Incorporating Breast Asymmetry Studies into CADx Systems

Breast cancer is one of the global leading causes of death among women, and an early detection is of uttermost importance to reduce mortality rates. Screening mammograms, in which radiologists rely only on their eyesight, are one of the most used early detection methods. However, characteristics, such as the asymmetry between breasts, a feature that could be very difficult to visually quantize, is key to breast cancer detection. Due to the highly heterogeneous and deformable structure of the breast itself, incorporating asymmetry measurements into an automated detection system is still a challenge. In this study, we proposed the use of a bilateral registration algorithm as an effective way to automatically measure mirror asymmetry. Furthermore, this information was fed to a machine learning algorithm to improve the accuracy of the model. In this study, 449 subjects (197 with calcifications, 207 with masses, and 45 healthy subjects) from a public database were used to train and evaluate the proposed methodology. Using this procedure, we were able to independently identify subjects with calcifications (accuracy = 0.825, AUC = 0.882) and masses (accuracy = 0.698, AUC = 0.807) from healthy subjects

Enfoques actuales de la educación en valores

Instrumento dirigido a educadores de cualquier tipo de especialidad con la intención de generar en ellos el compromiso de la educación en valores, objetivo fundamental de la educación, entendida como fuerza promotora del desarrollo integral de las personas. Se divide en cinco capítulos, cada uno de los cuales presenta objetivos y contenidos específicos, así como una selección de ejercicios y actividades para profundizar sobre las cuestiones más relevantes y actuales de la educación en valores.AndalucíaBiblioteca de Educación del Ministerio de Educación, Cultura y Deporte; Calle San Agustín 5 -3 Planta; 28014 Madrid; Tel. +34917748000; [email protected]

Cáncer colorrectal en Nuevo León: factores de riesgo,hallazgos clínicos y cambios en el desempeño físico de los pacientes a los 12 meses de postcirugía

En 2008 ocurrieron en México 3 275 muertes por cáncer co­lorrectal (CCR). De éstas, 798 (24.37%) correspondieron a los seis estados que forman la frontera norte de nuestro país. Nuevo León registró 135 muer­tes por CCR, lo cual representa 4.12 y 16.9% de los decesos ocurridos en México y en la frontera norte, respec­tivamente

The Involvement of Krüppel-like Factors in Cardiovascular Diseases

Krüppel-like factors (KLFs) are a set of DNA-binding proteins belonging to a family of zinc-finger transcription factors, which have been associated with many biological processes related to the activation or repression of genes, inducing cell growth, differentiation, and death, and the development and maintenance of tissues. In response to metabolic alterations caused by disease and stress, the heart will undergo cardiac remodeling, leading to cardiovascular diseases (CVDs). KLFs are among the transcriptional factors that take control of many physiological and, in this case, pathophysiological processes of CVD. KLFs seem to be associated with congenital heart disease-linked syndromes, malformations because of autosomal diseases, mutations that relate to protein instability, and/or loss of functions such as atheroprotective activities. Ischemic damage also relates to KLF dysregulation because of the differentiation of cardiac myofibroblasts or a modified fatty acid oxidation related to the formation of a dilated cardiomyopathy, myocardial infarctions, left ventricular hypertrophy, and diabetic cardiomyopathies. In this review, we describe the importance of KLFs in cardiovascular diseases such as atherosclerosis, myocardial infarction, left ventricle hypertrophy, stroke, diabetic cardiomyopathy, and congenital heart diseases. We further discuss microRNAs that have been involved in certain regulatory loops of KLFs as they may act as critical in CVDs

The Involvement of Krüppel-like Factors in Cardiovascular Diseases

Krüppel-like factors (KLFs) are a set of DNA-binding proteins belonging to a family of zinc-finger transcription factors, which have been associated with many biological processes related to the activation or repression of genes, inducing cell growth, differentiation, and death, and the development and maintenance of tissues. In response to metabolic alterations caused by disease and stress, the heart will undergo cardiac remodeling, leading to cardiovascular diseases (CVDs). KLFs are among the transcriptional factors that take control of many physiological and, in this case, pathophysiological processes of CVD. KLFs seem to be associated with congenital heart disease-linked syndromes, malformations because of autosomal diseases, mutations that relate to protein instability, and/or loss of functions such as atheroprotective activities. Ischemic damage also relates to KLF dysregulation because of the differentiation of cardiac myofibroblasts or a modified fatty acid oxidation related to the formation of a dilated cardiomyopathy, myocardial infarctions, left ventricular hypertrophy, and diabetic cardiomyopathies. In this review, we describe the importance of KLFs in cardiovascular diseases such as atherosclerosis, myocardial infarction, left ventricle hypertrophy, stroke, diabetic cardiomyopathy, and congenital heart diseases. We further discuss microRNAs that have been involved in certain regulatory loops of KLFs as they may act as critical in CVDs

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

<div><p>In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.</p></div

Characteristics of the model obtained for PAM50.

<p>(A) A heat map representation of the features associated to risk from PAM50 ROR. The figure shows the features selected by LASSO (vertical axis) and their univariate Spearman coefficient and rank along samples (horizontal axis) ordered by the PAM50 ROR score. The top of the figure includes common clinical indicators. The image data was scaled to z-score to nightlight differences. Orange dots at the right represent features also present in the OncotypeDX model. (B) Comparison of the estimated PAM50 recurrence score with that of the score predicted by the image model in (A). Each dot represents a sample. Colors represent subtypes and filled or open circles represent younger or older patients.</p

Artificial Scaffolds in Cardiac Tissue Engineering

Cardiovascular diseases are a leading cause of death worldwide. Current treatments directed at heart repair have several disadvantages, such as a lack of donors for heart transplantation or non-bioactive inert materials for replacing damaged tissue. Because of the natural lack of regeneration of cardiomyocytes, new treatment strategies involve stimulating heart tissue regeneration. The basic three elements of cardiac tissue engineering (cells, growth factors, and scaffolds) are described in this review, with a highlight on the role of artificial scaffolds. Scaffolds for cardiac tissue engineering are tridimensional porous structures that imitate the extracellular heart matrix, with the ability to promote cell adhesion, migration, differentiation, and proliferation. In the heart, there is an important requirement to provide scaffold cellular attachment, but scaffolds also need to permit mechanical contractility and electrical conductivity. For researchers working in cardiac tissue engineering, there is an important need to choose an adequate artificial scaffold biofabrication technique, as well as the ideal biocompatible biodegradable biomaterial for scaffold construction. Finally, there are many suitable options for researchers to obtain scaffolds that promote cell–electrical interactions and tissue repair, reaching the goal of cardiac tissue engineering

Radiogenomics pipeline used in the analysis of association between imaging features and gene signatures in patients with breast cancer.

<p>First, mammograms and tumor biopsy samples were acquired before surgery or treatment. A trained radiologist delimited the lesion region of interest to calculate Image features. For tumor biopsy samples, RNA was extracted and gene expression was measured using microarray technology, then the PAM50 molecular subtype and OncotypeDX recurrence score were measured. Univariate association based on correlation was used to show that image features are associated to signatures. Multivariate analysis was used to fit predictive models using cross-validation strategies and a feature selection algorithm. A similar procedure was used for contralateral images to evaluate whether the associations were tumor-specific.</p

Summary of patients per risk group.

<p>Summary of patients per risk group.</p