Genetic polymorphisms of RANTES, IL1-A, MCP-1 and TNF-A genes in patients with prostate cancer

BACKGROUND Inflammation has been implicated as an etiological factor in several human cancers, including prostate cancer. Allelic variants of the genes involved in inflammatory pathways are logical candidates as genetic determinants of prostate cancer risk. The purpose of this study was to investigate whether single nucleotide polymorphisms of genes that lead to increased levels of pro-inflammatory cytokines and chemokines are associated with an increased prostate cancer risk. METHODS A case-control study design was used to test the association between prostate cancer risk and the polymorphisms TNF-A-308 A/G (rs 1800629), RANTES-403 G/A (rs 2107538), IL1-A-889 C/T (rs 1800587) and MCP-1 2518 G/A (rs 1024611) in 296 patients diagnosed with prostate cancer and in 311 healthy controls from the same area. RESULTS Diagnosis of prostate cancer was significantly associated with TNF-A GA + AA genotype (OR, 1.61; 95% CI, 1.09-2.64) and RANTES GA + AA genotype (OR, 1.44; 95% CI, 1.09-2.38). A alleles in TNF-A and RANTES influenced prostate cancer susceptibility and acted independently of each other in these subjects. No epistatic effect was found for the combination of different polymorphisms studied. Finally, no overall association was found between prostate cancer risk and IL1-A or MCP-1 polymorphisms. CONCLUSION Our results and previously published findings on genes associated with innate immunity support the hypothesis that polymorphisms in proinflammatory genes may be important in prostate cancer development.

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).

Deficits in executive and memory processes in delusional disorder: a case-control study

OBJECTIVE Delusional disorder has been traditionally considered a psychotic syndrome that does not evolve to cognitive deterioration. However, to date, very little empirical research has been done to explore cognitive executive components and memory processes in Delusional Disorder patients. This study will investigate whether patients with delusional disorder are intact in both executive function components (such as flexibility, impulsivity and updating components) and memory processes (such as immediate, short term and long term recall, learning and recognition). METHODS A large sample of patients with delusional disorder (n = 86) and a group of healthy controls (n = 343) were compared with regard to their performance in a broad battery of neuropsychological tests including Trail Making Test, Wisconsin Card Sorting Test, Colour-Word Stroop Test, and Complutense Verbal Learning Test (TAVEC). RESULTS When compared to controls, cases of delusional disorder showed a significantly poorer performance in most cognitive tests. Thus, we demonstrate deficits in flexibility, impulsivity and updating components of executive functions as well as in memory processes. These findings held significant after taking into account sex, age, educational level and premorbid IQ. CONCLUSIONS Our results do not support the traditional notion of patients with delusional disorder being cognitively intact.