Computational modeling and preliminary iroN, fepA, and cirA gene expression in Salmonella Enteritidis under iron-deficiency-induced conditions.

Salmonellosis outbreaks in Europe, the United States, and Latin America have been associated with contaminated food derivatives including meat from the poultry industry. Salmonella grown under iron-limiting conditions has the capability to increase concentration of several iron-regulated outer-membrane proteins to augment the acquisition of the metal. These proteins have been proved to have immunogenic properties. Our aim was to increase the relative expression of iroN, fepA, and cirA in Salmonella Enteritidis domestic strain. Furthermore, we proposed a 3-dimensional structure model for each protein to predict and locate antigenic peptides. Our eventual objective is to produce an effective vaccine against regional avian salmonellosis. Two simple factorial designs were carried out to discriminate between 2 nitrogen sources and determine chelating-agent addition timing to augment relative gene expression. Two antigenic peptides located at the external face of each protein and 2 typical domains of iron-regulated outer-membrane proteins, plug and TonB-dep-Rec, were identified from the 3-dimensional models. Tryptone was selected as the best nitrogen source based on growth rate (µx = 0.36 h(-1)) and biomass productivity (Px = 0.9 g•h(-1)•L(-1)) as determined by a general factorial design. Optimum timing for chelating agent addition was in the middle of the log phase, which allowed relative expressions at 4 h of culture. Increase in iroN, fepA, and cirA relative expression was favored by the length of log phase and the addition of chelating agent, which decreased chelating toxicity and enhanced cell growth rate.

Minería de datos para la evaluación del riesgo de malignidad en pacientes con nódulos tiroideos con estudio citológico Bethesda IV / Risk of malignancy evaluation through data mining technic in patients with thyroid nodules with cytology study Bethesda IV

Introducción: en el campo de la salud, cada decisión representa datos, y las técnicas de minería de datos han empezado a ser una metodología prometedora para el análisis de esta información, especialmente en el diseño de los modelos predictivos. Métodos: estudio observacional analítico de pacientes mayores de 15 años, con reporte de punción de aspiración con aguja fina con estudio Bethesda IV, sometidos a manejo quirúrgico en el Hospital de San José de Bogotá. Los datos recogidos de los pacientes se incluyeron en tres grupos: la información sociodemográfica y clínica, los hallazgos en la citología y los reportes de la ecografía. Se realizó el análisis mediante Naive-Bayes, árbol de decisión y redes neuronales. Se usó la herramienta Weka versión 3.8.2. Resultados: de los 427 pacientes, 195 tuvieron resultados de patología de carcinoma de tiroides (45,6 %). Se evidenciaron mejores resultados usando la validación cruzada (10 fold) comparado con partición (66 %), la técnica de Bayes tuvo mejores resultados de clasificación correcta (91,1 %), comparado con la técnica de árbol (87,8 %) y la red neuronal (88,2 %). Conclusiones: el uso de la técnica de Naive Bayes muestra una importante exactitud para determinar la predicción de riesgo de malignidad en los pacientes con estudio citológico Bethesda IV, lo cual permitiría orientar de forma adecuada el manejo quirúrgico de los pacientes

Introduction: In the health field, each decision represents data, and data mining techniques have begun to be a promising methodology for the analysis of this information, especially in the design of predictive models. Methods: Analytical observational study; patients older than 15 years with a report of Bethesda IV after a fine needle aspiration biopsy that undergoing surgical management at the Hospital de San José in Bogotá. The data collected from those patients were included in three groups: sociodemographic-clinical information, cytology findings, and ultrasound reports. Analysis was performed using three technics: Naive Bayes, decision trees, and neural networks. Weka tool version 3.8.2 was used. Results: 195 patients out of 427, had a thyroid carcinoma pathology (45.6%). Better results were evidenced using cross-validation (10 fold) compared with a partition (66%), the Bayes technique had better results of correct classification (91.1%), than the tree technique (87.8%) and neural network (88.2%). Conclusions: The use of the Naive Bayes technique shows an important accuracy to determine the prediction of risk of malignancy in patients with a Bethesda IV cytological study, which would allow an adequate guide to the surgical management of patients.