Impact of Cement Dust on Physical and Chemical Nutrients Properties of Forest Topsoil

: The study examined the impact of Cement dust on physical and chemical nutrients properties of forest topsoil in close proximity to a major private cement industry in Obajana, Kogi State, Nigeria using standard methods by collecting Topsoil samples for physical and chemical properties analyses which are particle size, moisture content, pH, carbon, nitrogen,phosphorus, potassium, sodium, calcium, magnesium, cation exchange capacity and organic matter.Data revealed a strong influence of the particulate pollutants on the forest topsoil in close proximity to the Cement factory. It was observed that the soil properties; moisture content and soil pH varied at distances away from the factory. The result showed that the Cement dust particles entering the soil increased the pH of the soil, it more alkaline. The highest pH (6.03) was observed from hundred and fifty meters sample indicating the highest particulate pollution. There were also variations in the other soil nutrient properties; carbon, nitrogen, phosphorus, potassium, sodium, calcium, magnesium, cation exchange capacity and organic matter arising from the effect of cement dust. High organic matter content was recorded in the location samples compared with the control sample. This is attributed to the addition of cement dust to the soils, resulting in improved organic-matter cycling and plant growth. The result also showed that the chemical properties; organic carbon (OC), organic matter (OM), phosphorus (P), potassium (K), sodium (Na), calcium (Ca) and magnesium (Mg) are significantly higher in the study areas than the control. The study therefore concludes that the emission of cement dust on the forest stands over the years was found to have significantly affected the topsoil properties

Genetic studies in the nigerian population implicate an MSX1 mutation in complex oral facial clefting disorders

BACKGROUND: Orofacial clefts are the most common malformations of the head and neck with a World-wide prevalence of 1/700 births. They are commonly divided into CL(P) and CP based on anatomical, genetic and embryological findings. A Nigerian craniofacial anomalies study “NigeriaCRAN” was set up in 2006 to investigate the role of gene-environment interaction in the etiology of orofacial clefts in Nigeria. SUBJECTS AND METHODS: DNA isolated from saliva from the Nigerian probands was used for genotype association studies and direct sequencing on the cleft candidate genes: MSX1, IRF6, FOXE1, FGFR1, FGFR2, BMP4, MAFB, ABCA4, PAX7 and VAX1, and the chromosome 8q region. RESULTS: A missense mutation A34G in MSX1 was observed in nine cases and four hap map controls. No other apparent etiologic variations were identified. A deviation from HWE was observed in the cases (p= 0.00002). There was a significant difference between the affected side for unilateral CL (p=0.03) and, between bilateral clefts and clefts on either side (p=0.02). A significant gender difference was also observed for CP (p=0.008). CONCLUSIONS: The replication of a mutation previously implicated in other populations suggests a role for the MSX1 A34G variant in the etiology of CL(P)

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