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Item type:Publication, Phytochemical Characterization and In Vitro Biological Activities of Macleania rupestris or Ericaceae: Insights into Nutraceutical Potential(MDPI AG, 2025-10-31) ;Arianna Mayorga-Ramos ;Rebeca Gonzalez-Pastor ;Juan A. Puente-Pineda ;Carlos Barba-OstriaEduardo TejeraThe Ericaceae family encompasses several berries with recognized health-promoting properties; however, Macleania rupestris, a neotropical species endemic to the Andean region, remains poorly characterized. Background/Objectives: This study aimed to identify the chemical composition of M. rupestris ethanolic extracts and evaluate their biological activities, including antitumoral, hemolytic, anti-inflammatory, and leishmanicidal effects. Methods: The M. rupestris ethanolic extracts were obtained from lyophilized fruits and analyzed by HPLC-MS/MS for phytochemical profiling. Bioactivities were assessed in vitro using tumor and non-tumor cell lines (MTT assay), erythrocyte hemolysis assays, RAW 264.7 macrophage inflammation models, and Leishmania mexicana promastigotes. Results: The chemical analysis revealed anthocyanins (cyanidin-3-glucoside, malvidin-3-glucoside, petunidin-3-glucoside, delphinidin-3-arabinoside), flavonols (quercetin and myricetin derivatives), and coumaroyl iridoids. The extract showed modest antiproliferative activity (IC50 10.4–22.5 mg/mL) across tumor cell lines with low therapeutic indices, indicating limited selectivity. In contrast, hemolytic activity was negligible (<5% at all tested concentrations), suggesting high biocompatibility. Anti-inflammatory assays indicated a dose-dependent reduction in nitric oxide (NO) production, while no significant leishmanicidal activity was detected. Conclusions: This study provides the first comprehensive evaluation of the previously listed M. rupestris bioactivities. While its antitumoral effects appear limited, its strong hemocompatibility and presence of antioxidant metabolites highlight its potential for biomedical and nutraceutical applications where biocompatibility is critical. Further studies are needed to optimize bioactivity and explore potential synergistic effects. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the Multifaceted Biological Activities of Anthocyanins Isolated from Two Andean Berries(MDPI AG, 2024-08-21) ;Carlos Barba-Ostria ;Saskya E. Carrera-Pacheco; ; Natural pigments extracted from plant species are used in foods, cosmetics, and pharmaceuticals. This study evaluates the comprehensive biological activities of anthocyanins isolated from Andean blueberry (Vaccinium floribundum Kunth) and Andean blackberry (Rubus glaucus Benth), focusing on their antimicrobial, antioxidant, antitumoral, anti-inflammatory, and hemolytic properties. Chemical characterization revealed significant anthocyanin content with complex mass spectrometric profiles indicating diverse glycosylation patterns that may influence their bioactivity. The antimicrobial assays showed that the extracts were particularly effective against Gram-positive bacteria, with minimal inhibitory concentrations (MICs) as low as 1 mg/mL for Rubus glaucus, indicating strong potential for therapeutic use. The antioxidant capacity of the berries was substantial, albeit slightly lower than that of ascorbic acid. The extracts also exhibited notable antitumoral activity in various cancer cell lines, showing promise as adjunctive or preventive treatments. The anti-inflammatory effects were confirmed by inhibiting nitric oxide production in macrophage cells, highlighting their potential in managing inflammatory diseases. In terms of hemolytic activity, Rubus glaucus exhibited dose-dependent effects, potentially attributable to anthocyanins and phenolics, while Vaccinium floribundum demonstrated no significant hemolytic activity, underscoring its safety. These findings suggest that anthocyanins from Andean berries possess potent biological activities, which could be leveraged for health benefits in pharmaceutical and nutraceutical applications. Further studies are needed to isolate specific bioactive compounds and investigate their synergistic effects in clinical and real-world contexts. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Consensus strategy in genes prioritization and combined bioinformatics analysis for preeclampsia pathogenesis(Springer Science and Business Media LLC, 2017-08-08) ;Eduardo Tejera ;Maykel Cruz-Monteagudo ;Germán Burgos ;María-Eugenia SánchezAminael Sánchez-Rodríguez - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Breast Cancer Risk Associated with Genotype Polymorphisms of the Aurora Kinase a Gene (AURKA): a Case-Control Study in a High Altitude Ecuadorian Mestizo Population(Springer Science and Business Media LLC, 2017-06-24) ;Andrés López-Cortés ;Alejandro Cabrera-Andrade ;Fabián Oña-Cisneros ;Felipe RosalesMalena Ortiz - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Erratum to: Breast Cancer Risk Associated with Genotype Polymorphisms of the Aurora Kinase a Gene (AURKA): a Case-Control Study in a High Altitude Ecuadorian Mestizo Population(Springer Science and Business Media LLC, 2017-09-05) ;Andrés López-Cortés ;Alejandro Cabrera-Andrade ;Fabián Oña-Cisneros ;Carolina EcheverríaFelipe Rosales - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis(Springer Science and Business Media LLC, 2018-11-12) ;Andrés López-Cortés; ;Alejandro Cabrera-Andrade ;Stephen J. BarigyeCristian R. Munteanu<jats:title>Abstract</jats:title><jats:p>Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis(MDPI AG, 2020-02-05) ;Alejandro Cabrera-Andrade ;Andrés López-Cortés ;Gabriela Jaramillo-Koupermann; Yunierkis Pérez-Castillo<jats:p>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.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing(MDPI AG, 2020-11-22) ;Alejandro Cabrera-Andrade ;Andrés López-Cortés ;Gabriela Jaramillo-Koupermann ;Humberto González-DíazAlejandro Pazos<jats:p>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.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks(Springer Science and Business Media LLC, 2020-05-22) ;Andrés López-Cortés ;Alejandro Cabrera-Andrade ;José M. Vázquez-Naya ;Alejandro PazosHumberto González-Díaz<jats:title>Abstract</jats:title><jats:p>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 classifier 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 five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial 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 find new biomarkers and better therapeutic targets. Scripts can be downloaded at<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/muntisa/neural-networks-for-breast-cancer-proteins">https://github.com/muntisa/neural-networks-for-breast-cancer-proteins</jats:ext-link>.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds(American Chemical Society (ACS), 2020-10-15) ;Alejandro Cabrera-Andrade ;Andrés López-Cortés ;Cristian R. Munteanu ;Alejandro PazosYunierkis Pérez-Castillo
