Removal of Cd(II) from Aqueous Solution Using Blue Pine Sawdust: Equilibrium, Kinetics and Thermodynamic Studies

The adsorption of Cd(II) ions onto sawdust has been investigated in detail as a function of equilibration time (1-120 min), dosage of adsorbent (0.025-0.5 g)/10 ml, concentration of Cd(II) (9-1780 mu M) and of nature (pH 1-10). Maximum adsorption (ca. 95.3%) is achieved from deionised water in 30 minutes at pH 7 using 0.2 g adsorbent/10 ml adsorbate solution. The adsorption data follow Langmuir, Freundlich and Dubinin-Radushkevich (D-R) isotherms over the entire range of Cd(II) ions concentration examined and their characteristic constants have also been evaluated. The variation of adsorption with temperature has yielded Delta H, Delta S and Delta G values for the 18 mu M cadmium concentration. The kinetics of adsorption obeys Morris-Weber and Lagergren equations. The first order rate constant and the intraparticle diffusion rate have also been estimated. Sawdust appears to have potential to remove Cd(II) ions from aqueous solutions at trace or subtrace concentration, to preconcentrate or treat industrial wastewater

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