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.

Integration pattern of HIV-1 based lentiviral vector carrying recombinant coagulation factor VIII in Sk-Hep and 293T cells

293T and Sk-Hep-1 cells were transduced with a replication-defective self-inactivating HIV-1 derived vector carrying FVIII cDNA. The genomic DNA was sequenced to reveal LTR/human genome junctions and integration sites. One hundred and thirty-two sequences matched human sequences, with an identity of at least 98%. The integration sites in 293T-FVIIIDB and in Sk-Hep-FVIIIDB cells were preferentially located in gene regions. The integrations in both cell lines were distant from the CpG islands and from the transcription start sites. A comparison between the two cell lines showed that the lentiviral-transduced DNA had the same preferred regions in the two different cell lines.

Reference distribution of the bioelectrical impedance vector in healthy term newborns

Bioelectrical impedance vector analysis (BIVA) is a new method that is used for the routine monitoring of the variation in body fluids and nutritional status with assumptions regarding body composition values. The aim of the present study was to determine bivariate tolerance intervals of the whole-body impedance vector and to describe phase angle (PA) values for healthy term newborns aged 7-28 d. This descriptive cross-sectional study was conducted on healthy term neonates born at a low-risk public maternity. General and anthropometric neonatal data and bioelectrical impedance data (800 mu A-50 kHz) were obtained. Bivariate vector analysis was conducted with the resistance-reactance (RXc) graph method. The BIVA software was used to construct the graphs. The study was conducted on 109 neonates (52.3% females) who were born at term, adequate for gestational age, exclusively breast-fed and aged 13 (SD 3.6) d. We constructed one standard, reference, RXc-score graph and RXc-tolerance ellipses (50, 75 and 95 %) that can be used with any analyser. Mean PA was 3.14 (SD 0.43)degrees (3.12 (SD 0.39)degrees for males and 3.17 (SD 0.48)degrees for females). Considering the overlapping of ellipses of males and females with the general distribution, a graph for newborns aged 7-28 d with the same reference tolerance ellipse was defined for boys and girls. The results differ from those reported in the literature probably, in part, due to the ethnic differences in body composition. BIVA and PA permit an assessment without the need to know body weight and the prediction error of conventional impedance formulas.

Estimation of R(0) from the initial phase of an outbreak of a vector-borne infection

The magnitude of the basic reproduction ratio R(0) of an epidemic can be estimated in several ways, namely, from the final size of the epidemic, from the average age at first infection, or from the initial growth phase of the outbreak. In this paper, we discuss this last method for estimating R(0) for vector-borne infections. Implicit in these models is the assumption that there is an exponential phase of the outbreaks, which implies that in all cases R(0) > 1. We demonstrate that an outbreak is possible, even in cases where R(0) is less than one, provided that the vector-to-human component of R(0) is greater than one and that a certain number of infected vectors are introduced into the affected population. This theory is applied to two real epidemiological dengue situations in the southeastern part of Brazil, one where R(0) is less than one, and other one where R(0) is greater than one. In both cases, the model mirrors the real situations with reasonable accuracy.

Clear cell odontogenic carcinoma: case report with immunohistochemical findings adding support to the challenging diagnosis

Clear cell odontogenic carcinoma (CCOC) is a rare odontogenic tumor associated with aggressive clinical behavior, metastasis, and low survival. We report a case of CCOC affecting the mandible of a 39-year-old man. The tumor presented a biphasic pattern composed of clear cell nests intermingled with eosinophilic cells and separated by collagenous stroma. Immunoreactivity to cytokeratin (CK), specifically AE1/AE3 and CK 8, 14, 18, and 19 was found, as well as to epithelial membrane antigen (EMA). The tumor cells were negative for S100 protein, CK 13, vimentin, smooth muscle actin, laminin and type IV collagen. Low labeling indices for the proliferation markers Ki-67 and proliferating cell nuclear antigen and to p53 protein might predict a favorable prognosis for the lesion. A surgical resection was performed, followed by adjuvant radiotherapy. A 2-year follow-up has shown no signs of recurrence. The significance of histochemical and immunohistochemical resources in the correct diagnosis of CCOC is analyzed.

Photoelastic Study of the Support Structures of Distal-Extension Removable Partial Dentures

Purpose: The double system of support, in which the distal-extension removable partial denture adapts, causes inadequate stress around abutment teeth, increasing the possibility of unequal bone resorption. Several ways to reduce or more adequately distribute the stress between abutment teeth and residual ridges have been reported; however, there are no definitive answers to the problem. The purpose of this study was to analyze, by means of photoelasticity, the most favorable stress distribution using three retainers: T bar, rest, proximal plate, I bar (RPI), and circumferential with mesialized rest. Materials and Methods: Three photoelastic models were made simulating a Kennedy Class II inferior arch. Fifteen dentures with long saddles, five of each design, were adjusted to the photoelastic patterns and submitted first to uniformly distributed load, and then to a load localized on the last artificial tooth. The saddles were then shortened and the tests repeated. The quantitative and qualitative analyses of stress intensity were done manually and by photography, respectively. For intragroup analyses the Wilcoxon test for paired samples was used, while for intergroup analyses Friedman and Wilcoxon tests were used to better identify the differences (p < 0.05). Results: The RPI retainer, followed by the T bar, demonstrated the best distribution of load between teeth and residual ridge. The circumferential retainer caused greater concentration of stress between dental apexes. Stress distribution was influenced by the type of retainer, the length of the saddle, and the manner of load application. Conclusions: The long saddles and the uniformly distributed loads demonstrated better distribution of stress on support structures.