Resistant to antibiotics by microbes have become a major global challenge incurring economic and public health burden. Hence, research to develop new effective anti-biotics should be a major consideration to pharmaceutical industries, scientific researchers, Amanda ‘scientific researchers and world health organizations at large. This study is conducted to investigate the antimicrobial property of proteins present in the seeds of Datura stramonium. The extraction was carried out using Tris-HCL buffer prepared from 50M Tris and 0.3M NaCL, the proteins were isolated using ammonium sulphate precipitation to obtain 80% fraction. The isolated and extracted proteins sample were subjected to dialysis in which all the salt was removed and then purified using Ion-exchange chromatography. Acidic and basic fractions of the proteins obtained were subjected to SDS-PAGE electrophoresis to visualize their different molecular weight. More protein band was observed in the basic fraction between 9-45kDa. Antibacterial activities of both acidic and basic proteins were carried out using the paper disc diffusion method against clinical bacterial isolates of E. coli and Klebsiella pneumoniae. More activity was observed in basic protein with a diameter 8mm compared to the acidic fraction of 7mm in diameter.

In this work, kinetic growth models such as Luong, Yano, Teissier-Edward, Aiba, Haldane, Monod, Han and Levenspiel were used to model molybdenum blue production from Serratia sp. strain DRY5. Based on statistical analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), and accuracy factor (AF), the Monod model was chosen as the best. The calculated values for the monod constants qmax (the maximum specific substrate degradation rate (h−1), and Ks (concentration of substrate at the half maximal degradation rate (mg/L)) were found to be 3.86 (95% confidence interval of 2.29 to 5.43), and 43.41 (95% confidence interval of 12.36 to 74.46) respectively. The novel constants discovered during the modelling exercise could be used in further secondary modelling.

One of the serious problems affecting the environment nowadays is petroleum hydrocarbon contaminations resulting from the activities in the oil and gas sector, these include: oil-spill, tank leakage, lubrication, petroleum exploitation, transportation, and services. Various techniques including mechanical and chemical methods have been employed for the bioremediation and degradation of hydrocarbons pollutants from the environments, however, some of these methods are generally expensive and may have detrimental effects on the environment, hence bioremediation is the alternative solution to hydrocarbon pollutants. Among microorganisms used in bioremediation technology nowadays, fungi are efficient, reliable, cost-effective, and environmentally friendly that can be used to cleanup and detoxify hydrocarbons contaminants from the environment viz; soil, water, and sediments. Bioremediation using fungi ensures the complete degradation and mineralization of petroleum hydrocarbon contaminants into carbon dioxide, water, inorganic compounds, and cell biomass. This review focuses on the potentials of fungi in the bioremediation of total petroleum hydrocarbons including the polycyclic aromatic hydrocarbons (PAHs). We reviewed and discussed current approaches in the bioremediation of hydrocarbon including the mechanisms of fungal bioremediation of hydrocarbon, which involves biosurfactants production and the use of fungal enzymes in the degradation of hydrocarbon pollutants. In general, fungi are more efficient and effective in the removal of hydrocarbon contaminants from the environments viz., water, soil, and sediments. However, the potentiality of fungi has not been exploited fully, hence further studies are recommended especially in the current genomic and proteomic era.

In this paper, we present different growth models such as Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in fitting and analyzing the epidemic trend of COVID-19 in the form of total number of death cases of SARS-COV-2 in The United States as of 20th of July  2020. The MMF model was found to be the best model with the highest adjusted R2 value with the lowest RMSE value. The accuracy and bias factors values were close to unity (1.0). The parameters obtained from the MMF model include maximum growth of death rate (log) of 0.048 (95% ci from 0.047 to 0.048), curve constant (d) that affects the inflection point of 2.34 (95% ci from 2.31 to 2.38) and maximal total number of death (ymax) of 151,356 (95% ci from 147,911 to 154,525). The MMF predicted that the total number of death cases for The United States on the coming 20th of August and 20th of September 2020 will be 148,183 (95% ci of 149,199 to 147,173) and 153,780 (95% ci of 152,640 to 154,928), respectively. The predictive ability of the model utilized in this study is a powerful tool for epidemiologist to monitor and assess the severity of COVID-19 in The United States in months to come. However, as with any other model, these values need to be taken with caution due to the unpredictability of the COVID-19 situation locally and globally.

In this paper, we present various growth models such as Von Bertalanffy, Baranyi-Roberts, Morgan-Mercer-Flodin (MMF), modified Richards, modified Gompertz, modified Logistics and Huang in fitting and evaluating the COVID-19 epidemic pattern as of 15 July 2020 in the form of the total number of SARS-CoV-2 deaths in Nigeria. The MMF model was found to be the best model having the highest adjusted R2 value and lowest RMSE value. The values for the Accuracy and Bias Factors were near unity (1.0). The parameters derived from the MMF model include maximum growth rate (log) of 0.02 (95% CI from 0.02 to 0.03), curve constant (d) that affects the infection point of 1.61 (95% CI from 1.42 to 1.79) and maximal total number of death cases (Ymax) of 1,717 (95% CI from 1,428 to 2,123). The model estimated that the total number of death cases for Nigeria on the coming 15th of August and 15th of September 2020 were 940 (95% CI of 847 to 1,043) and 1,101 (95% CI of 968 to 1,252), respectively. The predictive ability of the model employed in this study is a powerful tool for epidemiologist to monitor and assess the severity of COVID-19 in Nigeria in months to come. However, like any other model, these values need to be taken with caution because of the COVID-19 uncertainty situation locally and globally.