Perspective Chapter: The Toxic Silver (Hg)

In the late 1950s, residents of a Japanese fishing village known as “Minamata” began falling ill and dying at an alarming rate. The Japanese authorities stated that methyl-mercury-rich seafood and shellfish caused the sickness. Burning fossil fuels represent ≈52.7% of Hg emissions. The majorities of mercury’s compounds are volatile and thus travel hundreds of miles with wind before being deposited on the earth’s surface. High acidity and dissolved organic carbon increase Hg-mobility in soil to enter the food chain. Additionally, Hg is taken up by areal plant parts via gas exchange. Mercury has no identified role in plants while exhibiting high affinity to form complexes with soft ligands such as sulfur and this consequently inactivates amino acids and sulfur-containing antioxidants. Long-term human exposure to Hg leads to neurotoxicity in children and adults, immunological, cardiac, and motor reproductive and genetic disorders. Accordingly, remediating contaminated soils has become an obligation. Mercury, like other potentially toxic elements, is not biodegradable, and therefore, its remediation should encompass either removal of Hg from soils or even its immobilization. This chapter discusses Hg’s chemical behavior, sources, health dangers, and soil remediation methods to lower Hg levels

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran