Risks Associated With Tendinitis: Effects From Demographic, Socioeconomic, and Psychological Status Among Brazilian Workers

Background Self-reported tendinitis/tenosynovitis was evaluated by gender, age group, skin color, family income, and educational and psychological status. Methods The study was carried out in a representative sample of formally contracted Brazilian workers from a household survey. A total of 54,660 participants were included. Occupations were stratified according to estimated prevalences of self-reported injuries. Non-conditional logistic regression was performed, and all variables were analyzed in two occupational groups. Results The overall prevalence rate of tendinitis/tenosynovitis was 3.1%: 5.5% in high-prevalence occupations (n=10,726); and 2.5% in low-prevalence occupations (n=43,934). White female workers between the ages of 45 and 64 years and at a higher socioeconomic level were more likely to report tendinitis/tenosynovitis regardless of their occupational category. An adjusted OR = 3.59 [95% CI: 3.15-4.09] was found between tendinitis/tenosynovitis and psychological status. Conclusion Among formally contracted Brazilian workers, higher income can imply greater physical and psychological demands that, regardless of occupational stratum, increase the risk of tendinitis/tenosynovitis. Am. J. Ind. Med. 53:72-79, 2010. (C) 2009 Wiley-Liss, Inc.

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.