Factores pronósticos del cáncer de mama. Modelo predictivo

Introduction. Breast cancer remains the most frecuent tumor among women in developed countries. The prognosis is linked to a great variety clinic and pathological factors. The objectives from this study are to identify markers related to survival of patients with primary diagnosis of breast cancer. Material and methods. We have reviewed the medical dossier from 2.227 consecutive women diagnosed for infiltrating breast cancer between January 1966 and december 2000 in a single institution. For statistic analysis we used 10.0 SPSS software. Results. In the univariate analysis, factors with the strongest predictive value for overall survival were: PEV, estrogene and progesterone receptors, TNM stage, lymphatic vessel involvement, histologic grade, Scarff differentiation and mitosis rate, elastosis, presence of histiocitosis, and the percentage of involved stage I and III lymph nodes (Berg clasification). In the multivariate analysis, 5 factors; progesterone receptors, Scarff mitotic rate, lymphatic vessel involvement, percentage of involved stage I lymph nodes, and presence of metastasis; were independent prognostic markers of survival. Conclusions. Many independent factors interact in the survival of patients with primary breast cancer. Determination of hormonal receptors, mainly progesterone’s, appear as the most powerful indicators. The analysis has generated a prognostic simplified classification, based in the 5 independent variables, that provides specific rates for survival at 2, 5 and 10 years.     

Allogeneic stem-cell transplantation may overcome the adverse prognosis of unmutated VH gene in patients with chronic lymphocytic leukemia.

PURPOSE: To investigate whether allogeneic stem-cell transplantation (allo-SCT) may overcome the negative impact of unmutated VH genes in the outcome of patients with chronic lymphocytic leukemia (CLL). PATIENTS AND METHODS: We analyzed the outcome of patients who underwent SCT according to their VH mutational status. RESULTS: Thirty-four patients (14 allo-SCT and 20 autologous SCT [auto-SCT]) presented unmutated VH genes and 16 patients presented mutated VH genes (nine allo-SCT and seven auto-SCT). Tumoral burden pre-SCT was significantly higher in the allo-SCT patients independent of the VH mutational status. The risk of relapse was significantly higher after auto-SCT (5-year risk, 61%; 95% CI, 44% to 84%) than after allo-SCT (5-year risk 12%, 95% CI, 3% to 44%; P < .05). In the unmutated group, 13 of 20 auto-SCT and two of 14 allo-SCT patients experienced disease progression, with a risk of relapse at 5 years of 66% (95% CI, 48% to 93%) v 17% (95% CI, 5% to 60%), respectively (P = .01). CONCLUSION: These results show that allo-SCT may overcome the unfavorable effect of unmutated VH genes in patients with CLL.     

Improvement to the Corrosion Resistance of Ti-Based Implants Using Hydrothermally Synthesized Nanostructured Anatase Coatings

The electrochemical behavior of polycrystalline TiO₂ anatase coatings prepared by a one-step hydrothermal synthesis on commercially pure (CP) Ti grade 2 and a Ti13Nb13Zr alloy for bone implants was investigated in Hank’s solution at 37.5 °C. The aim was to verify to what extent the in-situ-grown anatase improved the behavior of the substrate in comparison to the bare substrates. Tafel-plot extrapolations from the potentiodynamic curves revealed a substantial improvement in the corrosion potentials for the anatase coatings. Moreover, the coatings grown on titanium also exhibited lower corrosion-current densities, indicating a longer survival of the implant. The results were explained by considering the effects of crystal morphology, coating thickness and porosity. Evidence for the existing porosity was obtained from corrosion and nano-indentation tests. The overall results indicated that the hydrothermally prepared anatase coatings, with the appropriate morphology and surface properties, have attractive prospects for use in medical devices, since better corrosion protection of the implant can be expected.     

Effects of High-Fat Diet and Maternal Binge-Like Alcohol Consumption and Their Influence on Cocaine Response in Female Mice Offspring

BackgroundPrenatal alcohol exposure is a leading cause of neurobehavioral and neurocognitive deficits collectively known as fetal alcohol spectrum disorders, including eating disorders and increased risk for substance abuse as very common issues. In this context, the present study aimed to assess the interaction between prenatal and lactation alcohol exposure (PLAE) and a high-fat diet (HFD) during childhood and adolescence.MethodsPregnant C57BL/6 mice underwent a procedure for alcohol binge drinking during gestation and lactation periods. Subsequently, PLAE female offspring were fed with an HFD for 8 weeks, and thereafter, nutrition-related parameters as well as their response to cocaine were assessed.ResultsIn our model, feeding young females with an HFD increased their triglyceride blood levels but did not induce overweight compared with those fed with a standard diet. Moreover, PLAE affected how females responded to the fatty diet as they consumed less food than water-exposed offspring, consistent with a lower gain of body weight. HFD increased the psychostimulant effects of cocaine. Surprisingly, PLAE reduced the locomotor responses to cocaine without modifying cocaine-induced reward. Moreover, PLAE prevented the striatal overexpression of cannabinoid 1 receptors induced by an HFD and induced an alteration of myelin damage biomarker in the prefrontal cortex, an effect that was mitigated by an HFD-based feeding.ConclusionTherefore, in female offspring, some effects triggered by one of these factors, PLAE or an HFD, were blunted by the other, suggesting a close interaction between the involved mechanisms.     

Extracting MRS discriminant functional features of brain tumors

The current challenge in automatic brain tumor classification based on MRS is the improvement of the robustness of the classification models that explicitly account for the probable breach of the independent and identically distributed conditions in the MRS data points. To contribute to this purpose, a new algorithm for the extraction of discriminant MRS features of brain tumors based on a functional approach is presented. Functional data analysis based on region segmentation (RSFDA) is based on the functional data analysis formalism using nonuniformly distributed B splines according to spectral regions that are highly correlated. An exhaustive characterization of the method is presented in this work using controlled and real scenarios. The performance of RSFDA was compared with other widely used feature extraction methods. In all simulated conditions, RSFDA was proven to be stable with respect to the number of variables selected and with respect to the classification performance against noise and baseline artifacts. Furthermore, with real multicenter datasets classification, RSFDA and peak integration (PI) obtained better performance than the other feature extraction methods used for comparison. Other advantages of the method proposed are its usefulness in selecting the optimal number of features for classification and its simplified functional representation of the spectra, which contributes to highlight the discriminative regions of the MR spectrum for each classification task. Copyright © 2012 John Wiley & Sons, Ltd.