Serum n-6 polyunsaturated fatty acids and risk of atrial fibrillation: the Kuopio Ischaemic Heart Disease Risk Factor Study

Purpose N-6 polyunsaturated fatty acids (PUFA), particularly linoleic acid (LA), have been associated with lower risk of coronary heart disease (CHD), but little is known about their antiarrhythmic properties. We investigated the association of the serum n-6 PUFAs with the risk of atrial fibrillation (AF), the most common type of cardiac arrhythmia. Methods The study included 2450 men from the Kuopio Ischaemic Heart Disease Risk Factor Study, aged 42–60 years at baseline. The total n-6 PUFA includes linoleic acid (LA), arachidonic acid (AA), γ-linolenic acid (GLA) and dihomo-γ-linolenic acid (DGLA). Cox proportional hazards regression was used to estimate hazard ratio (HR) of incident events. Results During the mean follow-up of 22.4 years, 486 AF cases occurred. The multivariable-adjusted HR in the highest versus the lowest quartile of total serum n-6 PUFA concentration was 0.79 (95% CI 0.58–1.08, P trend = 0.04). When evaluated individually, only serum LA concentration was inversely associated with AF risk (multivariable-adjusted extreme-quartile HR 0.69, 95% CI 0.51–0.94, P trend = 0.02). These associations were stronger among the men without history of CHD or congestive heart failure at baseline, compared to men with such disease history (P for interaction = 0.05 for total n-6 PUFA and LA). Similar associations were observed with dietary LA and AA intakes. No significant associations were observed with serum AA, GLA or DGLA concentrations. Conclusions Higher circulating concentration and dietary intake of n-6 PUFA, mainly LA, are associated with lower risk of AF, especially among men without history of CHD or congestive heart failure.

RESISTANCE TO VIBRATION OF WOOD TEMPERATURE POLYMER COMPOSITE MATERIAL

In this work, a wood composite material is considered, which incorporates all the best properties of the known wood polymer composites: water resistance, moisture resistance, high bending strength. It was concluded that according to the criterion of resistance to temperature fluctuations, it is possible to give more complete recommendations on the optimal composition of the wood polymer composite material based on a two-factor study. To ensure comparability of optimization results for a number of other properties, a series of computer experiments was carried out according to the same plan as for the study of compression resistance, that is, the concentration of wood and at the same time the concentration of sand was changed from 10 to 50 % with a step of 10 %, respectively. The material has acquired new advantages, which do not have known analogues – the possibility of using any wood waste, higher strength due to the content of sand in the composition, cheaper production due to the fact that most of the components of the material are household and wood waste, namely waste from the woodworking and forestry industries in the form of chips, sawdust, chips, lump waste, as well as sand, which is a publicly available and inexpensive material.

Post-Analysis of Predictive Modeling with an Epidemiological Example

Post-analysis of predictive models fosters their application in practice, as domain experts want to understand the logic behind them. In epidemiology, methods explaining sophisticated models facilitate the usage of up-to-date tools, especially in the high-dimensional predictor space. Investigating how model performance varies for subjects with different conditions is one of the important parts of post-analysis. This paper presents a model-independent approach for post-analysis, aiming to reveal those subjects’ conditions that lead to low or high model performance, compared to the average level on the whole sample. Conditions of interest are presented in the form of rules generated by a multi-objective evolutionary algorithm (MOGA). In this study, Lasso logistic regression (LLR) was trained to predict cardiovascular death by 2016 using the data from the 1984–1989 examination within the Kuopio Ischemic Heart Disease Risk Factor Study (KIHD), which contained 2682 subjects and 950 preselected predictors. After 50 independent runs of five-fold cross-validation, the model performance collected for each subject was used to generate rules describing “easy” and “difficult” cases. LLR with 61 selected predictors, on average, achieved 72.53% accuracy on the whole sample. However, during post-analysis, three categories of subjects were discovered: “Easy” cases with an LLR accuracy of 95.84%, “difficult” cases with an LLR accuracy of 48.11%, and the remaining cases with an LLR accuracy of 71.00%. Moreover, the rule analysis showed that medication was one of the main confusing factors that led to lower model performance. The proposed approach provides insightful information about subjects’ conditions that complicate predictive modeling.

Anxiety towards Statistics and Its Relationship with Students’ Attitudes and Learning Approach

Many university students have difficulties when facing statistics related tasks, leading to an increase in their levels of anxiety and poor performance. Researchers have identified negative attitudes towards statistics, which have been shaped through students’ secondary education experience, as a major driver for their failure. In this study we want to uncover the causal recipes of attitudes leading to high and low levels of anxiety in secondary education students, and the role that the learning approach plays in these relationships. We used fuzzy sets comparative qualitative analysis (fsQCA) in a sample of 325 students surveyed on the multifactorial scale of attitudes toward statistics (MSATS) and the revised two factor study process questionnaire (R-SPQ-2F). The results indicate that, respectively, a high or a low level of self-confidence is the most important and a sufficient condition by itself for achieving a low or a high level of anxiety, while the learning approaches and other attitudes are only present in other causal combinations that represent a small number of cases.

Key Factor Study for Generic Long-Acting PLGA Microspheres Based on a Reverse Engineering of Vivitrol®

The FDA (U.S. Food and Drug Administration) has approved only a negligible number of poly(lactide-co-glycolide) (PLGA)-based microsphere formulations, indicating the difficulty in developing a PLGA microsphere. A thorough understanding of microsphere formulations is essential to meet the challenge of developing innovative or generic microspheres. In this study, the key factors, especially the key process factors of the marketed PLGA microspheres, were revealed for the first time via a reverse engineering study on Vivitrol® and verified by the development of a generic naltrexone-loaded microsphere (GNM). Qualitative and quantitative similarity with Vivitrol®, in terms of inactive ingredients, was accomplished by the determination of PLGA. Physicochemical characterization of Vivitrol® helped to identify the critical process parameters in each manufacturing step. After being prepared according to the process parameters revealed by reverse engineering, the GNM demonstrated similarity to Vivitrol® in terms of quality attributes and in vitro release (f2 = 65.3). The research on the development of bioequivalent microspheres based on the similar technology of Vivitrol® will benefit the development of other generic or innovative microspheres.