RESUMEN
Promising strategies for neural tissue engineering are based on the use of three-dimensional substrates for cell anchorage and tissue development. In this work, fibrillar scaffolds composed of electrospun randomly- and aligned-oriented fibers coated with plasma synthesized pyrrole polymer, doped and undoped with iodine, were fabricated and characterized. Infrared spectroscopy, thermogravimetric analysis, and X-ray diffraction analysis revealed the functional groups and molecular integration of each scaffold, as well as the effect of plasma polymer synthesis on crystallinity. Scanning microscopy imaging demonstrated the porous fibrillar micrometric structure of the scaffolds, which afforded adhesion, infiltration, and survival for the neural cells. Orientation analysis of electron microscope images confirmed the elongation of neurite-like cell structures elicited by undoped plasma pyrrole polymer-coated aligned scaffolds, without any biochemical stimuli. The MTT colorimetric assay validated the biocompatibility of the fabricated composite materials, and further evidenced plasma pyrrole polymer-coated aligned scaffolds as permissive substrates for the support of neural cells. These results suggest plasma synthesized pyrrole polymer-coated aligned scaffolds are promising materials for tissue engineering applications.
RESUMEN
BACKGROUND: It is estimated that 37% of Mexican adults have undiagnosed diabetes, and are therefore at high risk of developing the severe and devastating complications associated to it. In recent years, a variety of screening tools based on the characteristics of the adult Mexican population have been proposed in order to reduce the negative effects of the disease. OBJECTIVES: To assess the performance of screening models to diagnose diabetes in the Mexican adult population and to propose a screening model based on HbA1c measurements. MATERIALS AND METHODS: Data from the 2016 Halfway National Health and Nutrition Survey (NHNS) were used to assess the screening models and to develop and validate the proposed 2016 NHNS model, built using a multivariate logistic regression model. Explanatory variables included in the 2016 NHNS 2016 model were selected through a stepwise backward procedure, using sensitivity and specificity as performance indicators. RESULTS: Of the screening models assessed, only the model based on the 2006 NHNS survey showed a performance consistent with previous reports. The proposed 2016 NHNS model included age, waist circumference, and systolic blood pressure as explanatory variables and showed a sensitivity of 0.72 and a specificity of 0.80 in the validation data set. CONCLUSIONS: Age, waist circumference, and systolic blood pressure are variables of special importance for early detection of undiagnosed diabetes in Mexican adults. Based on the consistent performance of the 2006 NHNS model in different data sets, its use as a screening tool for adults with undiagnosed diabetes in Mexico is recommended.