High Prevalence of Signs of Renal Damage Despite Normal Renal Function in a Cohort of HIV-Infected Patients: Evaluation of Associated Factors

Renal disorders are an emerging problem in HIV-infected patients. We performed a cross-sectional study of the first 1000 HIV-infected patients attended at our HIV unit who agreed to participate. We determined the frequency of renal alterations and its related risk factors. Summary statistics and logistic regression were applied. The study sample comprised 970 patients with complete data. Most were white (94%) and men (76%). Median (IQR) age was 48 (42–53) years. Hypertension was diagnosed in 19%, dyslipidemia in 27%, and diabetes mellitus in 3%. According to the Chronic Kidney Disease Epidemiology Collaboration (CKD EPI) equation, 29 patients (3%) had an eGFR < 60 ml/min/1.73m2; 18 of them (62%) presented altered albumin/creatinine and protein/creatinine (UPC or UAC) ratios. Of the patients with eGFR> 60mL/min, it was present in 293 (30%), 38 of whom (7.1%) had UPC> 300mg/g. Increased risk of renal abnormalities was correlated with hypertension (OR, 1.821 [95%CI, 1.292;2.564]; p = 0.001), age (OR, 1.015 [95%CI, 1.001;1.030], per one year; p = 0.040), and use of tenofovir disoproxil fumarate (TDF) plus protease inhibitor (PI), (OR, 1.401 [95%CI, 1.078;1.821]; p = 0.012). Current CD4 cell count was a protective factor (OR, 0.9995 [95%CI, 0.9991;0.9999], per one cell; p = 0.035). A considerable proportion of patients presented altered UPC or UAC ratios, despite having an eGFR > 60mL/min. CD4 cell count was a protective factor; age, hypertension, and use of TDF plus PIs were risk factors for renal abnormalities. Based on our results, screen of renal abnormalities should be considered in all HIV-infected patients to detect these alterations early.

A new approach to the classification of mammographic masses and normal breast tissue

A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach

The normal distribution in some constrained sample spaces

Phenomena with a constrained sample space appear frequently in practice. This is the case e.g. with strictly positive data, or with compositional data, like percentages or proportions. If the natural measure of difference is not the absolute one, simple algebraic properties show that it is more convenient to work with a geometry different from the usual Euclidean geometry in real space, and with a measure different from the usual Lebesgue measure, leading to alternative models which better fit the phenomenon under study. The general approach is presented and illustrated using the normal distribution, both on the positive real line and on the D-part simplex. The original ideas of McAlister in his introduction to the lognormal distribution in 1879, are recovered and updated