984 resultados para Probability models
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BACKGROUND Several evidences indicate that gut microbiota is involved in the control of host energy metabolism. OBJECTIVE To evaluate the differences in the composition of gut microbiota in rat models under different nutritional status and physical activity and to identify their associations with serum leptin and ghrelin levels. METHODS IN A CASE CONTROL STUDY, FORTY MALE RATS WERE RANDOMLY ASSIGNED TO ONE OF THESE FOUR EXPERIMENTAL GROUPS: ABA group with food restriction and free access to exercise; control ABA group with food restriction and no access to exercise; exercise group with free access to exercise and feed ad libitum and ad libitum group without access to exercise and feed ad libitum. The fecal bacteria composition was investigated by PCR-denaturing gradient gel electrophoresis and real-time qPCR. RESULTS In restricted eaters, we have found a significant increase in the number of Proteobacteria, Bacteroides, Clostridium, Enterococcus, Prevotella and M. smithii and a significant decrease in the quantities of Actinobacteria, Firmicutes, Bacteroidetes, B. coccoides-E. rectale group, Lactobacillus and Bifidobacterium with respect to unrestricted eaters. Moreover, a significant increase in the number of Lactobacillus, Bifidobacterium and B. coccoides-E. rectale group was observed in exercise group with respect to the rest of groups. We also found a significant positive correlation between the quantity of Bifidobacterium and Lactobacillus and serum leptin levels, and a significant and negative correlation among the number of Clostridium, Bacteroides and Prevotella and serum leptin levels in all experimental groups. Furthermore, serum ghrelin levels were negatively correlated with the quantity of Bifidobacterium, Lactobacillus and B. coccoides-Eubacterium rectale group and positively correlated with the number of Bacteroides and Prevotella. CONCLUSIONS Nutritional status and physical activity alter gut microbiota composition affecting the diversity and similarity. This study highlights the associations between gut microbiota and appetite-regulating hormones that may be important in terms of satiety and host metabolism.
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A variety of host immunogenetic factors appear to influence both an individual's susceptibility to infection with Mycobacterium leprae and the pathologic course of the disease. Animal models can contribute to a better understanding of the role of immunogenetics in leprosy through comparative studies helping to confirm the significance of various identified traits and in deciphering the underlying mechanisms that may be involved in expression of different disease related phenotypes. Genetically engineered mice, with specific immune or biochemical pathway defects, are particularly useful for investigating granuloma formation and resistance to infection and are shedding new light on borderline areas of the leprosy spectrum which are clinically unstable and have a tendency toward immunological complications. Though armadillos are less developed in this regard, these animals are the only other natural hosts of M. leprae and they present a unique opportunity for comparative study of genetic markers and mechanisms associable with disease susceptibility or resistance, especially the neurological aspects of leprosy. In this paper, we review the recent contributions of genetically engineered mice and armadillos toward our understanding of the immunogenetics of leprosy.
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Given the significant impact the use of glucocorticoids can have on fracture risk independent of bone density, their use has been incorporated as one of the clinical risk factors for calculating the 10-year fracture risk in the World Health Organization's Fracture Risk Assessment Tool (FRAX(®)). Like the other clinical risk factors, the use of glucocorticoids is included as a dichotomous variable with use of steroids defined as past or present exposure of 3 months or more of use of a daily dose of 5 mg or more of prednisolone or equivalent. The purpose of this report is to give clinicians guidance on adjustments which should be made to the 10-year risk based on the dose, duration of use and mode of delivery of glucocorticoids preparations. A subcommittee of the International Society for Clinical Densitometry and International Osteoporosis Foundation joint Position Development Conference presented its findings to an expert panel and the following recommendations were selected. 1) There is a dose relationship between glucocorticoid use of greater than 3 months and fracture risk. The average dose exposure captured within FRAX(®) is likely to be a prednisone dose of 2.5-7.5 mg/day or its equivalent. Fracture probability is under-estimated when prednisone dose is greater than 7.5 mg/day and is over-estimated when the prednisone dose is less than 2.5 mg/day. 2) Frequent intermittent use of higher doses of glucocorticoids increases fracture risk. Because of the variability in dose and dosing schedule, quantification of this risk is not possible. 3) High dose inhaled glucocorticoids may be a risk factor for fracture. FRAX(®) may underestimate fracture probability in users of high dose inhaled glucocorticoids. 4) Appropriate glucocorticoid replacement in individuals with adrenal insufficiency has not been found to increase fracture risk. In such patients, use of glucocorticoids should not be included in FRAX(®) calculations.
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Although the relationship between personality and depressive illness is complex (Shea, 2005), there is empirical evidence that some personality features such as neuroticism, harm avoidance, introversion, dependency, self-criticism or perfectionism are related to depressive illness risk (Gunderson et al. 1999).
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BACKGROUND The effect of the macronutrient composition of the usual diet on long term weight maintenance remains controversial. METHODS 373,803 subjects aged 25-70 years were recruited in 10 European countries (1992-2000) in the PANACEA project of the EPIC cohort. Diet was assessed at baseline using country-specific validated questionnaires and weight and height were measured at baseline and self-reported at follow-up in most centers. The association between weight change after 5 years of follow-up and the iso-energetic replacement of 5% of energy from one macronutrient by 5% of energy from another macronutrient was assessed using multivariate linear mixed-models. The risk of becoming overweight or obese after 5 years was investigated using multivariate Poisson regressions stratified according to initial Body Mass Index. RESULTS A higher proportion of energy from fat at the expense of carbohydrates was not significantly associated with weight change after 5 years. However, a higher proportion of energy from protein at the expense of fat was positively associated with weight gain. A higher proportion of energy from protein at the expense of carbohydrates was also positively associated with weight gain, especially when carbohydrates were rich in fibre. The association between percentage of energy from protein and weight change was slightly stronger in overweight participants, former smokers, participants ≥60 years old, participants underreporting their energy intake and participants with a prudent dietary pattern. Compared to diets with no more than 14% of energy from protein, diets with more than 22% of energy from protein were associated with a 23-24% higher risk of becoming overweight or obese in normal weight and overweight subjects at baseline. CONCLUSION Our results show that participants consuming an amount of protein above the protein intake recommended by the American Diabetes Association may experience a higher risk of becoming overweight or obese during adult life.
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BACKGROUND Identifying individuals at high risk of excess weight gain may help targeting prevention efforts at those at risk of various metabolic diseases associated with weight gain. Our aim was to develop a risk score to identify these individuals and validate it in an external population. METHODS We used lifestyle and nutritional data from 53°758 individuals followed for a median of 5.4 years from six centers of the European Prospective Investigation into Cancer and Nutrition (EPIC) to develop a risk score to predict substantial weight gain (SWG) for the next 5 years (derivation sample). Assuming linear weight gain, SWG was defined as gaining ≥ 10% of baseline weight during follow-up. Proportional hazards models were used to identify significant predictors of SWG separately by EPIC center. Regression coefficients of predictors were pooled using random-effects meta-analysis. Pooled coefficients were used to assign weights to each predictor. The risk score was calculated as a linear combination of the predictors. External validity of the score was evaluated in nine other centers of the EPIC study (validation sample). RESULTS Our final model included age, sex, baseline weight, level of education, baseline smoking, sports activity, alcohol use, and intake of six food groups. The model's discriminatory ability measured by the area under a receiver operating characteristic curve was 0.64 (95% CI = 0.63-0.65) in the derivation sample and 0.57 (95% CI = 0.56-0.58) in the validation sample, with variation between centers. Positive and negative predictive values for the optimal cut-off value of ≥ 200 points were 9% and 96%, respectively. CONCLUSION The present risk score confidently excluded a large proportion of individuals from being at any appreciable risk to develop SWG within the next 5 years. Future studies, however, may attempt to further refine the positive prediction of the score.
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BACKGROUND Observational studies implicate higher dietary energy density (DED) as a potential risk factor for weight gain and obesity. It has been hypothesized that DED may also be associated with risk of type 2 diabetes (T2D), but limited evidence exists. Therefore, we investigated the association between DED and risk of T2D in a large prospective study with heterogeneity of dietary intake. METHODOLOGY/PRINCIPAL FINDINGS A case-cohort study was nested within the European Prospective Investigation into Cancer (EPIC) study of 340,234 participants contributing 3.99 million person years of follow-up, identifying 12,403 incident diabetes cases and a random subcohort of 16,835 individuals from 8 European countries. DED was calculated as energy (kcal) from foods (except beverages) divided by the weight (gram) of foods estimated from dietary questionnaires. Prentice-weighted Cox proportional hazard regression models were fitted by country. Risk estimates were pooled by random effects meta-analysis and heterogeneity was evaluated. Estimated mean (sd) DED was 1.5 (0.3) kcal/g among cases and subcohort members, varying across countries (range 1.4-1.7 kcal/g). After adjustment for age, sex, smoking, physical activity, alcohol intake, energy intake from beverages and misreporting of dietary intake, no association was observed between DED and T2D (HR 1.02 (95% CI: 0.93-1.13), which was consistent across countries (I(2) = 2.9%). CONCLUSIONS/SIGNIFICANCE In this large European case-cohort study no association between DED of solid and semi-solid foods and risk of T2D was observed. However, despite the fact that there currently is no conclusive evidence for an association between DED and T2DM risk, choosing low energy dense foods should be promoted as they support current WHO recommendations to prevent chronic diseases.
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Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.
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In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to 'explain' an observed set of alleles. The other procedure is probabilistic using Bayes' theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.
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BACKGROUND The purpose of this study was to assess the incidence of neurological complications in patients with infective endocarditis, the risk factors for their development, their influence on the clinical outcome, and the impact of cardiac surgery. METHODS AND RESULTS This was a retrospective analysis of prospectively collected data on a multicenter cohort of 1345 consecutive episodes of left-sided infective endocarditis from 8 centers in Spain. Cox regression models were developed to analyze variables predictive of neurological complications and associated mortality. Three hundred forty patients (25%) experienced such complications: 192 patients (14%) had ischemic events, 86 (6%) had encephalopathy/meningitis, 60 (4%) had hemorrhages, and 2 (1%) had brain abscesses. Independent risk factors associated with all neurological complications were vegetation size ≥3 cm (hazard ratio [HR] 1.91), Staphylococcus aureus as a cause (HR 2.47), mitral valve involvement (HR 1.29), and anticoagulant therapy (HR 1.31). This last variable was particularly related to a greater incidence of hemorrhagic events (HR 2.71). Overall mortality was 30%, and neurological complications had a negative impact on outcome (45% of deaths versus 24% in patients without these complications; P<0.01), although only moderate to severe ischemic stroke (HR 1.63) and brain hemorrhage (HR 1.73) were significantly associated with a poorer prognosis. Antimicrobial treatment reduced (by 33% to 75%) the risk of neurological complications. In patients with hemorrhage, mortality was higher when surgery was performed within 4 weeks of the hemorrhagic event (75% versus 40% in later surgery). CONCLUSIONS Moderate to severe ischemic stroke and brain hemorrhage were found to have a significant negative impact on the outcome of infective endocarditis. Early appropriate antimicrobial treatment is critical, and transitory discontinuation of anticoagulant therapy should be considered.
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Background Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable. In order to use multiple logistic regression, many studies have simulated "pseudo-absences" through a number of strategies, but it is unknown how the choice of strategy influences models and their geographic predictions of species. In this paper we evaluate the effect of several prevailing pseudo-absence strategies on the predictions of the geographic distribution of a virtual species whose "true" distribution and relationship to three environmental predictors was predefined. We evaluated the effect of using a) real absences b) pseudo-absences selected randomly from the background and c) two-step approaches: pseudo-absences selected from low suitability areas predicted by either Ecological Niche Factor Analysis: (ENFA) or BIOCLIM. We compared how the choice of pseudo-absence strategy affected model fit, predictive power, and information-theoretic model selection results. Results Models built with true absences had the best predictive power, best discriminatory power, and the "true" model (the one that contained the correct predictors) was supported by the data according to AIC, as expected. Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97), and the "true" model was also supported by the data. Models based on two-step approaches had intermediate fit, the lowest predictive power, and the "true" model was not supported by the data. Conclusion If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach. However, the resulting models can be expected to have limited fit.
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Heart tissue inflammation, progressive fibrosis and electrocardiographic alterations occur in approximately 30% of patients infected by Trypanosoma cruzi, 10-30 years after infection. Further, plasma levels of tumour necrosis factor (TNF) and nitric oxide (NO) are associated with the degree of heart dysfunction in chronic chagasic cardiomyopathy (CCC). Thus, our aim was to establish experimental models that mimic a range of parasitological, pathological and cardiac alterations described in patients with chronic Chagas’ heart disease and evaluate whether heart disease severity was associated with increased TNF and NO levels in the serum. Our results show that C3H/He mice chronically infected with the Colombian T. cruzi strain have more severe cardiac parasitism and inflammation than C57BL/6 mice. In addition, connexin 43 disorganisation and fibronectin deposition in the heart tissue, increased levels of creatine kinase cardiac MB isoenzyme activity in the serum and more severe electrical abnormalities were observed in T. cruzi-infected C3H/He mice compared to C57BL/6 mice. Therefore, T. cruzi-infected C3H/He and C57BL/6 mice represent severe and mild models of CCC, respectively. Moreover, the CCC severity paralleled the TNF and NO levels in the serum. Therefore, these models are appropriate for studying the pathophysiology and biomarkers of CCC progression, as well as for testing therapeutic agents for patients with Chagas’ heart disease.
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Public providers have no financial incentive to respect their legal obligation to exempt the poor from user fees. Health Equity Funds (HEFs) aim to make exemptions effective by giving NGOs responsibility for assessing eligibility and compensating providers for lost revenue. We use the geographic spread of HEFs over time in Cambodia to identify their impact on out-of-pocket (OOP) payments. Among households with some OOP payment, HEFs reduce the amount paid by 35%, on average. The effect is larger for households that are poorer and mainly use public health care. Reimbursement of providers through a government operated scheme also reduces household OOP payments but the effect is not as well targeted on the poor. Both compensation models raise household non-medical consumption but have no impact on health-related debt. HEFs reduce the probability of primarily seeking care in the private sector.
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Data on biliary carriage of bacteria and, specifically, of bacteria with worrisome and unexpected resistance traits (URB) are lacking. A prospective study (April 2010 to December 2011) was performed that included all patients admitted for <48 h for elective laparoscopic cholecystectomy in a Spanish hospital. Bile samples were cultured and epidemiological/clinical data recorded. Logistic regression models (stepwise) were performed using bactobilia or bactobilia by URB as dependent variables. Models (P < 0.001) showing the highest R(2) values were considered. A total of 198 patients (40.4% males; age, 55.3 ± 17.3 years) were included. Bactobilia was found in 44 of them (22.2%). The presence of bactobilia was associated (R(2) Cox, 0.30) with previous biliary endoscopic retrograde cholangiopancreatography (ERCP) (odds ratio [OR], 8.95; 95% confidence interval [CI], 2.96 to 27.06; P < 0.001), previous admission (OR, 2.82; 95% CI, 1.10 to 7.24; P = 0.031), and age (OR, 1.09 per year; 95% CI, 1.05 to 1.12; P < 0.001). Ten out of the 44 (22.7%) patients with bactobilia carried URB: 1 Escherichia coli isolate (CTX-M), 1 Klebsiella pneumoniae isolate (OXA-48), 3 high-level gentamicin-resistant enterococci, 1 vancomycin-resistant Enterococcus isolate, 3 Enterobacter cloacae strains, and 1 imipenem-resistant Pseudomonas aeruginosa strain. Bactobilia by URB (versus those by non-URB) was only associated (R(2) Cox, 0.19) with previous ERCP (OR, 11.11; 95% CI, 1.98 to 62.47; P = 0.006). For analyses of patients with bactobilia by URB versus the remaining patients, previous ERCP (OR, 35.284; 95% CI, 5.320 to 234.016; P < 0.001), previous intake of antibiotics (OR, 7.200; 95% CI, 0.962 to 53.906; P = 0.050), and age (OR, 1.113 per year of age; 95% CI, 1.028 to 1.206; P = 0.009) were associated with bactobilia by URB (R(2) Cox, 0.19; P < 0.001). Previous antibiotic exposure (in addition to age and previous ERCP) was a risk driver for bactobilia by URB. This may have implications in prophylactic/therapeutic measures.
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Aquest és un estudi retrospectiu que compara la mobilitat i el conflicto escàpulo-humeral entre 2 models diferents de pròtesi invertida d’espatlla. Aquestes pròtesis s’han implantat en pacients amb ruptures del manegot dels rotadors irreparables. Aquesta cirugía no està exenta de complicacions, i una de les més habituals és el conflicto escàpulo-humeral o notch.