Implementación en hardware de un SVPWM en un Soft-Core Nios II. Parte I

Este artículo expone la implementación en hardware de una modulación por ancho de pulsos en un espacio vectorial en un soft-core embebido Nios II. La utilización de dispositivos reconfigurables otorga flexibilidad en el diseño y otras mejoras en términos de consumo de potencia con la ayuda de herramientas de software que permiten hacer más eficientes los algoritmos. Se presenta la configuración de un procesador embebido Nios II en su modo estándar implementado en hardware sobre una FPGA Cyclone II de la familia de Altera, una de las ventajas del soft-core embebido Nios II es que podemos efectuar cálculos que contengan números del tipo flotante, esto nos sirve para realizar las operaciones trigonométricas requeridas por el algoritmo de modulación por ancho de pulsos en un espacio vectorial. El algoritmo es descrito en lenguaje C++ mediante una aplicación software también de la familia de Altera. La solución nos brinda buena precisión en los cálculos matemáticos y entre los resultados obtenidos se muestra la gráfica de ocupación del dispositivo Cyclone II para la implementación del soft-core embebido, así como tabla de tiempos medidos en simulaciones con modelsim y tabla de valores correspondientes a las seis salidas del SVPWM realizado en el Nios II y que se leyeron con ayuda de un analizador lógico en tiempo real.

Use of STAT6 Phosphorylation Inhibitor and Trimethylglycine as New Adjuvant Therapies for 5-Fluorouracil in Colitis-Associated Tumorigenesis

Colorectal cancer (CRC) is one of the most widespread and deadly types of neoplasia around the world, where the inflammatory microenvironment has critical importance in the process of tumor growth, metastasis, and drug resistance. Despite its limited effectiveness, 5-fluorouracil (5-FU) is the main drug utilized for CRC treatment. The combination of 5-FU with other agents modestly increases its effectiveness in patients. Here, we evaluated the anti-inflammatory Trimethylglycine and the Signal transducer and activator of transcription (STAT6) inhibitor AS1517499, as possible adjuvants to 5-FU in already established cancers, using a model of colitis-associated colon cancer (CAC). We found that these adjuvant therapies induced a remarkable reduction of tumor growth when administrated together with 5-FU, correlating with a reduction in STAT6-phosphorylation. This reduction upgraded the effect of 5-FU by increasing both levels of apoptosis and markers of cell adhesion such as E-cadherin, whereas decreased epithelial–mesenchymal transition markers were associated with aggressive phenotypes and drug resistance, such as β-catenin nuclear translocation and Zinc finger protein SNAI1 (SNAI1). Additionally, Il-10, Tgf-β, and Il-17a, critical pro-tumorigenic cytokines, were downmodulated in the colon by these adjuvant therapies. In vitro assays on human colon cancer cells showed that Trimethylglycine also reduced STAT6-phosphorylation. Our study is relatively unique in focusing on the effects of the combined administration of AS1517499 and Trimethylglycine together with 5-FU on already established CAC which synergizes to markedly reduce the colon tumor load. Together, these data point to STAT6 as a valuable target for adjuvant therapy in colon cancer

Clinical prediction models for mortality in patients with covid-19:external validation and individual participant data meta-analysis

OBJECTIVE: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. DESIGN: Two stage individual participant data meta-analysis. SETTING: Secondary and tertiary care. PARTICIPANTS: 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. DATA SOURCES: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge. MODEL SELECTION AND ELIGIBILITY CRITERIA: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. METHODS: Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. MAIN OUTCOME MEASURES: 30 day mortality or in-hospital mortality. RESULTS: Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al’s model (0.96, 0.59 to 1.55, 0.21 to 4.28). CONCLUSION: The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care