El índice del derecho a la educación en instituciones educativas: un ejercicio innovador

En el marco de la aplicación del Índice del Derecho a la Educación (IDE), que el Instituto para la Investigación Educativa y el Desarrollo Pedagógico (IDEP) decidió implementar en el año 2020, se realizó una revisión de literatura que tuvo como objetivo identificar si existen otras aplicaciones de este índice en el nivel de instituciones educativas

Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease

[Abstract] Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure–Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.Universidad de Las Américas (Quito, Ecuador); BIO.TPA.20.03Instituto de Salud Carlos III; PI17/01826Xunta de Galicia; ED431C 2018/4

Diseño y construcción de un riel electromecánico para el estudio de la cinemática de imágenes con difuminación lineal uniforme

This paper presents the design and construction of an electromechanical slider that allows to obtain the instantaneous speed and acceleration of a platform that holds a scientific camera now of the capture of the image, for the study blurred images by uniform motion (Motion blur). The mechanical and electrical design requirements are presented based on the conditions of presentation of the phenomenon. Then, the system was calibrated with respect to a standard instrument for the estimation of uncertainties and errors. In this paper, the design, construction and calibration of an electromechanical system for the study of images with uniform motion blur is presented. The development of the system was divided into the following steps: electromechanical design for speed, speed calibration, electromechanical design for acceleration and the calibration of the acceleration. Maximum uncertainties of 0,031 / were obtained for speed and 0,029 /2 for acceleration of the system. The developed system corresponds to an electromechanical system that allows to move a cart along a pair of parallel bars, of low uncertainty with the possibility of measuring instantaneous speed and acceleration for the study of motion blurred images and teaching of motion physics.En este artículo se presenta el diseño y la construcción de un riel electromecánico que permite obtener la velocidad y aceleración instantáneas de una plataforma que soporta una cámara científica al momento de la captura de la imagen, para el estudio de las imágenes difuminadas por movimiento uniforme (Motion blur). Se presentan los requerimientos de diseño mecánico y eléctrico basados en las condiciones de presentación del fenómeno. Seguido, el sistema fue calibrado con respecto a un instrumento patrón para el cálculo de incertidumbres y error. En este artículo se presenta el diseño, construcción y calibración de un sistema electromecánico para el estudio de las imágenes con difuminación uniforme. El desarrollo del sistema se dividió en los siguientes pasos: diseño electromecánico para velocidad, calibración de la velocidad, diseño electromecánico para aceleración y calibración de la aceleración.Se obtuvieron incertidumbres máximas de 0,031 / para la velocidad y de 0,029 /2 para la aceleración del sistema. El sistema desarrollado corresponde a un sistema electromecánico que permite desplazar una plataforma con cámara a lo largo de un par de barras paralelas, de baja incertidumbre con la posibilidad de medir la velocidad y aceleración instantáneas para el estudio de imágenes reales con difuminadas por movimiento uniforme y la enseñanza de física básica

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19–95.25% for training and 89.22–95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.Ministerio de Economía y Competitividad; CTQ2016-74881-PMinisterio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad; UNLC13-13-3503Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Gobierno Vasco; IT1045-16Instituto de Salud Carlos III; PI17/0182

A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing

[Abstract] Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.Universidad de Las Américas (Quito, Ecuador); ENF.RCA.18.01Gobierno Vasco; IT1045-16)-2016–202

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

[Abstract] Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.Instituto Carlos III; PI17/01826Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431G/0

Prediction of Breast Cancer Proteins Involved in Immunotherapy, Metastasis, and RNA-Binding Using Molecular Descriptors and Artifcial Neural Networks

[Abstract] Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. This work is proposing accurate prediction classifer for BC proteins using six sets of protein sequence descriptors and 13 machine-learning methods. After using a univariate feature selection for the mix of fve descriptor families, the best classifer was obtained using multilayer perceptron method (artifcial neural network) and 300 features. The performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980±0.0037, and accuracy of 0.936±0.0056 (3-fold cross-validation). Regarding the prediction of 4,504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. This powerful model predicts several BC-related proteins that should be deeply studied to fnd new biomarkers and better therapeutic targets. Scripts can be downloaded at https://github.com/muntisa/ neural-networks-for-breast-cancer-proteins.This work was supported by a) Universidad UTE (Ecuador), b) the Collaborative Project in Genomic Data Integration (CICLOGEN) PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER) - “A way to build Europe”; c) the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23); d) the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union; e) the Consolidation and Structuring of Competitive Research Units - Competitive Reference Groups (ED431C 2018/49), funded by the Ministry of Education, University and Vocational Training of the Xunta de Galicia endowed with EU FEDER funds; f) research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P), Basque government (IT1045-16), and kind support of Ikerbasque, Basque Foundation for Science; and, g) Sociedad Latinoamericana de Farmacogenómica y Medicina Personalizada (SOLFAGEM)Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Gobierno Vasco; IT1045-1

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

[Abstract] Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.Instituto de Salud Carlos III; PI17/0182

Índice del derecho a la educación en colegios públicos de Bogotá

87 páginasBogotá ha sido pionera en el país en poner en el centro la garantía del derecho a la educación en sus políticas públicas, así como la transformación pedagógica para el cierre de brechas. El IDEP ha aportado al posicionamiento conceptual y político de la concepción de la educación como derecho en tanto estrategia para complejizar la comprensión de lo educativo que trasciende las discusiones entre cobertura y calidad. A través de una metodología que integra componentes cuantitativos y cualitativos, en este documento el IDEP brinda una herramienta para orientar la gestión escolar al interior de los colegios, así como la toma de decisión en política educativa en la ciudad. Esta herramienta es el Índice del Derecho a la Educación (IDE), una propuesta que se ha desarrollado internacionalmente (Rigth to Education Index) y que en Colombia ha tenido dos aproximaciones a nivel de municipio. Este documento presenta los resultados de la línea de base del IDE para Bogotá a nivel de colegio. Se encuentra que la dimensión con mayor nivel de cumplimiento es la de accesibilidad mientras que la dimensión con niveles de cumplimiento inferiores es la de aceptabilidad. Algunas recomendaciones de política apuntan a consolidar sistemas de información robustos que garanticen el monitoreo a la implementación de política y los ejercicios de rendición de cuentas, la construcción de protocolos para el uso de la información por parte de todos los actores del sistema educativo, así como perfilar acciones para garantizar el derecho a la educación desde de las decisiones de política, de gestión institucional y de aula.El objetivo principal del presente documentos es describir la metodología para la elaboración de la línea de base del Índice del Derecho a la Educación en Bogotá donde se abarcan los siguientes temas: - Revisión de la literatura y modelo de las 4As - Fuente de los datos, universo de estudio y periodo abarcado - Metodología cualitativa - Análisis descriptivo IDE y hallazgos del componente cualitativo - Recomendaciones de política y anexo con la descripción de la construcción de los indicadore