Introducción: en pacientes con virus de inmunodeficiencia humana algunos antirretrovirales afectan el perfil lipídico incrementando el riesgo cardiovascular. Hay evidencia de que los inhibidores de integrasa afectan poco al perfil lipídico. El presente estudio buscó evaluar la mejor evidencia disponible sobre cambios en lípidos de pacientes con virus de inmunodeficiencia humana que cambiaron su terapia antirretroviral a esquemas con inhibidores de integrasa. Métodos: revisión sistemática de la literatura con intención metaanalítica. A partir de la pregunta: “En pacientes mayores de 16 años con virus de inmunodeficiencia humana, los esquemas antirretrovirales que incluyen inhibidores de integrasa comparados con aquellos esquemas antirretrovirales que no los incluyen, ¿presentan cambios en el perfil lipídico?” se extrajeron palabras clave para búsqueda de la evidencia publicada entre 1997 y diciembre 2019. Se incluyeron estudios experimentales y observacionales y su calidad fue evaluada. Se realizó análisis por inhibidor de integrasa y parámetro lipídico buscándose síntesis cuantitativa de la evidencia. Resultados: se identificaron 17 estudios relevantes susceptibles de síntesis de la evidencia con un total de 5 683 pacientes. De estos, 2 878 entraron a síntesis cuantitativa. Acorde a lo encontrado, los inhibidores de integrasa presentan mejor perfil lipídico comparados a otros antirretrovirales. Dolutegravir fue el que mostró mejor perfil lipídico cuando la comparación se hizo con inhibidores de proteasa. Raltegravir tuvo mejor perfil lipídico comparándolo con inhibidores de transcriptasa inversa no análogos de nucleósidos. Conclusiones: el uso de inhibidores de integrasa es un factor relevante en el control del riesgo cardiovascular en pacientes con virus de inmunodeficiencia humana.
Metabolomic studies generate large amounts of data, whose complexity increases if they are derived from in vivo experiments. As a result, analysis methods highly used in metabolomics, such as Partial Least Squares Discriminant Analysis (PLS-DA), can have particular difficulties with this type of data. However, there is evidence that indicates that Support Vector Machines (SVMs) can better deal with complex data. On the other hand, chronic exposure to organochlorines is a public health problem. It has been associated with diseases such as cancer. Therefore, its identification is relevant to reduce their impact on human health. This study explores the performance of SVMs in classifying metabolic profiles and identifying relevant metabolites in studies of exposure to organochlorines. For this purpose, two experiments were conducted: in the first one, organochlorine exposure was evaluated in HepG2 cells; and, in the second one, it was evaluated in serum samples of agricultural workers exposed to pesticides. The performance of SVMs was compared with that of PLS-DA. Four kernel functions were assessed in SVMs, and the accuracy of both methods was evaluated using a k-fold cross-validation test. In order to identify the most relevant metabolites, Recursive Feature Elimination (RFE) was used in SVMs and Variable Importance in Projection (VIP) in PLS-DA. The results show that SVMs exhibit a higher percentage of accuracy with fewer training samples and better performance in classifying the samples from the exposed agricultural workers. Finally, a workflow based on SVMs for the identification of biomarkers in samples with high biological complexity is proposed.
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