Elevation and cholera: an epidemiological spatial analysis of the cholera epidemic in Harare, Zimbabwe, 2008-2009

BACKGROUND: In highly populated African urban areas where access to clean water is a challenge, water source contamination is one of the most cited risk factors in a cholera epidemic. During the rainy season, where there is either no sewage disposal or working sewer system, runoff of rains follows the slopes and gets into the lower parts of towns where shallow wells could easily become contaminated by excretes. In cholera endemic areas, spatial information about topographical elevation could help to guide preventive interventions. This study aims to analyze the association between topographic elevation and the distribution of cholera cases in Harare during the cholera epidemic in 2008 and 2009. METHODS: We developed an ecological study using secondary data. First, we described attack rates by suburb and then calculated rate ratios using whole Harare as reference. We illustrated the average elevation and cholera cases by suburbs using geographical information. Finally, we estimated a generalized linear mixed model (under the assumption of a Poisson distribution) with an Empirical Bayesian approach to model the relation between the risk of cholera and the elevation in meters in Harare. We used a random intercept to allow for spatial correlation of neighboring suburbs. RESULTS: This study identifies a spatial pattern of the distribution of cholera cases in the Harare epidemic, characterized by a lower cholera risk in the highest elevation suburbs of Harare. The generalized linear mixed model showed that for each 100 meters of increase in the topographical elevation, the cholera risk was 30% lower with a rate ratio of 0.70 (95% confidence interval=0.66-0.76). Sensitivity analysis confirmed the risk reduction with an overall estimate of the rate ratio between 20% and 40%. CONCLUSION: This study highlights the importance of considering topographical elevation as a geographical and environmental risk factor in order to plan cholera preventive activities linked with water and sanitation in endemic areas. Furthermore, elevation information, among other risk factors, could help to spatially orientate cholera control interventions during an epidemic

Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

ancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer

Effect of sugar beet pulp fibre fractions on growth performance, fecal digestibility and digestive physiology in rabbits around weaning

The present study investigated the effect of the different fibre components of sugar beet pulp (SBP) on growth performance and some digestive traits. Four semi-synthetic diets were formulated with similar NDF (33% DM) and protein (16% DM) level. Control diet was formulated to contain the lowest level of soluble fibre (3% DM) and SBP diet the highest (9%). The soluble (pectins) and insoluble fractions of SBP were studied in other two diets (Pectin and InsSBP diets). A total of 136 weanling rabbits (25 d of age) was housed individually, randomly assigned to 4 experimental groups, and fed ad libitum with the experimental diets during 10 days after weaning. The type of diet did not affect growth rate and stomach pH. Animals fed with SBP diet showed higher DM and NDF digestibility (4 and 83%, respectively), gain:feed ratio (13%), cecal and total tract weight (13 and 9%) and ileal viscosity (148%) than rabbits fed the Control diet, but lower cecal pH (9%). Pectin diet increased ileal viscosity and decreased the weight of stomach content with respect to SBP diet. Rabbits fed InsSBP diet showed similar results to SBP diet but lower ileal viscosity and cecal pH than those fed Pectin diet. In conclusion, SBP and their soluble and insoluble fractions are well digested in young rabbits. However the soluble and insoluble fibre of SBP produce different effects in the gastrointestinal tract

Molecular Characterization of Growth Hormone-producing Tumors in the GC Rat Model of Acromegaly

D.A.C. was supported by the Nicolás Monardes program of the Andalusian Ministry of Health (C-0015-2014) and by a grant from the Andalusian Ministry of Science and Innovation (CTS-7478). A.S-M and A.L.C were supported by grants from the ISCIII-Subdirección General de Evaluación y Fomento de la Investigación co-funded with Fondos FEDER (PI12/0143 and PI13/02043, respectively) and the Andalusian Regional Government (CTS-444) and a grant from Pfizer Spain. R.L.C. was supported by a grant from Andalusian Ministry of Health (PI0302-2012). R.M.L. was supported by grants from Proyecto de Investigación en Salud (FIS) PI13- 00651 (funded by Instituto de Salud Carlos III), CTS-1406, PI-0639-2012, BIO-0139 (funded by Junta de Andalucía) and by Ayuda Merck Serono 2013. J. P. C. was funded by a grant (BFU2013-43282-R) from Ministerio de Economía y Competitividad. CIBER is an initiative of Instituto de Salud Carlos III, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain. J.F.M.R. is supported by the “Sara Borrell” program from the Instituto de Salud Carlos III. R.M. Luque and J.P. Castaño have received grants and lecture fees from Ipsen and Novartis. E. Venegas-Moreno and A. Soto-Moreno received grants and lecture fees from Ipsen, Novartis and Pfizer. A. Leal-Cerro received grants from Novartis and Pfizer. David Cano received a grant from Novartis

Role of Haptoglobin in Polycystic Ovary Syndrome (PCOS), Obesity and Disorders of Glucose Tolerance in Premenopausal Women

alleles of the haptoglobin α–chain polymorphism reduce the anti-oxidant properties and increase the pro-inflammatory actions of this acute-phase protein in a gene-dosage fashion. We hypothesized that the haptoglobin polymorphism might contribute to the increased oxidative stress and low-grade chronic inflammation frequently associated with polycystic ovary syndrome, obesity, and abnormalities of glucose tolerance.<0.001), yet no association was found between obesity and haptoglobin genotypes. No differences were observed in haptoglobin levels or genotype frequencies depending on glucose tolerance. Fifty percent of the variation in serum haptoglobin concentrations was explained by the variability in serum C-reactive protein concentrations, BMI, insulin sensitivity and haptoglobin genotypes. alleles suggests that the anti-oxidant and anti-inflammatory properties of haptoglobin may be reduced in these patients

The regulatory subunit of PKA-I remains partially structured and undergoes β-aggregation upon thermal denaturation

Background: The regulatory subunit (R) of cAMP-dependent protein kinase (PKA) is a modular flexible protein that responds with large conformational changes to the binding of the effector cAMP. Considering its highly dynamic nature, the protein is rather stable. We studied the thermal denaturation of full-length RIα and a truncated RIα(92-381) that contains the tandem cyclic nucleotide binding (CNB) domains A and B. Methodology/Principal Findings: As revealed by circular dichroism (CD) and differential scanning calorimetry, both RIα proteins contain significant residual structure in the heat-denatured state. As evidenced by CD, the predominantly α-helical spectrum at 25°C with double negative peaks at 209 and 222 nm changes to a spectrum with a single negative peak at 212-216 nm, characteristic of β-structure. A similar α→β transition occurs at higher temperature in the presence of cAMP. Thioflavin T fluorescence and atomic force microscopy studies support the notion that the structural transition is associated with cross-β-intermolecular aggregation and formation of non-fibrillar oligomers. Conclusions/Significance: Thermal denaturation of RIα leads to partial loss of native packing with exposure of aggregation-prone motifs, such as the B' helices in the phosphate-binding cassettes of both CNB domains. The topology of the β-sandwiches in these domains favors inter-molecular β-aggregation, which is suppressed in the ligand-bound states of RIα under physiological conditions. Moreover, our results reveal that the CNB domains persist as structural cores through heat-denaturation. © 2011 Dao et al

Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial.

The main purpose of many medical studies is to estimate the effects of a treatment or exposure on an outcome. However, it is not always possible to randomize the study participants to a particular treatment, therefore observational study designs may be used. There are major challenges with observational studies; one of which is confounding. Controlling for confounding is commonly performed by direct adjustment of measured confounders; although, sometimes this approach is suboptimal due to modeling assumptions and misspecification. Recent advances in the field of causal inference have dealt with confounding by building on classical standardization methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where new estimators were developed to overcome the limitations of the previous estimators (ie, nonparametric and parametric g-formula, inverse probability weighting, double-robust, and data-adaptive estimators). We illustrate the implementation of different methods using an empirical example from the Connors study based on intensive care medicine, and most importantly, we provide reproducible and commented code in Stata, R, and Python for researchers to adapt in their own observational study. The code can be accessed at https://github.com/migariane/Tutorial_Computational_Causal_Inference_Estimators

High risk for obstructive sleep apnea and other sleep disorders among overweight and obese pregnant women

Background: Obstructive sleep apnea (OSA), a common and serious disorder in which breathing repeatedly stops during sleep, is associated with excess weight and obesity. Little is known about the co-occurrence of OSA among pregnant women from low and middle-income countries. Methods: We examined the extent to which maternal pre-pregnancy overweight or obesity status are associated with high risk for OSA, poor sleep quality, and excessive daytime sleepiness in 1032 pregnant women in Lima, Peru. The Berlin questionnaire was used to identify women at high risk for OSA. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were used to examine sleep quality and excessive daytime sleepiness, respectively. Multinomial logistic regression procedures were employed to estimate odds ratios (aOR) and 95 % confidence intervals (CI) adjusted for putative confounding factors. Results: Compared with lean women (<25 kg/m2), overweight women (25–29.9 kg/m2) had 3.69-fold higher odds of high risk for OSA (95 % CI 1.82–7.50). The corresponding aOR for obese women (≥30 kg/m2) was 13.23 (95 % CI: 6.25–28.01). Obese women, as compared with their lean counterparts had a 1.61-fold higher odds of poor sleep quality (95 % CI: 1.00–2.63). Conclusion: Overweight or obese pregnant women have increased odds of sleep disorders, particularly OSA. OSA screening and risk management may be indicated among pregnant women in low and middle income countries, particularly those undergoing rapid epidemiologic transitions characterized by increased prevalence of excessive adult weight gain

The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study

Background: This paper examines the development of the Connected Health research landscape with a view on providing a historical perspective on existing Connected Health research. Connected Health has become a rapidly growing research field as our healthcare system is facing pressured to become more proactive and patient centred. Objective: We aimed to identify the extent and coverage of the current body of knowledge in Connected Health. With this, we want to identify which topics have drawn the attention of Connected health researchers, and if there are gaps or interdisciplinary opportunities for further research. Methods: We used a systematic mapping study that combines scientific contributions from research on medicine, business, computer science and engineering. We analyse the papers with seven classification criteria, publication source, publication year, research types, empirical types, contribution types research topic and the condition studied in the paper. Results: Altogether, our search resulted in 208 papers which were analysed by a multidisciplinary group of researchers. Our results indicate a slow start for Connected Health research but a more recent steady upswing since 2013. The majority of papers proposed healthcare solutions (37%) or evaluated Connected Health approaches (23%). Case studies (28%) and experiments (26%) were the most popular forms of scientific validation employed. Diabetes, cancer, multiple sclerosis, and heart conditions are among the most prevalent conditions studied. Conclusions: We conclude that Connected Health research seems to be an established field of research, which has been growing strongly during the last five years. There seems to be more focus on technology driven research with a strong contribution from medicine, but business aspects of Connected health are not as much studied