Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa).

OBJECTIVES: The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study. Using this method, we obtain CVI estimates and calculate confidence intervals (CI), using the EFLM-BVD CVI estimates as gold standard for comparison. METHODS: Data were collected over a 18-month period for 7 measurands, from 3 Spanish hospitals; inclusion criteria: patients 18-75 years with more than two determinations. For each measurand, four different strategies were carried out based on the coefficient of variation ratio (rCoeV) and based on the use of the bootstrap method (OS1, RS2 and RS3). RS2 and RS3 use symmetry reference change value (RCV) to clean database. RESULTS: RS2 and RS3 had the best correlation for the CVI estimates with respect to EFLM-BVD. RS2 used the symmetric RCV value without eliminating outliers, while RS3 combined RCV and outliers. When using the rCoeV and OS1 strategies, an overestimation of the CVI value was obtained. CONCLUSIONS: Our study presents a new strategy for obtaining robust CVI estimates using an indirect method together with the value of symmetric RCV to select the target population. The CVI estimates obtained show a good correlation with those published in the EFLM-BVD database. Furthermore, our strategy can resolve some of the limitations encountered when using direct methods such as calculating confidence intervals.

Persistent Taenia solium Cysticercosis In the State of Morelos, Mexico: Human and Porcine Seroprevalence.

Serological tests are needed to estimate the prevalence of Taenia solium cysticercosis in endemic rural areas. The predictive value of serum antibody levels to diagnose porcine cysticercosis and human neurocysticercosis (NC) was herein assessed by ELISA using serum samples from 247 backyard pigs (141 without cysticercosis and 106 with cysticercosis) and 183 human subjects (116 non-NC subjects and 67 NC patients) in central Mexico diagnosed by necropsy and computed tomography, respectively. A sensitivity of 77.3 and 92.5% and a specificity of 88.6 and 100% were found to diagnose porcine and human cysticercosis, respectively. The prevalence of porcine and human cysticercosis in the state of Morelos was estimated by ELISA. Anti-cysticercal antibodies were found in 8.4 and 19.02% of assayed sera from 1,811 humans and 804 pigs, respectively. Marginalization and living in the eastern region were risk factors for humans, whereas free-roaming, medium marginalization levels and living in Sierra de Huautla were risk factors for pigs. These results clearly evidence the persistence of cysticercosis transmission and neurocysticercosis in a region neighboring Mexico City, pointing out the need to apply effective measures already available for its control.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies.

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Prevalencia y factores de riesgo consumo de cigarrillo para estudiantes universitarios entre 18 a 25 años, durante el primer semestre académico del año 2013 / Prevalence and risk factors for cigarette consumption among college students between 18-25 years during the first semester of 2013

El consumo de cigarrillo es un problema actual de salud pública, el cual es responsable de una parte importante de la carga de morbilidad y mortalidad en momentos actuales...

Cigarette smoking is a current public health problem, responsible for a large part of burden of morbidity and mortality...

Concordancia del autoreporte de peso y talla para valoración nutricional en estudiantes universitarios de 18 a 25 años / Concordance between self-reported weight, height, and BMI and anthropometric evaluation in 18 to 25 years university students

Antecedentes: la prevalencia de obesidad es un problema de salud pública mundial, que requiere un sistema de vigilancia basado en datos de fácil obtención. Objetivo: determinar la concordancia entre el auto-reporte y la determinación real de peso, estatura e índice de masa corporal (IMC). Materiales y métodos: estudio descriptivo de corte transversal en el que participaron 462 personas de ambos sexos, de 18-25 años, estudiantes de la Universidad de Pamplona, Norte de Santander-Colombia. A cada participante se le indagó su peso y talla y el índice de masa corporal (IMC) fue calculado a partir de estos datos. Además, un profesional entrenado determinó el valor real de estas variables. Se obtuvo el coeficiente de correlación intra-clase entre el auto-reporte y la determinación por profesional para: peso, talla e IMC. Resultados: el IMC real fue: 22,8±3,1 Kg/m² en hombres y 21,9±2,8 Kg/m² en mujeres. Los coeficientes de correlación intra-clase (CCI) fueron: para peso: 0,965 (IC95%: 0,96-0,97; estatura: 0,967 (IC95%: 0,96-0,97), para IMC: 0,920 (IC95% 0,90-0,93). Conclusión: los datos auto-reportados en los estudiantes de la Universidad de Pamplona son útiles para la valoración de la obesidad.

Background: Obesity is a worldwide public health problem, which requires a surveillance system based on readily available data. Objective: To determine the concordance between self-reported and actual weight, height, and body mass index (BMI). Materials and methods: 462 students, male and female, between18-25 years old participated in a cross-sectional study. All participants were students at Universidad de Pamplona, Colombia. Participants were asked for their weight and height. BMI was calculated based on reported data. Furthermore, the variables were actually measured by a trained professional. Intra-class-coefficients (ICC) between self-reported and actual data were calculated for each variable. Results: The actual BMI was 22,8±3,1 Kg/m² in men and 21,9±2,8 Kg/m² in women. ICC (IC95%) for weight, height, and BMI were, respectively: 0.965 (0.96 to 0.97), 0.967 (0.96 to 0.97), and 0.920 (0.90 to 0.93). Conclusion: Self-reported weight and height data are useful for obesity assessment in students from the Universidad de Pamplona-Colombia.

Determination of iodine in human milk and infant formulas.

The aim of this study was to develop a method to determine iodine in human milk and infant formulas using ICP-MS. The milk samples were digested using an alkaline digestion (5% NH(3), 45 W, 2 min and 30s), and the method was validated using a certified reference material (CRM) BCR CRM151. On the other hand the milk was separated in three fractions, whey, fat and caseins using ultracentrifugation (15 min, 4 degrees C, 50,000 rpm) and the iodine was determined in the different fractions. About 27 samples of different infant formulas and 14 samples of human milk have been studied. In the human milk the values found were between 144+/-93.2 microg kg(-1), whereas in the infant formulas the values were 53.3+/-19.5. For both types of samples the bigger amount of iodine is in the whey fraction, between 80% and 90%, whereas in the fat there is about a 2% of the total iodine and in the casein fraction the levels are between 5% and 10% depending on the type of sample.