLA-A, HLA-B, HLA-DRB1 and HLA-DQB1 haplotypes should be considered with caution.

Risk of Potential Drug-Drug Interactions among Brazilian Elderly A Population-Based, Cross-Sectional Study

Background: Drug-drug interactions (DDIs) are one of the main causes of adverse reactions related to medications, being responsible for up to 23% of hospital admissions. However, only a few studies have evaluated this problem in elderly Brazilians. Objectives: To determine the prevalence of potential DDIs (PDDIs) in community-dwelling elderly people in Brazil, analyse these interactions with regard to severity and clinical implications, and identify associated factors. Methods: A population-based cross-sectional study was carried out involving 2143 elderly (aged 60 years) residents of the metropolitan area of Sao Paulo, Brazil. Data were obtained from the SABE (Saude, Bem estar e Envelhecimento [Health, Well-Being, and Aging]) survey, which is a multicentre study carried out in seven countries of Latin America and the Caribbean, coordinated by the Pan-American Health Organization. PDDIs were analysed using a computerized program and categorized according to level of severity, onset, mechanism and documentation in the literature. The STATA software statistical package was used for data analysis, and logistic regression was conducted to determine whether variables were associated with PDDIs. Results: Analysis revealed that 568 (26.5%) of the elderly population included in the study were taking medications that could lead to a DDI. Almost two-thirds (64.4%) of the elderly population exposed to PDDIs were women, 50.7% were aged >= 75 years, 71.7% reported having fair or poor health and 65.8% took 2-5 medications. A total of 125 different PDDIs were identified; the treatment combination of an ACE inhibitor with a thiazide or loop diuretic (associated with hypotension) was the most frequent cause of PDDIs (n=322 patients; 56.7% of individuals with PDDIs). Analysis of the PDDIs revealed that 70.4% were of moderate severity, 64.8% were supported by good quality evidence and 56.8% were considered of delayed onset. The multivariate analysis showed that the risk of a PDDI was significantly increased among elderly individuals using six or more medications (odds ratio [OR] 3.37) and in patients with hypertension (OR 2.56), diabetes mellitus (OR 1.73) or heart problems (OR 3.36). Conclusions: Approximately one-quarter of the elderly population living in Sao Paulo could be taking two or more potentially interacting medicines. Polypharmacy predisposes elderly individuals to PDDIs. More than half of these drug combinations (57.6%, n = 72) were part of commonly employed treatment regimens and may be responsible for adverse reactions that compromise the safety of elderly individuals, especially at home. Educational initiatives are needed to avoid unnecessary risks.

Effect of image analysis software on neurofunctional activation during processing of emotional human faces

Functional brain imaging techniques such as functional MRI (fMRI) that allow the in vivo investigation of the human brain have been exponentially employed to address the neurophysiological substrates of emotional processing. Despite the growing number of fMRI studies in the field, when taken separately these individual imaging studies demonstrate contrasting findings and variable pictures, and are unable to definitively characterize the neural networks underlying each specific emotional condition. Different imaging packages, as well as the statistical approaches for image processing and analysis, probably have a detrimental role by increasing the heterogeneity of findings. In particular, it is unclear to what extent the observed neurofunctional response of the brain cortex during emotional processing depends on the fMRI package used in the analysis. In this pilot study, we performed a double analysis of an fMRI dataset using emotional faces. The Statistical Parametric Mapping (SPM) version 2.6 (Wellcome Department of Cognitive Neurology, London, UK) and the XBAM 3.4 (Brain Imaging Analysis Unit, Institute of Psychiatry, Kings College London, UK) programs, which use parametric and non-parametric analysis, respectively, were used to assess our results. Both packages revealed that processing of emotional faces was associated with an increased activation in the brain`s visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex, in the cingulate cortex (anterior and posterior cingulate), and in the dorsolateral and ventrolateral prefrontal cortex. However, blood oxygenation level-dependent (BOLD) response in the temporal regions, insula and putamen was evident in the XBAM analysis but not in the SPM analysis. Overall, SPM and XBAM analyses revealed comparable whole-group brain responses. Further Studies are needed to explore the between-group compatibility of the different imaging packages in other cognitive and emotional processing domains. (C) 2009 Elsevier Ltd. All rights reserved.

MDA-based re-engineering with Object-Z

This paper describes a practical application of MDA and reverse engineering based on a domain-specific modelling language. A well defined metamodel of a domain-specific language is useful for verification and validation of associated tools. We apply this approach to SIFA, a security analysis tool. SIFA has evolved as requirements have changed, and it has no metamodel. Hence, testing SIFA’s correctness is difficult. We introduce a formal metamodelling approach to develop a well-defined metamodel of the domain. Initially, we develop a domain model in EMF by reverse engineering the SIFA implementation. Then we transform EMF to Object-Z using model transformation. Finally, we complete the Object-Z model by specifying system behavior. The outcome is a well-defined metamodel that precisely describes the domain and the security properties that it analyses. It also provides a reliable basis for testing the current SIFA implementation and forward engineering its successor.