Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.

BackgroundFacioscapulohumeral dystrophy (FSHD) is a progressive muscle disease caused by mutations that lead to epigenetic derepression and inappropriate transcription of the double homeobox 4 (DUX4) gene in skeletal muscle. Drugs that enhance the repression of DUX4 and prevent its expression in skeletal muscle cells therefore represent candidate therapies for FSHD.MethodsWe screened an aggregated chemical library enriched for compounds with epigenetic activities and the Pharmakon 1600 library composed of compounds that have reached clinical testing to identify molecules that decrease DUX4 expression as monitored by the levels of DUX4 target genes in FSHD patient-derived skeletal muscle cell cultures.ResultsOur screens identified several classes of molecules that include inhibitors of the bromodomain and extra-terminal (BET) family of proteins and agonists of the beta-2 adrenergic receptor. Further studies showed that compounds from these two classes suppress the expression of DUX4 messenger RNA (mRNA) by blocking the activity of bromodomain-containing protein 4 (BRD4) or by increasing cyclic adenosine monophosphate (cAMP) levels, respectively.ConclusionsThese data uncover pathways involved in the regulation of DUX4 expression in somatic cells, provide potential candidate classes of compounds for FSHD therapeutic development, and create an important opportunity for mechanistic studies that may uncover additional therapeutic targets.Electronic supplementary materialThe online version of this article (doi:10.1186/s13395-017-0134-x) contains supplementary material, which is available to authorized users.

Diabetic retinopathy is a major cause of vision loss in diabetic patients. Currently, there is a need for making decisions using intelligent computer algorithms when screening a large volume of data. This paper presents an expert decision-making system designed using a fuzzy support vector machine (FSVM) classifier to detect hard exudates in fundus images. The optic discs in the colour fundus images are segmented to avoid false alarms using morphological operations and based on circular Hough transform. To discriminate between the exudates and the non-exudates pixels, colour and texture features are extracted from the images. These features are given as input to the FSVM classifier. The classifier analysed 200 retinal images collected from diabetic retinopathy screening programmes. The tests made on the retinal images show that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and features sets, the area under the receiver operating characteristic curve reached 0.9606, which corresponds to a sensitivity of 94.1% with a specificity of 90.0%. The results suggest that detecting hard exudates using FSVM contribute to computer-assisted detection of diabetic retinopathy and as a decision support system for ophthalmologists.

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