Identification of Small, Regularly Shaped Cerebral Aneurysms Prone to Rupture.

BACKGROUND AND PURPOSE: Many small, regularly shaped cerebral aneurysms rupture; however, they usually receive a low score based on current risk-assessment methods. Our goal was to identify patient and aneurysm characteristics associated with rupture of small, regularly shaped aneurysms and to develop and validate predictive models of rupture in this aneurysm subpopulation. MATERIALS AND METHODS: Cross-sectional data from 1079 aneurysms smaller than 7 mm with regular shapes (without blebs) were used to train predictive models for aneurysm rupture using machine learning methods. These models were based on the patient population, aneurysm location, and hemodynamic and geometric characteristics derived from image-based computational fluid dynamics models. An independent data set with 102 small, regularly shaped aneurysms was used for validation. RESULTS: Adverse hemodynamic environments characterized by strong, concentrated inflow jets, high speed, complex and unstable flow patterns, and concentrated, oscillatory, and heterogeneous wall shear stress patterns were associated with rupture in small, regularly shaped aneurysms. Additionally, ruptured aneurysms were larger and more elongated than unruptured aneurysms in this subset. A total of 5 hemodynamic and 6 geometric parameters along with aneurysm location, multiplicity, and morphology, were used as predictive variables. The best machine learning rupture prediction-model achieved a good performance with an area under the curve of 0.84 on the external validation data set. CONCLUSIONS: This study demonstrated the potential of using predictive machine learning models based on aneurysm-specific hemodynamic, geometric, and anatomic characteristics for identifying small, regularly shaped aneurysms prone to rupture.

CMA: a comprehensive Bioconductor package for supervised classification with high dimensional data.

BACKGROUND: For the last eight years, microarray-based classification has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the so-called "p >> n" setting where the number of predictors p by far exceeds the number of observations n, hence the term "ill-posed-problem". Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for statisticians without experience in this area or for scientists with limited statistical background. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers. RESULTS: In this article, we introduce a new Bioconductor package called CMA (standing for "Classification for MicroArrays") for automatically performing variable selection, parameter tuning, classifier construction, and unbiased evaluation of the constructed classifiers using a large number of usual methods. Without much time and effort, users are provided with an overview of the unbiased accuracy of most top-performing classifiers. Furthermore, the standardized evaluation framework underlying CMA can also be beneficial in statistical research for comparison purposes, for instance if a new classifier has to be compared to existing approaches. CONCLUSION: CMA is a user-friendly comprehensive package for classifier construction and evaluation implementing most usual approaches. It is freely available from the Bioconductor website at (http://bioconductor.org/packages/2.3/bioc/html/CMA.html).

Effect of prolactin on release of selected enzymes from the isolated rabbit liver.

This report analyzes the effect of prolactin (PRL) on ALT and AST release from the rabbit liver into the preservation solution. Dissected and perfused livers were stored for 24 hours in Ringer's solution without (control group) or with PRL (experimental group). During the organ preservation, sample of the solution were obtained at 1, 8, 12, 16, 18, and 24 hours. It was found that PRL added to Ringer's solution significantly decreased the quantity and rate of released ALT (P < .081) and AST (P < .029) from the preserved liver. ALT was released 2.51 times more slowly (kappa = -0.03329 [h(-1)]) and AST 3.43 times more slowly (kappa = -0.08356 [h(-1)]) into Ringer's solution with PRL. The experimental group showed maintenance of the value of the de Ritis index at a stable level between 2.0-3.0. In conclusion, PRL added to a preservation solution significantly decreased the quantity and slowed the release rate of ALT and AST aminotransferases from the preserved rabbit liver, implying that this hormone has hepatoprotective properties.