External Validation Of Equations To Estimate Resting Energy Expenditure In 14952 Adults With Overweight And Obesity And 1948 Adults With Normal Weight From Italy

Background & aims: We cross-validated 28 equations to estimate resting energy expenditure (REE) in a very large sample of adults with overweight or obesity. Methods: 14952 Caucasian men and women with overweight or obesity and 1498 with normal weight were studied. REE was measured using indirect calorimetry and estimated using two meta-regression equations and 26 other equations. The correct classification fraction (CCF) was defined as the fraction of subjects whose estimated REE was within 10% of measured REE. Results: The highest CCF was 79%, 80%, 72%, 64%, and 63% in subjects with normal weight, overweight, class 1 obesity, class 2 obesity, and class 3 obesity, respectively. The Henry weight and height and Mifflin equations performed equally well with CCFs of 77% vs. 77% for subjects with normal weight, 80% vs. 80% for those with overweight, 72% vs. 72% for those with class 1 obesity, 64% vs. 63% for those with class 2 obesity, and 61% vs. 60% for those with class 3 obesity. The Sabounchi meta-regression equations offered an improvement over the above equations only for class 3 obesity (63%). Conclusions: The accuracy of REE equations decreases with increasing values of body mass index. The Henry weight & height and Mifflin equations are similarly accurate and the Sabounchi equations offer an improvement only in subjects with class 3 obesity

External Validation of Equations to Estimate Resting Energy Expenditure in 2037 Children and Adolescents with and 389 without Obesity : a Cross-Sectional Study

We performed an external cross-validation study of 10 equations to estimate resting energy expenditure (REE) in 2037 children with and 389 without obesity. Inclusion criteria were Caucasian ethnicity, age 64 18 years, and availability of REE. REE was measured using indirect calorimetry. The correct classification fraction (CCF) of an equation was defined as the fraction of subjects whose estimated REE was within 10% of measured REE. The Moln\ue1r equation was the most accurate REE prediction equation with CCFs of 0.70 (95% CI 0.65 to 0.76) in girls without obesity, 0.64 (95% CI 0.61 to 0.66) in girls with obesity, 0.76 (95% CI 0.67 to 0.83) in boys without obesity, and 0.66 (95% CI 0.63 to 0.69) in boys with obesity. The Mifflin equation was the second most accurate equation with CCFs of 0.67 (95% CI 0.61 to 0.73) in girls without obesity, 0.61 (95% CI 0.58 to 0.64) in girls with obesity, 0.75 (95% CI 0.66 to 0.82) in boys without obesity, and 0.66 (95% CI 0.63 to 0.69) in boys with obesity

The Influence of the effect of solute on the thermodynamic driving force on grain refinement of Al alloys

Grain refinement is known to be strongly affected by the solute in cast alloys. Addition of some solute can reduce grain size considerably while others have a limited effect. This is usually attributed to the constitutional supercooling which is quantified by the growth restriction factor, Q. However, one factor that has not been considered is whether different solutes have differing effects on the thermodynamic driving force for solidification. This paper reveals that addition of solute reduces the driving force for solidification for a given undercooling, and that for a particular Q value, it is reduced more substantially when adding eutectic-forming solutes than peritectic-forming elements. Therefore, compared with the eutectic-forming solutes, addition of peritectic-forming solutes into Al alloys not only possesses a higher initial nucleation rate resulted from the larger thermodynamic driving force for solidification, but also promotes nucleation within the constitutionally supercooled zone during growth. As subsequent nucleation can occur at smaller constitutional supercoolings for peritectic-forming elements, a smaller grain size is thus produced. The very small constitutional supercooling required to trigger subsequent nucleation in alloys containing Ti is considered as a major contributor to its extraordinary grain refining efficiency in cast Al alloys even without the deliberate addition of inoculants.The Australian Research Council (ARC DP10955737)

Técnicas de inteligencia artificial aplicadas a problemas científico-tecnológicos

Cada vez es más intensivo el uso de técnicas de inteligencia artificial en la mayoría de las actividades humanas y en particular, en el área científicotecnológico. Algunas de las áreas, que muestran tanto la diversidad de campos a los que pueden aplicarse como la utilidad que pueden alcanzar, son: Bioinformática, Robótica, Tecnología de los alimentos y Recuperación de información. Una de las tareas más requeridas es la generación automática de modelos que resuman las características que debe cumplir un elemento para pertenecer a una determinada categoría, tarea que se conoce como clasificación automática. Los métodos computacionales desarrollados para tal fin forman parte de lo que se conoce como Aprendizaje Automatizado (Machine Learning). Otra tarea importante es la optimización de funciones, en las últimas décadas diferentes tipos de algoritmos de optimización han sido desarrollados para resolver una gran cantidad de problemas. El principal desafío radica en problemas que presentan una función objetivo altamente no lineal y no convexa, esto dificulta garantizar la localización del mínimo global. Por lo tanto, la necesidad de encontrar nuevas técnicas que proporcionen un mejor desempeño en este tipo de problemas sigue aún vigente y hacen de esto un campo excitante para trabajar.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informática (RedUNCI

The risk stratification of adverse neonatal outcomes in women with gestational diabetes (STRONG) study

Aims: To assess the risk of adverse neonatal outcomes in women with gestational diabetes (GDM) by identifying subgroups of women at higher risk to recognize the characteristics most associated with an excess of risk. Methods: Observational, retrospective, multicenter study involving consecutive women with GDM. To identify distinct and homogeneous subgroups of women at a higher risk, the RECursive Partitioning and AMalgamation (RECPAM) method was used. Overall, 2736 pregnancies complicated by GDM were analyzed. The main outcome measure was the occurrence of adverse neonatal outcomes in pregnancies complicated by GDM. Results: Among study participants (median age 36.8 years, pre-gestational BMI 24.8 kg/m2), six miscarriages, one neonatal death, but no maternal death was recorded. The occurrence of the cumulative adverse outcome (OR 2.48, 95% CI 1.59–3.87), large for gestational age (OR 3.99, 95% CI 2.40–6.63), fetal malformation (OR 2.66, 95% CI 1.00–7.18), and respiratory distress (OR 4.33, 95% CI 1.33–14.12) was associated with previous macrosomia. Large for gestational age was also associated with obesity (OR 1.46, 95% CI 1.00–2.15). Small for gestational age was associated with first trimester glucose levels (OR 1.96, 95% CI 1.04–3.69). Neonatal hypoglycemia was associated with overweight (OR 1.52, 95% CI 1.02–2.27) and obesity (OR 1.62, 95% CI 1.04–2.51). The RECPAM analysis identified high-risk subgroups mainly characterized by high pre-pregnancy BMI (OR 1.68, 95% CI 1.21–2.33 for obese; OR 1.38 95% CI 1.03–1.87 for overweight). Conclusions: A deep investigation on the factors associated with adverse neonatal outcomes requires a risk stratification. In particular, great attention must be paid to the prevention and treatment of obesity

Risk of obstructive sleep apnea with daytime sleepiness is associated with liver damage in non-morbidly obese patients with nonalcoholic Fatty liver disease

BACKGROUND: A high prevalence of obstructive sleep apnea syndrome (OSAS) has been reported in severely obese patients with nonalcoholic fatty liver disease (NAFLD), but few studies have evaluated OSAS in non-morbidly obese NAFLD patients. AIMS: To determine the prevalence of risk for OSAS with or without daytime sleepiness in non-morbidly obese patients with NAFLD and evaluate the association with the severity of liver damage. METHODS: We considered 159 consecutive patients with histological NAFLD and body mass index (BMI) <35 Kg/m2, and 80 controls without ultrasonographic steatosis matched for age, sex, and BMI. OSAS risk was determined by positivity for Berlin questionnaire (BQ), and daytime sleepiness by the Sleepness Epworth Scale (ESS). Liver damage was evaluated according to the NAFLD activity score. RESULTS: In NAFLD patients, BQ alone was positive in 39 (25%), ESS in 8 (5%), and both in 13 (8%, OSAS with sleepines); p\u200a=\u200ans vs. controls without steatosis. In NAFLD patients at risk for OSAS with (but not in those without) sleepiness, we observed a higher prevalence of nonalcoholic steatohepatitis (NASH; 11/13, 85% vs. 72/146, 49%; p\u200a=\u200a0.018), and of clinically significant fibrosis (stage>1; 9/13, 69% vs. 39/146, 27%; p\u200a=\u200a0.003). At multivariate logistic regression analysis, OSAS with sleepiness was strongly associated with NASH and fibrosis>1 independently of known clinical risk factors such as age, gender, BMI, diabetes, and ALT levels (OR 7.1, 95% c.i. 1.7-51, p\u200a=\u200a0.005 and OR 14.0, 95% c.i. 3.5-70, p\u200a=\u200a0.0002, respectively). CONCLUSIONS: A proportion of NAFLD patients without severe obesity is at risk for OSAS with daytime sleepiness, which is associated with the severity of liver damage independently of body mass and other cofactors

Técnicas de inteligencia artificial aplicadas a problemas de visión por computadora

El Aprendizaje Automatizado (AA) es uno de los campos científicos y técnicos de más rápido crecimiento en la actualidad. Está situado en el núcleo de la inteligencia artificial y ciencia de datos. Sus técnicas han surgido como las más elegidas para dar solución a problemas en distintas disciplinas. Una de ellas es la visión por computadora cuya finalidad es crear modelos del mundo real a partir de imágenes. Entre las tareas más importantes de esta disciplina están la detección de puntos claves faciales, el reconocimiento de emociones humanas y la detección de plagas en cultivos. El presente proyecto tiene como objetivo el desarrollo de nuevos algoritmos basados en AA que permitan realizar estas tareas en forma automática.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

An international genome-wide meta-analysis of primary biliary cholangitis: Novel risk loci and candidate drugs

Backgrounds & Aims Primary biliary cholangitis (PBC) is a chronic liver disease in which autoimmune destruction of the small intrahepatic bile ducts eventually leads to cirrhosis. Many patients have inadequate response to licensed medications, motivating the search for novel therapies. Previous genome-wide association studies (GWAS) and meta-analyses (GWMA) of PBC have identified numerous risk loci for this condition, providing insight into its aetiology. We undertook the largest GWMA of PBC to date, aiming to identify additional risk loci and prioritise candidate genes for in silico drug efficacy screening. Methods We combined new and existing genotype data for 10,516 cases and 20,772 controls from 5 European and 2 East Asian cohorts. Results We identified 56 genome-wide significant loci (20 novel) including 46 in European, 13 in Asian, and 41 in combined cohorts; and a 57th genome-wide significant locus (also novel) in conditional analysis of the European cohorts. Candidate genes at newly identified loci include FCRL3, INAVA, PRDM1, IRF7, CCR6, CD226, and IL12RB1, which each play key roles in immunity. Pathway analysis reiterated the likely importance of pattern recognition receptor and TNF signalling, JAK-STAT signalling, and differentiation of T helper (TH)1 and TH17 cells in the pathogenesis of this disease. Drug efficacy screening identified several medications predicted to be therapeutic in PBC, some of which are well-established in the treatment of other autoimmune disorders. Conclusions This study has identified additional risk loci for PBC, provided a hierarchy of agents that could be trialled in this condition, and emphasised the value of genetic and genomic approaches to drug discovery in complex disorders. Lay summary Primary biliary cholangitis (PBC) is a chronic liver disease that eventually leads to cirrhosis. In this study, we analysed genetic information from 10,516 people with PBC and 20,772 healthy individuals recruited in Canada, China, Italy, Japan, the UK, or the USA. We identified several genetic regions associated with PBC. Each of these regions contains several genes. For each region, we used diverse sources of evidence to help us choose the gene most likely to be involved in causing PBC. We used these ‘candidate genes’ to help us identify medications that are currently used for treatment of other conditions, which might also be useful for treatment of PBC

Levels of alkaline phosphatase and bilirubin are surrogate end points of outcomes of patients with primary biliary cirrhosis:an international follow-up study

Noninvasive surrogate end points of long-term outcomes of patients with primary biliary cirrhosis (PBC) are needed to monitor disease progression and evaluate potential treatments. We performed a meta-analysis of individual patient data from cohort studies to evaluate whether patients' levels of alkaline phosphatase and bilirubin correlate with their outcomes and can be used as surrogate end points. We performed a meta-analysis of data from 4845 patients included in 15 North American and European long-term follow-up cohort studies. Levels of alkaline phosphatase and bilirubin were analyzed in different settings and subpopulations at different time points relative to the clinical end point (liver transplantation or death). Of the 4845 patients, 1118 reached a clinical end point. The median follow-up period was 7.3 years; 77% survived for 10 years after study enrollment. Levels of alkaline phosphatase and bilirubin measured at study enrollment (baseline) and each year for 5 years were strongly associated with clinical outcomes (lower levels were associated with longer transplant-free survival). At 1 year after study enrollment, levels of alkaline phosphatase that were 2.0 times the upper limit of normal (ULN) best predicted patient outcome (C statistic, 0.71) but not significantly better than other thresholds. Of patients with alkaline phosphatase levels ≤ 2.0 times the ULN, 84% survived for 10 years compared with 62% of those with levels >2.0 times the ULN (P 1.0 times the ULN (P < .0001). Combining levels of alkaline phosphatase and bilirubin increased the ability to predict patient survival times. We confirmed the predictive value of alkaline phosphatase and bilirubin levels in multiple subgroups, such as patients who had not received treatment with ursodeoxycholic acid, and at different time points after study enrollment. Levels of alkaline phosphatase and bilirubin can predict outcomes (liver transplantation or death) of patients with PBC and might be used as surrogate end points in therapy trial