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Approximately 60 % of men globally over the age of 50 experience a diminished quality of life as a result of benign prostatic hyperplasia (BPH). Consequently, while the national direct cost for managing BPH is hovering around $4 billion in the USA, the individual direct cost for medications is estimated at $1536 and $425 annually for the USA and Ghana, respectively. Due to the chemotherapeutic drawbacks in BPH treatment, a concerted effort is urgently needed to find new chemotypes with novel mechanism of action to combat the disease. This research aims to employ computational techniques to identify new compounds from ethno-pharmacological plants from Ghana for treating BPH. Altogether, 250 natural products from Croton membranaceus, Heliotropium angiospermum, Annona muricata, Serenoa repens, Cissus quadrangularis, Tribulus terrestrials, Vernonia amagdalina and Momordica foetida from Ghanaian origin were virtually screened against 5alpha reductase 2 (5aR2), a primary drug target implicated in the parthenogenesis of BPH. Out of the 254 compounds docked, three compounds (A, B and C) showed a binding energy lower than testosterone (-10.4 kcal/mol), the main substrate of the target and comparative to finasteride (-11.7 kcal/mol), one of the drugs currently used for treating the disease. Molecular docking and dynamics simulations studies revealed Leu224 to be critical for ligand binding and complex stability. The pharmacological and physicochemical profiles show the compounds to possess good pharmacodynamics with negligible toxicities. While the compounds A, C, G, H, I and J were, respectively, predicted to treat prostate disorders with Pa (0.909, 0.221, 0.743, 0.729, 0.978, and 0.443) and Pi (0.003, 0.132, 0.005,0.005, 0.002, 0.016), that of A, B, D, G, H, I, and J were found to be associated with the inhibition of 5alpha reductase with Pa (0.840, 0.102, 0.097, 0.377, 0.123, 0.909, and 0.201) and Pi (0.001, 0.075, 0.089, 0.003, 0.036, 0.001, and 0.004), respectively. These lead-like compounds possessing plausible chemotherapeutic moieties with putative 5aR2 inhibitory potential need further experimental validation via in vitro studies.

Background/aim: Patients with co-morbid hypertension and diabetes may take several medicines. Although multiple medications may aid in the management of their condition, they also raise the likelihood of drug interactions and unfavorable drug reactions. This study therefore evaluated polypharmacy and its associated factors in patients with co-morbid hypertension and diabetes. Methodology: This was a cross-sectional study with data collection spanning a 3-months period. Patients attending their review clinic in a municipal hospital in Ghana were interviewed with a pre-tested semi-structured questionnaire to collect data on socio-demographic characteristics, prescription medicines, therapeutic lifestyle changes, blood pressure management and fasting blood glucose. Polypharmacy was defined as the use of five or more medicines in a single day. Results: Of the 338 patients with co-morbid hypertension and diabetes, (18 %, n = 61) were men and (82 %, n = 277) were women. Most of the study participants (40.2 %, n = 136) were between 55 and 64 years. Polypharmacy (≥5 medications) was observed in (64.8 %, n = 219) of the participants and being male [cOR = 2.29, 95 % CI (1.18 – 4.42), p = 0.014] and living in a rural setting [cOR = 2.46, 95 % CI (1.13 - 5.34), p = 0.023] were significantly associated with more than the two-fold increased risk of having polypharmacy compared to being female and living in an urban setting respectively. Most patients were on biguanides (89.1 %, n = 301), angiotensin receptor blockers (68.6 %, n = 232), calcium channel blockers (66.6 %, n = 225) and sulphonylurease (53.8 %, n = 182). Conclusion: Polypharmacy is common in patients with co-morbid hypertension and diabetes, and is associated with sex, marital status, and residential status. Healthcare providers must carefully consider these associated factors when prescribing or modifying treatment plans for their patients.

The River Subin, which serves as a source of direct drinking water downstream, flows through the Metropolis of Kumasi in Ghana, making it vulnerable to pollution due to human activities. In this study, we assessed the heavy metals content in the sediment of the Subin River as well as the physicochemical properties and microbial load in the river. Water and sediments were sampled from Race course upstream and downstream, Asafo, and Abinkyi upstream and downstream, Kaase, Asokwa and Wood village with a sampling distance of 100 m. Index of Geoaccumulation (Igeo), contamination factor (CF), contamination degree (CD) and pollution load index (PLI) were used to assess the level of heavy metal enrichment and contamination in the sediments of River Subin. Analysis showed that temperature of the river ranged between 31.23 to 31.40 °C, pH ranged between 6.62 and 7.52, conductivity ranged between 1.16 and 4.67 µS/cm, total hardness ranged from 0.65 to 3.82 mg/L, Ca ranged from 0.26 to 1.22 mg/L, alkalinity ranged from 324 to 711 mg/L, Mg from 0.24 to 3.35 mg/L and Cl ranged from 56.76 to 99. 30 mg/L. The microbial load assessment indicated that the River Subin is highly polluted by E. coli, Faecal Enterococci, Total coliform and faecal coliform, which generally increased as the river flows downstream since more solid waste from domestic and industrial effluents enter the river. Igeo data suggested that the river was highly contaminated by Ag and Cd and moderately polluted by Pb and Co. The CF, CD and PLI, results showed high levels of Ag, Cd, Cr, Cu, Fe, Hg, Co, and Zn, indicating a progressive deterioration of water quality at the various sites. Our results suggest that the River Subin is highly polluted and thus, urgent management strategies are needed to mitigate further deterioration of the river.

For years, the study of free surface vortices at hydropower plant intakes has been a topical and intriguing subject among engineers and researchers. This subject will continue to attract attention especially as the world strives to meet the ever-increasing demand for energy. Despite the numerous benefits associated with hydropower, the sustainability of some hydropower plants is being threatened due to low inflows often associated with climate change. Free surface vortices associated with low water levels or submergence at plant intakes can have very detrimental consequences on the operation of hydropower plants if not addressed. Notwithstanding this, free surface vortex flows have also been found to be very relevant in emerging technologies such as the water vortex hydropower plant system. This paper, therefore, presents a state-of-the-art review of the subject including summarised historical findings, but with an emphasis on current developments, findings and research gaps to guide practitioners and researchers. In response to the research gaps identified, the authors make a number of recommendations for further studies which include establishing relationships between free surface vortices formation and turbine efficiency, development of more accurate models for critical submergence and free surface vortices, assessment of free surface vortices at multiple and multi-level intakes, establishing the relationship between free surface vortices and sediment transport at intakes, application of Computational Fluid Dynamics (CFD) shape optimization tools for intake and anti-vortex device optimisation, as well as the continuing development of CFD tools to simulate air-entrained vortices at hydropower intakes.

A vortex is a ubiquitous everyday phenomenon that is observed in nature and it is formed due to the rotational motion of fluid around an axis perpendicular to the free surface. Free surface vortices are a common unwanted occurrence at hydraulic intakes which can cause serious detrimental impacts on mechanical devices such as turbines and pumps. In this paper, an experimentally observed air-core vortex is numerically simulated using the OpenFOAM LTSInterFoam solver. The LTSInterFoam solver has hitherto been mainly used for hydrodynamic studies relating to ship manoeuvrability by researchers. This solver uses a local time stepping approach to speed up convergence towards steady state conditions thus overcoming some of the challenges associated with the use of the conventional interFoam solver for the simulation of free surface vortices. The Shear Stress Transport (SST) k−ω Model was used for the simulation. There was generally good agreement when results from the study were compared with other vortex-related analytical models and experimental data. Overall, the study concludes that the OpenFOAM LTSInterFoam solver is capable of simulating free surface vortices at hydraulic intakes. However, being a steady state solver, the solver cannot account for the transient process involved in the evolution of free surface vortices.

Objective: The plant Terminalia ivorensis is used in traditional medicine as a diuretic and in the management of renal failure. We reported previously that the ethanol stem bark extract of the plant protects against gentamicin-induced renal and hepatic damage in rats. To further elucidate the mechanism of its renoprotective activity, we studied the effects of the extract on Potassium dichromate–induced nephrotoxicity in rats. The present study assessed the effectiveness of the ethanol stem bark extract of Terminalia ivorensis - against renal oxidative injury evoked by potassium dichromate. Methods: Adult Sprague Dawley rats pre-treated with (100–1000 mg/kg p.o. bwt) of Terminalia ivorensis extract for 5 days were challenged with a single dose of Potassium dichromate (20 mg/kg Sc) in the neck region on the 4th day. On the sixth day, renal function and markers of oxidative injury were assessed. Results: Terminalia ivorensis (300–1000 mg/kg p.o) pre-treatment dose dependently prevented decreases in urine output in rats challenged with a nephrotoxic dose of Potassium Dichromate. The extract also protected the rats against Potassium dichromate-induced rise in serum electrolytes, urea and creatinine. Furthermore, it dose dependently prevented Potassium dichromate-induced decrease in renal glutathione (GSH) levels whereas tissue oxidative enzymes Superoxide dismutase (SOD) and catalase were protected from damage. Markers of lipid peroxidation such as level of renal Malondialdehyde (MDA) and myeloperoxidase (MPO) also decreased dose dependently when compare with Potassium dichromate treated groups. The extract also protected the histomorphology of the kidney against Potassium dichromate induced damage. Conclusion: The ethanol stem bark extract of Terminalia ivorensis protects kidney against Potassium dichromate-induced renal damage.

The grid-connected solar power generated by the Bui Power Authority is sold to Ghana Grid Company Limited (GRIDCo) and other customers through bilateral contracts. However, there have been challenges in meeting the supply commitments due to the stochastic nature of solar energy. Fluctuating weather and climatic conditions make it difficult for operators to predict the output of the solar photovoltaic (PV) plant in advance. This has the tendency of leading to anticipated power loss or excess power not being managed effectively. In this study, the random forest and gradient boosting regressor algorithms were used to produce deterministic and probabilistic predictions of solar power generation by using data collected over an eleven-month span. Some of the findings show that the random forest model that produced probabilistic predictions performed better than the other compared models in terms of its accuracy. It produces reliable predictions with a normalized mean absolute error of 1.18%. This is deemed acceptable for operational purposes in the sphere of renewable energy prediction. As a result, these predictions are efficient enough to be used by utility companies as input to numerous decision-making problems during operations which consequently leads to the grid being managed effectively. It may also help them to choose the appropriate modelling approach for predicting solar PV power. Overall, this study supports the generation of clean and reliable energy through improved solar power prediction which directly contributes to African Union's agenda 2063 goals especially, in creating environmentally sustainable and climate resilient economies and communities. This is directly linked to the 7th and 13th UN sustainable developmental goals in ensuring access to affordable, reliable, sustainable, and modern energy for all and, combating climate change and its impacts.

This study examined the biological, social, and clinical risk factors for mortality in coronavirus of the year 2019 (COVID-19) hospitalised patients. The population of the study is prone to COVID-19, thus understanding the most common traits and comorbidities of people who were affected is crucial in reducing its consequences. In this study, four supervised machine learning algorithms were implemented and compared to predict the mortality rate based on the explanatory variables across the five districts of Limpopo Province in South Africa. The data was obtained from Limpopo Department of Health. Prediction about the chances of dying from COVID-19 disease was made using logistic regression, random forest, support vector machine, and decision tree algorithms on the dataset of 20,592 records with twenty-one attributes. Due to the imbalanced nature of the data, Random Over-Sampling Examples (ROSE) were employed to balance our data for more accurate classification effectively. The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. We used 70% of the data for training, while 30% was selected for testing the predictive algorithms. A technique called Step Akaike's Information Criterion (StepAIC) was deployed to reduce the insignificant variables from the full model of the logistic regression. According to the findings of the study, among the four algorithms tested, random forest had the highest recall rate for predicting mortality at roughly 79 percent compared to the other three algorithms. Accordingly, we conclude that random forest algorithm is appropriate for predicting the chances of patients dying from COVID-19 based on the attributes of the five districts of Limpopo Province. In terms of the features and their importance, a function called Variable Importance (VarImp) was used to check which of the attributes have predictive power on the outcome variable (discharged status). The findings revealed that age, ever ventilated, ever oxygenated, intensive ward upon admission, Waterberg district, and private facility type were among the risk factors that could be selected for the logistic regression model to predict mortality in hospitalised patients. This implies that special attention should be given to these identified variables. The random forest model is adequate to establish these factors since the findings reveal that a fairly considerable percentage of explained variation would correctly classify 79% of the cases. The novelty of conducting research on the analysis of COVID-19 cases and comorbidities using machine learning algorithms lies in the potential to uncover new insights and patterns that might not be immediately apparent through traditional statistical methods. Machine learning algorithms can quickly and accurately analyze large datasets and identify complex relationships between variables, which could provide valuable information for public health officials and policymakers.

The high prevalence of illicit forest activities has adverse effects on biodiversity and the environment and setbacks to sustainable forest management and economies. This paper analyzed official reports on forest offenses and linked tree offenses' types to their ecological importance over a ten-year period (2008 to 2017) in the Bibiani Forest District. It sought insights from forestry officials and forest fringe communities' motives and means of managing forest offenses in Ghana. The reviewed official records revealed seven offenses types, with the most frequently recorded offense being illegal farming (n = 67). It was followed by illegal logging (n = 51), illicit mining (n = 33), and chainsaw lumbering (n = 13). The lowest recorded offenses (n = 3) were human-induced wildfire, charcoal production, and canoe carving. From the tree species related offenses, a total of 31 timber species were reportedly felled illegally, with Celtis mildbreadi being the most felled, followed by Triplochiton scleroxylon and Pterygopta macrocarpa. It was also revealed that most of the trees were under imminent threat of extinction (Scarlet-star rated species). The study found lack of farmlands, limited access to NTFPs and trees, logistical challenges (e.g., vehicle, motorbikes, staff and modern technology like drones), weak law enforcement and interferences from some key stakeholders to be factors that motivate culprits to commit illicit forest activities. It is recommended that the Forestry Commission should invest more in technological and human resources to strengthen its surveillance in partnership with forest fringe communities to enhance the ecological, economic and social benefits of forest resources to all segments of society.

The Government of Ghana has defined key targets for its energy sector to provide electricity for all the citizens by the end of 2020. Renewable energy has been identified as one of the promising options for electricity generation in Ghana. Thus, Renewable Energy Act, 2011 (Act 882) was espoused to provide regulatory framework and monetary incentives to boost private sector investment and promote the use of renewable energy in efficient and sustainable manner. There is also a target to contribute 10% renewable energy (excluding large hydro) to the electricity generation mix by 2020. To achieve this target, the Government provides a lot of support for investment in renewable energy. This paper presents a comprehensive review of the status of renewable energy penetration in Ghana's electricity supply mix after the Renewable Energy Act was adopted. The review mainly focused on renewable energy resources, current generation status and their applications, and ongoing renewable energy projects in Ghana, with comparative information from five countries spread across Africa; Kenya, Ethiopia, South Africa, Nigeria and Egypt, that are also focused on development of renewable energy resources. The review reveled that renewable energy resources abound in Ghana that could be promoted for economic development and reduce the negative effects of climate change. However, a small fraction of the renewable energy potential has been exploited for electricity supply despite the Government's efforts to promote and increase renewable energy technologies in the country. The renewable energy target of 10% by the year 2020 is unlikely to be achieved as most of the policy instruments in the Renewable Energy Act could not be fully operationalised to boost renewable energy investment. The government has therefore, extended the 10% target to the year 2030 and has come up with Renewable Energy Master Plan with estimated investments required to achieve the target.

Although herbicides have extensively been used in forest landscape restoration, its effects and opportunity costs have rarely been studied in Ghana. This study evaluated types of herbicides used, assessed the perceptions of the effects of herbicide usage among farmers enrolled in a taungya (i.e. farm forestry programs) in the dry semi-deciduous ecological zone of Ghana. The study also analyzed the challenges associated with the application of herbicides and determined the impact of socio-economic factors that influence farmers decision to use herbicides on farms. Data were collected from 300 taungya farmers and 20 herbicide traders in Offinso North, Offinso South, and Techiman South Municipal. Data collection was completed in December 2016. Data were analyzed descriptively and quantitatively. Results indicate that 100% of farmers perceived herbicides as having deleterious effects on flora (seedlings, under-storey wild foods, medicines) and fauna (soil micro-organisms, fish in rivers) as well as water quality, soil structure and human health. Nevertheless, 93% of them use herbicides to reduce labour costs and to establish larger acreages. Based on their functional properties, 85% of herbicides traded are non-selective for bush clearing and 15% are selective for weeding. Farmers use them indiscriminately due to poor knowledge on recommended practices and intensity of invasiveness of weed species. Education, residential status and age of respondents significantly predicted usage. The study recommends a review of Ghana's pesticide deployment strategies and herbicides guide book for use in farm forestry. Capacity of frontline forestry professionals, traders and farmer-based organizations must be enhanced to effectively advice and monitor safe application. Plant based herbicides must be promoted for safety and enhancement of biodiversity.