Utilización de soja integral en la ración de acabado del ternero tradicional de raza rubia gallega: I. Efecto sobre la calidad de la carne

Se utilizaron 16 terneros rubios gallegos para estudiar el efecto del aca- bado con pienso, que contiene soja integral sobre el peso y las característi- cas de la canal y de la carne del ternero tradicional producido en un siste- ma de pastoreo. Se controlo el peso vivo, el peso al sacrificio, el peso canal, la conformación y el estado de engrasamiento de la canal y en la carne a 24 horas postmorten, el pH, el color L* (luminosidad) a* (índice de rojo) y b* (índice de amarillo) (CIE 1978), la composición química por espectroscopia NIRS, las pérdidas de agua por cocción, la textura midiendo la fuerza máxi- ma de corte, resistencia al corte y trabajo total y el contenido en pigmentos hemínicos. Los resultados se analizaron mediante ANOVA con el programa SPSS 8.0. No se encontraron diferencias significativas entre tratamientos en ninguno de los parámetros estudiados de peso vivo y características de la canal y de la carne. Se concluye que la sustitución de la soja desengrasada por soja integral en la ración de acabado no afecta al crecimiento y a las características de la canal y de la carne del ternero tradicional Gallego

The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants

MOTIVATION: High-throughput sequencing platforms are increasingly used to screen patients with genetic disease for pathogenic mutations, but prediction of the effects of mutations remains challenging. Previously we developed SAAPdap (Single Amino Acid Polymorphism Data Analysis Pipeline) and SAAPpred (Single Amino Acid Polymorphism Predictor) that use a combination of rule-based structural measures to predict whether a missense genetic variant is pathogenic. Here we investigate whether the same methodology can be used to develop a differential phenotype predictor, which, once a mutation has been predicted as pathogenic, is able to distinguish between phenotypes-in this case the two major clinical phenotypes (hypertrophic cardiomyopathy, HCM, and dilated cardiomyopathy, DCM) associated with mutations in the beta-myosin heavy chain (MYH7) gene product (Myosin-7). RESULTS: A random forest predictor trained on rule-based structural analyses together with structural clustering data gave a Matthews' correlation coefficient (MCC) of 0.53 (accuracy, 75%). A post hoc removal of machine learning models that performed particularly badly, increased the performance (MCC = 0.61, Acc = 79%). This proof of concept suggests that methods used for pathogenicity prediction can be extended for use in differential phenotype prediction

Subclinical Hypertrophic Cardiomyopathy in Elite Athletes: Knowledge Gaps Persist

Subclinical hypertrophic cardiomyopathy (HCM) is a phenotypic entity that has emerged from the increased use of cardiovascular magnetic resonance imaging in the evaluation and family screening of patients with HCM. We describe the case of a competitive athlete with a sarcomere gene mutation and family history of HCM who was found to exhibit the subclinical HCM phenotype on cardiovascular magnetic resonance imaging in the absence of left ventricular hypertrophy. We discuss the clinical uncertainties in her management. (Level of Difficulty: Advanced.

THE REVOLUTION, WE HAVE ALMOST OVERSLEPT

Human genome decoding and the development of relatively simple methods of sequencing made it possible to unveil genetic origin of various diseases, including cardiological. Canalopathies, cardiomyopathies, family forms of hyperlipidemia, pulmonary arterial hypertension — these diseases abroad are the indications for more profound genetic test. The article focuses on the indications for routine sequencing of new generation

Co-exposure of the organic nanomaterial fullerene C60 with benzo[a]pyrene in Danio rerio (zebrafish) hepatocytes: Evidence of toxicological interactions

Compounds from the nanotechnology industry, such as carbon-based nanomaterials, are strong candidates to contaminate aquatic environments because their production and disposal have exponentially grown in a few years. Previous evidence shows that fullerene C60, a carbon nanomaterial, can facilitate the intake of metals or PAHs both in vivo and in vitro, potentially amplifying the deleterious effects of these toxicants in organisms. The present work aimed to investigate the effects of fullerene C60 in a Danio rerio (zebrafish) hepatocyte cell lineage exposed to benzo[a]pyrene (BaP) in terms of cell viability, oxidative stress parameters and BaP intracellular accumulation. Additionally, a computational docking was performed to investigate the interaction of the fullerene C60 molecule with the detoxificatory and antioxidant enzyme πGST. Fullerene C60 provoked a significant (p 0.05) alter the enzyme activity when added to GST purified extracts from the zebrafish hepatocyte cells. These results show that fullerene C60 can increase the intake of BaP into the cells, decreasing cell viability and impairing the detoxificatory response by phase II enzymes, such as GST, and this latter effect should be occurring at the transcriptional level.Fil: Ribas Ferreira, Josencler L.. Universidade Federal do Rio Grande do Sul; BrasilFil: Lonné, María Noelia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: França, Thiago A.. Universidade Federal do Rio Grande do Sul; BrasilFil: Maximilla, Naiana R.. Universidade Federal do Rio Grande do Sul; BrasilFil: Lugokenski, Thiago H.. Universidade Federal de Santa Maria. Departamento de Química; BrasilFil: Costa, Patrícia G.. Universidade Federal do Rio Grande do Sul; BrasilFil: Fillmann, Gilberto. Universidade Federal do Rio Grande do Sul; BrasilFil: Soares, Félix A.. Universidade Federal de Santa Maria. Departamento de Química; BrasilFil: de la Torre, Fernando Roman. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Luján. Departamento de Ciencias Básicas; ArgentinaFil: Monserrat, José María. Universidade Federal do Rio Grande do Sul; Brasil. Instituto Nacional de Ciência e Tecnologia de Nanomateriais de Carbono; Brasi

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

[EN] A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 +/- 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow.This study was partially funded by two grants Catedra UPVFundacion Quaes, obtained by Eduardo Villamor Medina and Antonio Cutillas Pardines, and one FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor Medina.Villamor, E.; Monserrat Aranda, C.; Del Río, L.; Romero-Martín, J.; Rupérez Moreno, MJ. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine. 193:1-11. https://doi.org/10.1016/j.cmpb.2020.105484S111193Holt, G., Smith, R., Duncan, K., Hutchison, J. D., & Reid, D. (2009). Changes in population demographics and the future incidence of hip fracture. 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Computer Methods in Biomechanics and Biomedical Engineering, 19(2), 208-216. doi:10.1080/10255842.2015.1006209Si, H. (2015). TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator. ACM Transactions on Mathematical Software, 41(2), 1-36. doi:10.1145/2629697Yang, L., Peel, N., Clowes, J. A., McCloskey, E. V., & Eastell, R. (2009). Use of DXA-Based Structural Engineering Models of the Proximal Femur to Discriminate Hip Fracture. Journal of Bone and Mineral Research, 24(1), 33-42. doi:10.1359/jbmr.080906Schileo, E., Dall’Ara, E., Taddei, F., Malandrino, A., Schotkamp, T., Baleani, M., & Viceconti, M. (2008). An accurate estimation of bone density improves the accuracy of subject-specific finite element models. Journal of Biomechanics, 41(11), 2483-2491. doi:10.1016/j.jbiomech.2008.05.017Morgan, E. F., & Keaveny, T. M. (2001). Dependence of yield strain of human trabecular bone on anatomic site. Journal of Biomechanics, 34(5), 569-577. doi:10.1016/s0021-9290(01)00011-2Morgan, E. 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C., Schiffers, N., Pandorf, T., Radermacher, K., Weichert, D., & Forst, R. (2000). Critical evaluation of known bone material properties to realize anisotropic FE-simulation of the proximal femur. Journal of Biomechanics, 33(10), 1325-1330. doi:10.1016/s0021-9290(00)00069-5Eckstein, F., Wunderer, C., Boehm, H., Kuhn, V., Priemel, M., Link, T. M., & Lochmüller, E.-M. (2003). Reproducibility and Side Differences of Mechanical Tests for Determining the Structural Strength of the Proximal Femur. Journal of Bone and Mineral Research, 19(3), 379-385. doi:10.1359/jbmr.0301247Orwoll, E. S., Marshall, L. M., Nielson, C. M., Cummings, S. R., Lapidus, J., … Cauley, J. A. (2009). Finite Element Analysis of the Proximal Femur and Hip Fracture Risk in Older Men. Journal of Bone and Mineral Research, 24(3), 475-483. doi:10.1359/jbmr.081201Choi, W. J., Cripton, P. A., & Robinovitch, S. N. (2014). 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