Survival and Predictive Factors of Lethality in Hemodyalisis: D/I Polymorphism of The Angiotensin I-Converting Enzyme and of the Angiotensinogen M235T Genes

Background: End-stage kidney disease patients continue to have markedly increased cardiovascular disease morbidity and mortality. Analysis of genetic factors connected with the renin-angiotensin system that influences the survival of the patients with end-stage kidney disease supports the ongoing search for improved outcomes. Objective: To assess survival and its association with the polymorphism of renin-angiotensin system genes: angiotensin I-converting enzyme insertion/deletion and angiotensinogen M235T in patients undergoing hemodialysis. Methods: Our study was designed to examine the role of renin-angiotensin system genes. It was an observational study. We analyzed 473 chronic hemodialysis patients in four dialysis units in the state of Rio de Janeiro. Survival rates were calculated by the Kaplan-Meier method and the differences between the curves were evaluated by Tarone-Ware, Peto-Prentice, and log rank tests. We also used logistic regression analysis and the multinomial model. A p value ≤ 0.05 was considered to be statistically significant. The local medical ethics committee gave their approval to this study. Results: The mean age of patients was 45.8 years old. The overall survival rate was 48% at 11 years. The major causes of death were cardiovascular diseases (34%) and infections (15%). Logistic regression analysis found statistical significance for the following variables: age (p = 0.000038), TT angiotensinogen (p = 0.08261), and family income greater than five times the minimum wage (p = 0.03089), the latter being a protective factor. Conclusions: The survival of hemodialysis patients is likely to be influenced by the TT of the angiotensinogen M235T gene.

Influence of pore size distribution on drying behaviour of porous media by a continuous model

Bundle of capillaries, drying kinetics, continuous model, relative permeability, capillary pressure, control volume method

Development and Validation of Predictive Models of Cardiac Mortality and Transplantation in Resynchronization Therapy

AbstractBackground:30-40% of cardiac resynchronization therapy cases do not achieve favorable outcomes.Objective:This study aimed to develop predictive models for the combined endpoint of cardiac death and transplantation (Tx) at different stages of cardiac resynchronization therapy (CRT).Methods:Prospective observational study of 116 patients aged 64.8 ± 11.1 years, 68.1% of whom had functional class (FC) III and 31.9% had ambulatory class IV. Clinical, electrocardiographic and echocardiographic variables were assessed by using Cox regression and Kaplan-Meier curves.Results:The cardiac mortality/Tx rate was 16.3% during the follow-up period of 34.0 ± 17.9 months. Prior to implantation, right ventricular dysfunction (RVD), ejection fraction < 25% and use of high doses of diuretics (HDD) increased the risk of cardiac death and Tx by 3.9-, 4.8-, and 5.9-fold, respectively. In the first year after CRT, RVD, HDD and hospitalization due to congestive heart failure increased the risk of death at hazard ratios of 3.5, 5.3, and 12.5, respectively. In the second year after CRT, RVD and FC III/IV were significant risk factors of mortality in the multivariate Cox model. The accuracy rates of the models were 84.6% at preimplantation, 93% in the first year after CRT, and 90.5% in the second year after CRT. The models were validated by bootstrapping.Conclusion:We developed predictive models of cardiac death and Tx at different stages of CRT based on the analysis of simple and easily obtainable clinical and echocardiographic variables. The models showed good accuracy and adjustment, were validated internally, and are useful in the selection, monitoring and counseling of patients indicated for CRT.

Association between Functional Variables and Heart Failure after Myocardial Infarction in Rats

Background: Heart failure prediction after acute myocardial infarction may have important clinical implications. Objective: To analyze the functional echocardiographic variables associated with heart failure in an infarction model in rats. Methods: The animals were divided into two groups: control and infarction. Subsequently, the infarcted animals were divided into groups: with and without heart failure. The predictive values were assessed by logistic regression. The cutoff values predictive of heart failure were determined using ROC curves. Results: Six months after surgery, 88 infarcted animals and 43 control animals were included in the study. Myocardial infarction increased left cavity diameters and the mass and wall thickness of the left ventricle. Additionally, myocardial infarction resulted in systolic and diastolic dysfunction, characterized by lower area variation fraction values, posterior wall shortening velocity, E-wave deceleration time, associated with higher values of E / A ratio and isovolumic relaxation time adjusted by heart rate. Among the infarcted animals, 54 (61%) developed heart failure. Rats with heart failure have higher left cavity mass index and diameter, associated with worsening of functional variables. The area variation fraction, the E/A ratio, E-wave deceleration time and isovolumic relaxation time adjusted by heart rate were functional variables predictors of heart failure. The cutoff values of functional variables associated with heart failure were: area variation fraction < 31.18%; E / A > 3.077; E-wave deceleration time < 42.11 and isovolumic relaxation time adjusted by heart rate < 69.08. Conclusion: In rats followed for 6 months after myocardial infarction, the area variation fraction, E/A ratio, E-wave deceleration time and isovolumic relaxation time adjusted by heart rate are predictors of heart failure onset.

Cost Factors in Digital Projects: A Model Useful in Other Applications

Description of a costing model developed by digital production librarian to determine the cost to put an item into the Claremont Colleges Digital Library at the Claremont University Consortium. This case study includes variables such as material types and funding sources, data collection methods, and formulas and calculations for analysis. This model is useful for grant applications, cost allocations, and budgeting for digital project coordinators and digital library projects.

MB-MDR: Model-Based Multifactor Dimensionality Reduction for detecting interactions in high-dimensional genomic data

L’anàlisi de l’efecte dels gens i els factors ambientals en el desenvolupament de malalties complexes és un gran repte estadístic i computacional. Entre les diverses metodologies de mineria de dades que s’han proposat per a l’anàlisi d’interaccions una de les més populars és el mètode Multifactor Dimensionality Reduction, MDR, (Ritchie i al. 2001). L’estratègia d’aquest mètode és reduir la dimensió multifactorial a u mitjançant l’agrupació dels diferents genotips en dos grups de risc: alt i baix. Tot i la seva utilitat demostrada, el mètode MDR té alguns inconvenients entre els quals l’agrupació excessiva de genotips pot fer que algunes interaccions importants no siguin detectades i que no permet ajustar per efectes principals ni per variables confusores. En aquest article il•lustrem les limitacions de l’estratègia MDR i d’altres aproximacions no paramètriques i demostrem la conveniència d’utilitzar metodologies parametriques per analitzar interaccions en estudis cas-control on es requereix l’ajust per variables confusores i per efectes principals. Proposem una nova metodologia, una versió paramètrica del mètode MDR, que anomenem Model-Based Multifactor Dimensionality Reduction (MB-MDR). La metodologia proposada té com a objectiu la identificació de genotips específics que estiguin associats a la malaltia i permet ajustar per efectes marginals i variables confusores. La nova metodologia s’il•lustra amb dades de l’Estudi Espanyol de Cancer de Bufeta.

Sistema de control remot d'una màquina de rajos X via web

El projecte desenvolupat té com a objectiu la millora en la gestió de la configuració de les màquines de rajos X de l'empresa Varpe. La principal funció d’aquestes màquines és la inspecció d'envasos de productes alimentaris, amb l'objectiu de detectar anomalies produïdes durant l'envasat. En l'actualitat la resolució d'incidències d'aquestes màquines requereix el desplaçament físic d'operaris a les localitzacions on estan situades les màquines. El projecte proporciona una nova forma de treball a partir de l'ús de bases de dades distribuïdes en les màquines de rajos X i que són gestionades per un servidor central. Entre d'altres, la principal avantatge d'aquesta gestió és que la reconfiguració i resolució d'indecències es pot realitzar fàcilment a través d'una aplicació web.