Adipose Tissue Dysfunction Signals Progression of Hepatic Steatosis Towards Nonalcoholic Steatohepatitis in C57Bl/6 Mice

OBJECTIVE - Nonalcoholic fatty liver disease (NAFLD) is linked to obesity and diabetes, suggesting an important role of adipose tissue in the pathogenesis of NAFLD. Here, we aimed to investigate the interaction between adipose tissue and liver in NAFLD and identify potential early plasma markers that predict nonalcoholic steatohepatitis (NASH). RESEARCH DESIGN AND METHODS - C57Bl/6 mice were chronically fed a high-fat diet to induce NAFLD and compared with mice fed a low-fat diet. Extensive histological and phenotypical analyses coupled with a time course study of plasma proteins using multiplex assay were performed. RESULTS - Mice exhibited pronounced heterogeneity in liver histological scoring, leading to classification into four subgroups: low-fat low (LFL) responders displaying normal liver morphology, low-fat high (LFH) responders showing benign hepatic steatosis, high-fat low (HFL) responders displaying pre-NASH with macrovesicular lipid droplets, and high fat high (HFH) responders exhibiting overt NASH characterized by ballooning of hepatocytes, presence of Mallory bodies, and activated inflammatory cells. Compared with HFL responders, HFH mice gained weight more rapidly and exhibited adipose tissue dysfunction characterized by decreased final fat mass, enhanced macrophage infiltration and inflammation, and adipose tissue remodeling. Plasma haptoglobin, IL-1β, TIMP-1, adiponectin, and leptin were significantly changed in HFH mice. Multivariate analysis indicated that in addition to leptin, plasma CRP, haptoglobin, eotaxin, and MIP-1α early in the intervention were positively associated with liver triglycerides. Intermediate prognostic markers of liver triglycerides included IL-18, IL-1β, MIP-1γ, and MIP-2, whereas insulin, TIMP-1, granulocyte chemotactic protein 2, and myeloperoxidase emerged as late markers. CONCLUSIONS - Our data support the existence of a tight relationship between adipose tissue dysfunction and NASH pathogenesis and point to several novel potential predictive biomarkers for NASH

Geostatistical Models for the Prediction of Water Supply Network Failures in Bogotá, Integrating Machine Learning Algorithms

[EN] Currently new strategies of spatial referencing, data analysis, and machine learning methods are integrated with Geographical Information Systems (GISs) to understand specific characteristics and water supply dynamics. This work explores the variables that can cause spacial failures and potential risk areas with application to a zone in the Bogotá water supply network. Machine learning algorithms are proposed to generate prediction models and potential failure maps. A sensitivity analysis was held to identify the model with the best fit for the estimation. This study will allow water supply decisions makers to focalize their efforts in the field.[ES] Actualmente se buscan nuevas estrategias y/o metodologías basadas en la integración de los Sistemas de Información Geográfica (SIGs) como forma de georeferenciacion espacial y visualización de las variables analizadas, junto con métodos de aprendizaje automático (Machine Learning) que permitan entender características puntuales, variables influyentes y dinámicas de los sistemas de abastecimiento de agua potable.En este trabajo se hace la identificación espacial de los fallos y zonas potenciales de riesgo que se presentan en una zona de la red de abastecimiento de Bogotá, explorando las variables que puedan tener mayor incidencia en los mismos. Se propone el uso de algoritmos de aprendizaje automático para la generación de modelos de predicción y la elaboración de mapas de fallos potenciales, identificando, a través de un análisis de sensibilidad, cuál de estos modelos presenta un mejor ajuste en la estimación. Este estudio permite a los gestores del abastecimiento una localización precisa y eficiente de los fallos en la red, apoyando el proceso de toma de decisiones.Navarrete-López, CF.; Calderón-Rivera, D.; Díaz Arévalo, JL.; Herrera Fernández, AM.; Izquierdo Sebastián, J. (2018). Modelos geoestadísticos para la predicción de fallos de una zona de la red de abastecimiento de agua de Bogotá, integrando algoritmos de Machine Learning. Social Science Research Network. 1-8. https://doi.org/10.2139/ssrn.3113048S1

the prevention of chronic diseases through ehealth a practical overview

Disease prevention is an umbrella term embracing individual-based or population-based interventions aimed at preventing the manifestation of diseases (primary prevention), reducing the impact of a disease that has arisen (secondary prevention), or mitigating the impact of an ongoing illness (tertiary prevention). Digital health has the potential to improve prevention of chronic diseases. Its application ranges from effective mHealth weight-loss intervention to prevent or delay the onset of diabetes in overweight adults to the cost-effective intervention on the provision of mental-health care via mobile-based or Internet-based programs to reduce the incidence or the severity of anxiety. The present contribution focuses on the effectiveness of eHealth preventive interventions and on the role of digital health in improving health promotion and disease prevention. We also give a practical overview on how eHealth interventions have been effectively implemented, developed, and delivered for the primary, secondary, and tertiary prevention of chronic diseases

Effects of Once-Weekly Exenatide on Cardiovascular Outcomes in Type 2 Diabetes.

Abstract BACKGROUND: The cardiovascular effects of adding once-weekly treatment with exenatide to usual care in patients with type 2 diabetes are unknown. METHODS: We randomly assigned patients with type 2 diabetes, with or without previous cardiovascular disease, to receive subcutaneous injections of extended-release exenatide at a dose of 2 mg or matching placebo once weekly. The primary composite outcome was the first occurrence of death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. The coprimary hypotheses were that exenatide, administered once weekly, would be noninferior to placebo with respect to safety and superior to placebo with respect to efficacy. RESULTS: In all, 14,752 patients (of whom 10,782 [73.1%] had previous cardiovascular disease) were followed for a median of 3.2 years (interquartile range, 2.2 to 4.4). A primary composite outcome event occurred in 839 of 7356 patients (11.4%; 3.7 events per 100 person-years) in the exenatide group and in 905 of 7396 patients (12.2%; 4.0 events per 100 person-years) in the placebo group (hazard ratio, 0.91; 95% confidence interval [CI], 0.83 to 1.00), with the intention-to-treat analysis indicating that exenatide, administered once weekly, was noninferior to placebo with respect to safety (P<0.001 for noninferiority) but was not superior to placebo with respect to efficacy (P=0.06 for superiority). The rates of death from cardiovascular causes, fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, hospitalization for heart failure, and hospitalization for acute coronary syndrome, and the incidence of acute pancreatitis, pancreatic cancer, medullary thyroid carcinoma, and serious adverse events did not differ significantly between the two groups. CONCLUSIONS: Among patients with type 2 diabetes with or without previous cardiovascular disease, the incidence of major adverse cardiovascular events did not differ significantly between patients who received exenatide and those who received placebo. (Funded by Amylin Pharmaceuticals; EXSCEL ClinicalTrials.gov number, NCT01144338 .)

Nonlinear process monitoring using bottle-neck neural networks

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