Resuscitation of Bacteria after Dechlorination of Treated Drinking Water

In this study, the effects of initial bacterial load (102 , 104 and 106 cells/ml) and dechlorination idiosyncrasy on the efficacy of varying concentrations (0.018–0.126% v/v applied for 30 minutes) of a point-of-use chlorine-based proprietary disinfectant (WaterCare®) were investigated in drinking water. Results indicated that higher initial cell populations significantly affected the efficacy of the disinfectant.Subsequent dechlorination of the disinfected water resulted in resuscitation of bacteria. This was demonstrated after 1440 minutes post-dechlorination for Salmonella typhi and after at least 120 minutes for Escherichia coli, Proteus vulgaris and Shigella dysenteriae, particularly at lower disinfectant concentrations (0.018–0.054% v/v). Thus, whilst the use of WaterCare® at the recommended dosage(0.072% v/v or 10 ml to 25 litres of clean water for 30 minutes) may produce microbiologically safe drinking water high initial microbial load and post-disinfection residual chlorine loss may limit its efficacy. Keywords: Bacterial resuscitation, Escherichia coli, Dechlorination, Drinking water, Salmonella typhi, WaterCare

Fungal Airsporal Contamination of Different Hospital Environments in Lagos, Nigeria

Hospital infections caused by fungi have been frequently reported in hospitalized patients, with a high morbidity and mortality, making it increasingly important to the awareness of air quality. This study is designed to isolate, identify and investigate the occurrence of airborne fungal spores present in various working environments of some selected hospitals within Lagos State, Nigeria. Air samples at six working area (reception, out-patient, male ward, female ward, maternity ward and operating theatre) of the selected hospitals were obtained on triplicate of Sabouraud dextrose agar plates, every week days for six consecutive weeks. Isolated fungi were subsequently identified using both microscopic and macroscopic characteristics. Seven species of fungi belonging to three genera were recovered during this study period; Aspergillus niger, A. flavus, A. fumigatus, A. species, Candida species, Penicillium citrinum and P. species. The most abundant fungus encountered in this study is A. niger with P. species being the least isolated fungus from all the investigated hospitals. Of the six working areas sampled in the eight hospitals, the reception and out-patient areas appeared to record the highest occurrence of encountered fungi while the examined operating theatre rooms had the least fungal airsporal contaminant. It was gathered from this study that despite the daily cleaning of the investigated hospital working environments with different antiseptic and disinfectants, fungal contaminant was observed throughout the sampled period. We thereby conclude that the hospital environments are continuously monitored in order to identify and eliminate the sources of these fungal spores

Modelling and prediction of water current using artificial neural networks: A case study of the commodore channel

Water current modelling and prediction techniques along coastal inlets have attracted growing concern in recent years. This is largely so because water current component continues to be a major contributor to movement of sediments, tracers and pollutants, and to a whole range of offshore applications in engineering, environmental observations, exploration and oceanography. However, most research works are lacking adequate methods for developing precise prediction models along the commodore channel in Lagos State. This research work presents water current prediction using Artificial Neural Networks (ANNs). The Back Propagation (BP) technique with feed forward architecture and optimized training algorithm known as Levenbergq-Marquardt was used to develop a Neural Network Water Current Prediction model-(NNWLM) in a MATLAB programming environment. It was passed through model sensitivity analysis and afterwards tested with data from the Commodore channel (Lagos Lagoon). The result revealed prediction accuracy ranging from 0.012 to 0.045 in terms of Mean Square Error (MSE) and 0.80 to 0.83 in terms of correlation coefficient (R-value). With this high performance, the Neural network developed in this work can be used as a veritable tool for water current prediction along the Commodore channel and in extension a wide variety of coastal engineering and development, covering sediment management program: dredging, sand bypassing, beach-contingency plans, and protection of beaches vulnerable to storm erosion and monitoring and prediction of long-term water current variations in coastal inlets. Keywords: Artificial Neural Network, Commodore Channel, Coastal Inlet, Water Current, Back Propagation

Nosocomial infections and the challenges of control in developing countries.

Nosocomial infection is a recognized public health problem world-wide with a prevalence rate of 3.0-20.7% and an incidence rate of 5-10%. It has become increasingly obvious that infections acquired in the hospital lead to increased morbidity and mortality which has added noticeably to economic burden. However, after about three decades of nosocomial infection surveillance and control world-wide, it still remains an important problem for hospitals today. Studies have shown that most hospitals in developing countries especially Africa, have no effective infection control programme due to lack of awareness of the problem, lack of personnel, poor water supply, erratic electricity supply, ineffective antibiotic policies with emergence of multiply antibiotic resistant microbes, poor laboratory backup, poor funding and non-adherence to safe practices by health workers. It is recommended that the cost of hospital infection control programme should be included in the health budget of the country and fund allocated for the infection control committee for routine control purposes and to bear the cost of outbreaks. There is need for adequate staffing and continuous education of staff on the principles of infection control, especially hand washing which is the single most important effective measure to reduce the risks of cross infection.