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1.
Rev. chil. obstet. ginecol ; 79(5): 408-419, oct. 2014. ilus, graf, tab
Article in Spanish | LILACS | ID: lil-729404

ABSTRACT

Antecedentes: Los lípidos plasmáticos maternos durante el embarazo pueden influir en el crecimiento fetal, particularmente en pacientes con diabetes gestacional; estos lípidos cambian su concentración plasmática materna a lo largo de la gestación. Objetivo: Calcular tablas y curvas de lípidos normales según edad gestacional en una población de embarazadas chilenas. Método: Se midió el colesterol total (CT), colesterol LDL (LDL-C) triglicéridos (TG), Colesterol-HDL (HDL-C), y ácidos grasos no esterificados (NEFA), en 94 embarazadas sanas y jóvenes (<33 años, edad media de 27,6 +/- 6,2 años), con peso pregestacional normal (Índice de Masa Corporal entre 20 y 24,9 Kg/m2 y medio de 23,3 +/- 2,0 Kg/m2). Las pacientes provenían de: Hospital Parroquial de San Bernardo, Santiago (n=55), Hospital de Talca (n=2); Hospital del Profesor, Santiago (n=18); Hospital Regional de Concepción (n=9) y Hospital Clínico de la Pontificia Universidad Católica de Chile (n=10). Resultados: Calculamos, para cada uno de los cuatro lípidos, las curvas de percentil 50, percentil 90 y percentil 10, en mg/dL y mmol/l. Los NEFA solo fueron expresados en mmol/l. Incluimos las funciones matemáticas de las curvas de regresión polinomial de los cuatro lípidos con el fin que sean fácilmente reproducibles en otros tamaños. Conclusiones: Calculamos las tablas y curvas de lípidos maternos normales a lo largo del embarazo, que sean aplicables a la población de embarazadas chilenas.


Background: In normal human pregnancy, maternal lipids can modify the rate of fetal growth, particularly in pregnancies with Gestational Diabetes Mellitus (GDM). These lipids change continuously their serum concentration in the mother along the pregnancy. Aim: To calculate tables and curves of normal serum lipids, according to gestational age, in healthy Chilean pregnant women. Methods: We measured total cholesterol (CT), LDL-cholesterol (LDL-C), triglycerides (TG), HDL-Cholesterol (HDL-C), and Non-Esterified Fatty Acids (NEFA) in 94 young and healthy pregnant women (< 33 years, mean age 27.6 +/- 6.2 years), with normal pregestational Body Mass Index (BMI, 20.0-24.9 Kg/m2 , mean value= 23.3 +/- 2.0 Kg/m2). The women of the study were patients of 5 hospitals: Hospital Parroquial de San Bernardo, Santiago (n=55), Hospital de Talca (n=2); Hospital del Profesor, Santiago (n=18); Hospital Regional de Concepción (n=9) and Hospital Clínico de la Pontificia Universidad Católica de Chile (n=10). Results: For each one of the lipids, we calculated curves of 50th, 90th and 10th percentiles, both in mg/dL and mmol/L (the NEFA were expressed only in mmol/L). The mathematical functions of the curves of polynomial regression of all lipids were included in the manuscript, in order to facilitate their reproduction. Conclusions: We calculated tables and curves of normal maternal serum lipids in relation to gestational, in order to make these available for use in the care of Chilean pregnant women.


Subject(s)
Humans , Adult , Fatty Acids, Nonesterified/blood , Cholesterol/blood , Pregnancy/blood , Triglycerides/blood , Chile , Cholesterol, HDL/blood , Cholesterol, LDL/blood
2.
Clin Microbiol Infect ; 20(10): O619-22, 2014 Oct.
Article in English | MEDLINE | ID: mdl-24612452

ABSTRACT

Genotyping and molecular characterization of drug resistance mechanisms in Mycobacterium leprae enables disease transmission and drug resistance trends to be monitored. In the present study, we performed genome-wide analysis of Airaku-3, a multidrug-resistant strain with an unknown mechanism of resistance to rifampicin. We identified 12 unique non-synonymous single-nucleotide polymorphisms (SNPs) including two in the transporter-encoding ctpC and ctpI genes. In addition, two SNPs were found that improve the resolution of SNP-based genotyping, particularly for Venezuelan and South East Asian strains of M. leprae.


Subject(s)
Drug Resistance, Multiple, Bacterial , Mycobacterium leprae/genetics , Sequence Analysis, DNA/methods , Asia, Southeastern , Genome, Bacterial , Genotype , Humans , Leprosy/microbiology , Molecular Sequence Data , Mycobacterium leprae/classification , Phylogeny , Polymorphism, Single Nucleotide , Venezuela
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