0000000000246857
AUTHOR
Virginia Pérez-doñate
showing 5 related works from this author
Microesferas de ácido poliláctico marcadas con 166Ho. Una alternativa frente a las de 90Y en el tratamiento del carcinoma hepático mediante radioembo…
2021
Los tumores hepáticos constituyen un importante problema de salud a nivel mundial que en multitud de ocasiones va asociado a patologías previas y factores de riesgo como las hepatitis víricas B y C, el consumo excesivo de alcohol y el aumento de casos de esteatohepatitis no alcohólica, cada vez más relevante en los países industrializados.En hepatocarcinomas no susceptibles de resección quirúrgica, la braquiterapia se está mostrando muy eficaz frente a la quimioterapia sistémica y transarterial, por lo que se desarrollan nuevos tratamientos locorregionales mínimamente invasivos y con menor toxicidad.La radioembolización hepática es una forma de braquiterapia consistente en la administración…
Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree
2021
Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation p…
Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.
2019
Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals, assayed against promastigotes of Leishmania amazonensis, is used to develop the the…
Isolation and characterization of extracellular vesicles in Candida albicans
2020
Background : The occurrence of systemic infections due to C. albicans has increased especially in critically ill patients. In fungal infections, secretory mechanisms are key events for disease establishment. Recent findings demonstrate that fungal organisms release many molecular components to the extracellular space in extracellular vesicles. Aims: We develop a method to obtain exosomes from yeast cultures of the Candida albicans . Methods : Yeast strains used in this work were C. albicans SC5314, C. parapsilosis (ATCC 22019) and C. krusei (ATCC 6258). Yeasts were grown at 37.º in liquid YPD medium. The cell cultures were centrifuged and the supernatant filtered through sterile nitrocellul…
Prediction of potential environmental toxicity of chemicals in <em>Lactuca sativa</em> seed germination using computational tools
2019
The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of phytotoxicity effects of chemical compounds on the Lactuca sativa seeds germination. A database of 73 compounds, assayed against L. sativa and Dragon’s molecular descriptors are used to obtain a QSAR model for the prediction of the phytotoxicity. The model is carried out with QSARINS software and validated according to OECD principles. The best model showed good value for the determination coefficient (R2 = 0.917) and others parameters appropriate for fitting (s = 0.256 and RMSEtr= 0.236). The validation results confirmed that the model has good robustness and stability …