A woman with reddish nodule on the skin of the breast.

A 54-year-old woman presented a peri-areolar nodule located in the skin of the right breast. Clinical examination showed a 6 x 5 cm exophytic, lobed, ulcerated, and bleeding nodule. The patient reported that the tumor had grown gradually over a period of 3 months. The patient had been diagnosed 8 years prior to presentation with infiltrating ductal carcinoma of the right breast (pT2NO). This tumor was treated with partial mastectomy (conservative surgery) and lymph node dissection, then subsequently received 30 tangent field radiotherapy sessions to the breast for a total dose of 45 Gy. The rest of her cutaneous exam was normal. There was no family history of any similar tumor.

Skin infection caused by Scedosporium apiospermum in immunocompromised patients. Report of two cases.

Scedosporium apiospermum is a filamentous fungus that can cause cutaneous or extracutaneous disease. A large number of cases have been published over the last decades, mainly in patients immunocompromised as a result of their disease or treatment. These kinds of infections can progress rapidly and become disseminated, leading to very serious or even fatal complications. We report two new cases of skin infection by Scedosporium apiospermum from our hospital.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).