Kinetics and tissue distribution of bismuth, tin and lead after implantation of miniature shotgun alloy pellets in rats.

INTRODUCTION: Shotgun pellets containing bismuth (Bi) as substitute for lead (Pb) are increasingly being used due to environmental concerns. Information on toxicokinetics of Bi is lacking for the assessment of humans accidentally shot by Bi-containing shotgun alloy pellets. METHODS: Male Wistar rats were exposed to miniature alloy pellets containing Bi, tin (Sn) and minor amounts of Pb by implantation in muscle tissues of the hind legs. RESULTS: The concentrations of Bi in whole blood and urine increased up to 53 weeks after implantation. The highest concentrations of Sn in whole blood were observed three weeks after implantation, then declining to background levels 53 weeks after implantation. Lead in whole blood increased up to 13 weeks of exposure, and declined for the remaining observation period. Bismuth and Sn accumulated mainly in kidney, but also in liver, testicle and brain. Analytical field emission scanning electron microscopy of post-implant pellets showed depletion of Pb towards the pellet surface. Oxygen and chlorine accumulated in Sn rich lamellas in areas next to the pellet surface. The distribution of Bi remained visually unaffected as compared to pre-implant pellets. CONCLUSION: The concentration of Bi increased during the whole observation period in blood, urine, kidney, brain, testicle and liver. The decline in the concentrations of Pb and Sn in blood and urine after reaching the peak concentration may be related to alterations in the chemical composition and element distribution of the implanted alloy pellets.

Characterizing Attention with Predictive Network Models.

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction.