Cytokine polymorphisms in silicosis and other pneumoconioses

Silicosis and coal workers' pneumoconiosis are complex multifactorial lung diseases whose etiopathogenesis are not well defined. It is generally accepted that fibrotic lung disorders are mediated by macrophage-derived cytokines and growth factors. There is evidence showing a crucial role for tumor necrosis factor- (TNF-) and interleukin-1 (IL-1) in inflammation caused by silica dust and in the transition from simple to progressive massive fibrosis. In this review we discuss genetic polymorphisms responsible for regulating the production of these proinflammatory cytokines and their role in modifying silicosis severity.     

Enhanced nitric oxide and reactive oxygen species production and damage after inhalation of silica

In previous reports from this study, measurements of pulmonary inflammation, bronchoalveolar lavage cell cytokine production and nuclear factor-kappa B activation, cytotoxic damage, and fibrosis were detailed. In this study, we investigated the temporal relationship between silica inhalation, nitric oxide (NO), and reactive oxygen species (ROS) production, and damage mediated by these radicals in the rat. Rats were exposed to a silica aerosol (15 mg/m(3) silica, 6 h/day, 5 days/wk) for 116 days. We report time-dependent changes in 1) activation of alveolar macrophages and concomitant production of NO and ROS, 2) immunohistochemical localization of inducible NO synthase and the NO-induced damage product nitrotyrosine, 3) bronchoalveolar lavage fluid NO(x) and superoxide dismutase concentrations, and 4) lung lipid peroxidation levels. The major observations made in this study are as follows: 1) NO and ROS production and resultant damage increased during silica exposure, and 2) the sites of inducible NO synthase activation and NO-mediated damage are associated anatomically with pathological lesions in the lungs.     

Cytokine polymorphisms in silicosis and other pneumoconioses

Silicosis and coal workers' pneumoconiosis are complex multifactorial lung diseases whose etiopathogenesis are not well defined. It is generally accepted that fibrotic lung disorders are mediated by macrophage-derived cytokines and growth factors. There is evidence showing a crucial role for tumor necrosis factor-a (TNF-alpha) and interleukin-1 (IL-1) in inflammation caused by silica dust and in the transition from simple to progressive massive fibrosis. In this review we discuss genetic polymorphisms responsible for regulating the production of these proinflammatory cytokines and their role in modifying silicosis severity.     

Robbery-Related Injury in Convenience Stores: Estimating Lifetime Risk and Identifying High-Risk Populations

Robbery-related injuries constitute a major risk for convenience store workers in the United States. Studies that focus on the injury outcomes associated with convenience store robbery are extremely limited in number. This is a prospective study of 1271 convenience stores in three metropolitan areas of Virginia between February 1, 1995 and September 30, 1996. The study quantifies the lifetime risk for an occupational robbery-related injury occurrence and determines the relative importance of various types of factors in the classification of high risk stores. Lifetime risk was estimated by calculating the probability in convenience stores for having one or more employee(s) sustain at least one robbery-related injury over a range of years that a store could be in operation. Results indicate that knowledge of the circumstances of the robbery are needed to maximize the identification of high risk stores. Estimated lifetime risk reaches 567 stores with an occupational robbery-related injury occurrence per 1,000 stores in operation after 45 years. This study addresses limitations of previous research by including information on clerk resistance and the number of robbers in its analysis. These two circumstantial characteristics of robbery have been previously hypothesized to be associated with robbery-related injury.     

Identifying Populations at High Risk for Occupational Back Injury with Neutral Networks

For this study a simulation is conducted to investigate the accuracy of neural networks and logistic regression in identifying populations at high risk for occupational back injury. In contrast to most standard regression techniques, neural networks do not rely on linearity or explicitly specifying the nature of the association. Because the underlying relationships between work exposures, personal risk factors, and injury are often not well defined, neural networks may prove useful for injury risk assessment. Accuracy was assessed by comparing the injury status to the predicted level of risk in each worker. In simulations of a non-linear association, workers (used in the training data) were correctly classified 85% of the time with neural networks, 74% of the time with the main effects logistic model, and 79% of the time with the fully-specified logistic model. Using the test data, however, workers were correctly classified 67% of the time with neural networks, and 71% and 69% of the time with the main effects and fully specified logistic models, respectively. Simulations of a null association indicated that neural networks may be more likely to overfit random associations. These findings provide a valuable guide concerning statistical methodology for identifying high-risk worker populations.