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In this study, we present a boronizing treatment on a steel microalloy that was performed according to dehydrated paste-pack boriding. The temperatures conducted were at 1173, 1223 and 1273 K; at various exposure times of 1, 3, 6 and 9 h. As a result of the boriding process, diffusion-controlled growth of the FeB/Fe2B layers was obtained at the surface of the micro-alloy steel, and the kinetics of the growth process changed parabolically over time. The results of these examination properties of the boride layer as revealed by Optical Microscopy (OP) showed the morphology of the boride layer as a saw-tooth with a thickness ranging from 33 μm to 220 μm depending on the boronizing time. The analysis of Scanning Electron Microscopy (SEM-EDS); showed a distribution of the alloying elements that were detected by Energy Dispersive Spectrometry. The X-ray diffraction (XRD) technique indicated that the surface was a mixture of FeB and Fe2B borides. The evaluation of adhesion of the layers was determined by the technique of Rockwell-C hardness. Young’s modulus and hardness of the layer were evaluated by a nanoindentation technique with a load of 250 mN. The paste dehydrate boriding of micro-alloy reveal a change of properties on the surface; also the coatings FeB and Fe2B, to make a sacrificial function in the steels micro alloyed as widely used in pipelines transporting oil.

El desgaste de los insertos de Polietileno de Ultra-Alto Peso Molecular (UHMWPE pos sus siglas en inglés) continúa afectando la longevidad de las prótesis totales de rodilla (PTR) junto con el aflojamiento aséptico, y ambos constituyen las dos principales causas de falla de las prótesis. Considerando esto, es necesario encontrar soluciones adecuadas para evitar el desgaste excesivo y hasta la ruptura de los insertos de polietileno. En este trabajo se realizó el estudio mediante simulación numérica de una ELTP Scorpio II[R] Stryker[R], la cual se retiró por desgaste del inserto de UHMWPE en el Hospital 1 de Octubre del ISSSTE en México. Se utilizaron las hipótesis de Bartel et al. (1995) y Chillag et al. (1991) para la validación del método numérico utilizado, las cuales establecen que el desgaste del polietileno puede reducirse utilizando insertos tibiales de mayor espesor, lo cual disminuye las presiones de contacto. Los análisis se realizaron mediante MEF variando el espesor del inserto de 6, 8, 10, 12 y 14 mm, suponiendo cargas axiales de tipo cuasi-estático en la articulación a cero grados de flexión, para 1.33 veces el peso de un individuo de 75 kg (736 N) empleando el ciclo normalizado de marcha. Los resultados obtenidos muestran similitud con los reportados por Bei et al. (2004) y Deen et al. (2006). Después de validar el método, se desarrolló el modelo de MEF de la PTR y se determinaron las curvas de esfuerzo y de áreas de contacto del inserto de UHMWPE, con lo que se obtuvo información importante para modificar el diseño y obtener una prótesis de geometría conforme en los planos coronal y sagital del inserto femoral y el inserto de polietileno, de acuerdo con el fenotipo mexicano.

The International Staging System (ISS) and the Revised International Staging System (R-ISS) are commonly used prognostic scores in multiple myeloma (MM). These methods have significant gaps, particularly among intermediate-risk groups. The aim of this study was to improve risk stratification in newly diagnosed MM patients using data from three different trials developed by the Spanish Myeloma Group. For this, we applied an unsupervised machine learning clusterization technique on a set of clinical, biochemical and cytogenetic variables, and we identified two novel clusters of patients with significantly different survival. The prognostic precision of this clusterization was superior to those of ISS and R-ISS scores, and appeared to be particularly useful to improve risk stratification among R-ISS 2 patients. Additionally, patients assigned to the low-risk cluster in the GEM05 over 65 years trial had a significant survival benefit when treated with VMP as compared with VTD. In conclusion, we describe a simple prognostic model for newly diagnosed MM whose predictions are independent of the ISS and R-ISS scores. Notably, the model is particularly useful in order to re-classify R-ISS score 2 patients in 2 different prognostic subgroups. The combination of ISS, R-ISS and unsupervised machine learning clusterization brings a promising approximation to improve MM risk stratification.