Cardiovascular drug use and differences in the incidence of cardiovascular mortality in elderly Seriban men

Objective:To assess whether the difference in risk of cardiovascular mortality between urban and rural areas of Serbia could be explained by differences in the use of cardiovascular medication. Methods: The Serbian cohorts of the Seven Countries Study, Velika Krsna (VK), Zrenjanin (ZR) and Belgrade (BG), were enrolled in 1962-1964 and were followed up for 25 years. The survivors of these cohorts were re-examined in 1987, 1988 and 1989, respectively. This second examination of elderly men aged 65 to 84 years included a questionnaire about current use of cardiovascular medication, risk factors and diseases and a physical examination. All subjects were followed until death or the predefined censor date (10 years after baseline). The Cox proportional hazards model was used to calculate the risk of cardiovascular mortality in the rural cohorts compared to the urban cohort and to adjust for confounding. Main outcome measure: Cardiovascular death. Results: A total of 227 men from VK, 184 men from ZR and 287 men from BG were followed for a mean duration of 7.4 years and was complete for all subjects. After exclusion of 13 subjects with missing medication data, the incidences of cardiovascular mortality in VK, ZR, and BG were 60, 74, and 26 per 1000 person-years, respectively. The prevalence of cardiovascular medication use was 38% in VK, 52% in ZR, and 59% in BG. The greatest difference in use of specific medication was observed for betablockers (0% in VK and ZR, 13% in BG). After adjustment for cardiovascular risk factors, diseases and age, the relative risks (RRs) of cardiovascular mortality were 2.12 [95% CI: 1.44¿3.12], and 2.27 [95% CI: 1.56¿3.30] in VK, and ZR compared to BG. Additional adjustment for the use of cardiovascular medication increased these RRs to 2.40 [95% CI: 1.61¿3.60] and 2.55 [95% CI: 1.72¿3.78], respectively. Conclusion:The variation in cardiovascular medication use could not explain the excess risk of mortality in the rural Serbian cohorts compared to urban Belgrade

Proteomics as a quality control tool of pharmaceutical probiotic bacterial lysate products

Probiotic bacteria have a wide range of applications in veterinary and human therapeutics. Inactivated probiotics are complex samples and quality control (QC) should measure as many molecular features as possible. Capillary electrophoresis coupled to mass spectrometry (CE/MS) has been used as a multidimensional and high throughput method for the identification and validation of biomarkers of disease in complex biological samples such as biofluids. In this study we evaluate the suitability of CE/MS to measure the consistency of different lots of the probiotic formulation Pro-Symbioflor which is a bacterial lysate of heat-inactivated Escherichia coli and Enterococcus faecalis. Over 5000 peptides were detected by CE/MS in 5 different lots of the bacterial lysate and in a sample of culture medium. 71 to 75% of the total peptide content was identical in all lots. This percentage increased to 87–89% when allowing the absence of a peptide in one of the 5 samples. These results, based on over 2000 peptides, suggest high similarity of the 5 different lots. Sequence analysis identified peptides of both E. coli and E. faecalis and peptides originating from the culture medium, thus confirming the presence of the strains in the formulation. Ontology analysis suggested that the majority of the peptides identified for E. coli originated from the cell membrane or the fimbrium, while peptides identified for E. faecalis were enriched for peptides originating from the cytoplasm. The bacterial lysate peptides as a whole are recognised as highly conserved molecular patterns by the innate immune system as microbe associated molecular pattern (MAMP). Sequence analysis also identified the presence of soybean, yeast and casein protein fragments that are part of the formulation of the culture medium. In conclusion CE/MS seems an appropriate QC tool to analyze complex biological products such as inactivated probiotic formulations and allows determining the similarity between lots

CSBN: a hybrid approach for survival time prediction with missing data

CSBN: A Hybrid Approach For Survival TimePrediction With Missing DataSimon Rabinowicz1, Raphaela Butz2,3, Arjen Hommersom3,4, and Matt Williams5,61Faculty Of Medicine, Imperial College London, UK2Institute for Computer Science, TH K ̈oln, Germany3Department of Computer Science, Open University of the Netherlands4Department of Software Science, Radboud University, The Netherlands5Department of Radiotherapy, Charing Cross Hospital, London, UK6Computational Oncology Laboratory, Imperial College London, UKAbstract.Survival prediction models most commonly use Cox Proportional Hazards (CPH) models, and are frequently used in medical statistics and clinical practice. However, such models underperform when the predictor variables are missing. By building Bayesian networks we automatically construct a model with the most important risk factors and relationships between risk factors and Bayesian networks are able to infer the likely values of missing data. We therefore propose a hybrid solution, consisting of a CPH model and a BN, where the predictive variables in the CPH model are the child nodes of a BN, which we call CSBN. We learn the CPH and BN models separately, using standard techniques, with the only constraint being that the variables that are predictors in the CPH model are child nodes in the BN. This allows us to fuse the two models, using the predictors of the CPH models as the join points. We test our approach by examining the performance of the CPH model, against the hybrid CSBN model, using both complete data cases and in cases with missing data. We calculate the performance of the survival prediction for both CPH and CSBN using the C-index and a normalised error function as metrics. For the CPH model, predictive error was significantly larger for missing data (±3120.8 days) compared to complete data (±1171.5 days;p= 3.6e−07). This was also true for the CSBN±1387.3 days for missing data compared with±1171.5 days with complete data (p= 0.01568). However, with missing data, the predictive error was significantly larger for the CPH model (±3120.8 days) than theCSBN (±1171.5 days;p= 0.03274). In conclusion the CSBN methodology provides a more effective method of predicting survival when using incomplete data

Selective Hydrogenation of Methylacetylne (MA) and Propadiene (PD) present in the C3 Naphtha Cut

DelftChemTechApplied Science

Evolutionary intelligence in asphalt pavement modeling and quality-of-information

he analysis and development of a novel approach to asphalt pavement modeling, able to attend the need to predict failure according to technical and non- technical criteria in a highway, is a hard task, namely in terms of the huge amount of possible scenarios. Indeed, the current state-of-the-art for service-life prediction is at empiric and empiric-mechanistic levels, and do not provide any suitable answer even for one single failure criteria. Consequently, it is imperative to achieve qualified models and qualitative reasoning methods, in particular due to the need to have first-class environments at our disposal where defective information is at hand. In order to fulfill this goal, this paper presents a dynamic and formal model oriented to fulfill the task of making predictions for multi-failure criteria, in particular in scenarios with incomplete information; it is an intelligence tool that advances according to the Quality-of- Information of the extensions of the predicates that model the universe of discourse. On the other hand, it is also considered the Degree-of-Confidence factor, a parameter that measures one`s confidence on the list of characteristics presented by an asphalt pavement, set in terms of the attributes or variables that make the argument of the predicates referred to above.The authors would like to thank the Foundation of Science and Technology (FCT), Portugal, for financial support received under the contract UTAustin/CA/0012/2008

An adverse event reporting and learning system for water sector based on an extension of the Eindhoven classification model

The International Water Association and the World Health Organization has promoted, worldwide, the implementation of Water Safety Plans (WSPs) to ensure, consistently and systematically the water quality for human consumption. In order to complement and potentiate the WSPs, this work presents an adverse event reporting and learning system that may help to prevent hazards and risks. The proposed framework will allow for automatic knowledge extraction and report generation, in order to identify the most relevant causes of error. It will cater for the delineation of advance strategies to problem accomplishment, concluding about the impact, place of occurrence, form or type of event recorded with respect to the entities that operate in the water sector. To respond to this challenge the Eindhoven Classification Model was extended and adapted to the water industry, and used to classify the root causes of adverse events. Logic programming was used as a knowledge representation and reasoning mechanism, allow ng one to model the universe of discourse in terms of defective data, information and knowledge, and its embedded quality, that enables a direct study of the event’s root causes. Other approaches to address specific issues of water industry, presented in literature, do not consider the problem from a perspective of having to deal with incomplete, unknown, contradictory or even forbidden data, information or knowledge, and their conclusions are not object of a formal proof. Here it is not only presented a solution to the problem, but also a proof that the solution(s) is (are) the only one(s).(undefined)info:eu-repo/semantics/publishedVersio