ABSTRACT
Breast cancer (BC) is a leading cause of women's morbimortality worldwide. Unfortunately, attempts to predict women's susceptibility to developing BC well before it becomes symptomatic, based on their genetic, family, and reproductive background have proved unsatisfactory. Here we analyze the matching of personality traits and protein serum profiles to predict women's susceptibility to developing cancer. We conducted a prospective study among 150 women (aged 18-70 years), who were distributed into three groups (n = 50): women without breast pathology and women diagnosed with BC or benign breast pathology. Psychological data were obtained through standardized psychological tests and serum protein samples were analyzed through semiquantitative protein immunoblotting. The matching for psychological and immunological profiles was constructed from these data using a mathematical generalized linear model.The model predicted that women who have stronger associations between high-intensity stress responses, emotional containment, and an increased number and reduced variability of serum proteins (detected by IgG autoantibodies) have the greatest susceptibility to develop BC before the disease has manifested clinically. Hence, the present study endorses the possibility of using psychological and biochemical tests in combination to increase the possibility of identifying women at risk of developing BC before the disease shows clinical manifestations. A longitudinal study must be instrumented to test the prediction ability of the instrument in real scenarios. Trial registration: Committee of Ethical Research of the Hospital General de México "Dr. Eduardo Liceaga," Ministry of Health (DI/12/111/03/064).
ABSTRACT
The modeling of ecological data that include both abiotic and biotic factors is fundamental to our understanding of ecosystems. Repositories of biodiversity data, such as GBIF, iDigBio, Atlas of Living Australia, and SNIB (Mexico's National System of Biodiversity Information), contain a great deal of information that can lead to knowledge discovery about ecosystems. However, there is a lack of tools with which to efficiently extract such knowledge. In this paper, we present SPECIES, an open, web-based platform designed to extract implicit information contained in large scale sets of ecological data. SPECIES is based on a tested methodology, wherein the correlations of variables of arbitrary type and spatial resolution, both biotic and abiotic, discrete and continuous, may be explored from both niche and network perspectives. In distinction to other modeling systems, SPECIES is a full stack exploratory tool that integrates the three basic components: data (which is incrementally growing), a statistical modeling and analysis engine, and an interactive visualization front end. Combined, these components provide a powerful tool that may guide ecologists toward new insights. SPECIES is optimized to support fast hypothesis prototyping and testing, analyzing thousands of biotic and abiotic variables, and presenting descriptive results to the user at different levels of detail. SPECIES is an open-access platform available online (http://species.conabio.gob.mx), that is, powerful, flexible, and easy to use. It allows for the exploration and incorporation of ecological data and its subsequent integration into predictive models for both potential ecological niche and geographic distribution. It also provides an ecosystemic, network-based analysis that may guide the researcher in identifying relations between different biota, such as the relation between disease vectors and potential disease hosts.
