Seroprevalence of Hepatitis B virus and human immunodeficiency virus co-infection in pregnant women from Osun State, Nigeria.

INTRODUCTION: Hepatitis B virus and human immunodeficiency virus (HBV/HIV) co-infection is a global health concern due to its significant impact on morbidity and mortality. Reports of HBV/HIV co-infections are increasing in Nigeria, but information on the disease burden in pregnant women and its implications on the fetus is scarce. This study aimed to determine the prevalence of HBV/HIV co-infection in pregnant women. In addition, the study identified the risk factors for the disease in pregnant women attending antenatal clinics in Osun State, Nigeria. METHODOLOGY: We collected plasma samples from 303 consenting pregnant women and used enzyme-linked immunosorbent assay (ELISA) to test for HBV (HBsAg) and HIV I/II antigens. We obtained demographic and risk factor data on HBV and HIV transmission using a structured questionnaire. RESULTS: Our analysis revealed a prevalence of 3.96% for HBV/HIV co-infection in pregnant women. Bivariate analysis indicated a history of blood transfusion, oral or anal sex, and multiple sexual partners may be associated with an increased likelihood of HBV/HIV co-infection in pregnant women. After adjusting for other variables in multivariate analysis, none of these risk factors were significant at the 5% level. In contrast, formal education was a potential preventive factor in this population. CONCLUSIONS: Our study provides valuable information on the disease burden of HBV/HIV co-infection in pregnant women in Osun State, Nigeria, highlighting the importance of routine screening for HBV and HIV during antenatal care and emphasizing the importance of implementing preventive measures to reduce the morbidity and mortality associated with HBV/HIV co-infection.

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets.

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases.

Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.

A bibliometric review of grid parity, energy transition and electricity cost research for sustainable development.

As the topic of sustainable development continues to prominence in global affairs, the case for renewable energy has never been stronger. To be regarded as a perfect alternative to conventional (non-renewable) energy sources in many climes, renewable energy, such as solar and wind, shows promise when considering concepts like grid parity. A significant number of studies have been devoted to understanding the concept. However, only a few studies have committed themselves to analysing the research activity carried out on it. This paper will present a bibliometric and empirical review of worldwide grid parity, energy transition, and electricity cost research. To situate the progress in this research area, a detailed search of Scopus was used to identify and situate research development in the field from 1965 until 2021. Using the data extracted from Scopus and VOSviewer for analysis, we explore different aspects of the publications, such as the volume, growth rate, and coverage of published documents, the most influential research papers and journals in this research area, and the most studied research themes in recent years. We also discuss Governmental policies in developed and developing economies that have accelerated the attainment of Grid parity in certain countries. Also, an empirical review of top-down, bottom-up, and artificial neural network approaches to evaluating grid parity was conducted. The study revealed a steady increase in the research articles focused on grid parity, energy transition, and electricity cost research from 2006. The geographic distribution of the publications shows that most of the publications on the subject originated from the USA, Germany, China, United Kingdom, and Spain, raking in 42.2% of the publications. Also, the top 7 authors with the highest document count from Scopus are from Finland, which coincidentally is one of the countries making significant progress in Grid parity attainment. Of the total document count from Scopus, only 0.02% are papers published from African Countries. Could this reluctance to publish research findings on energy transition be one of the reasons for the slow progression of sustainable energy for all in Africa? Therefore, it is imperative now more than ever for more research focusing on the attainment of grid parity, energy transition, and electricity costs for developing countries to be brought to the fore. This article provides a review of state-of-the-art research on the attainment of grid parity and energy transition with a focus on the Levelized Cost of Electricity (LCOE) models of renewable energy sources.

Performance evaluation of the prospects and challenges of effective power generation and distribution in Nigeria.

Effective Electric Power Generation and Distribution result in the overall increase in efficiency in an economy. Nigeria generates 4500 MW for a population of 200 million people; hence, more than 50% of the population has no electricity access, and those with access experience power outages. Inasmuch as Nigeria is blessed with a vast amount of renewable energy sources, the country heavily relies on Natural Gas for power Generation. With regards to Power Generation, it is seen that the Power Generated is not evacuated efficiently. The purpose of this study is to access the growth of the Nigerian Power Sector from 1898 till date. This is achieved by evaluating the Power Sector Reforms enacted by different government to analyze their effectiveness. Furthermore, the study reviews strategically research that have discussed the strengths and weakness of the Nigerian Power Sector to be able to suggest hidden opportunities and reveal threats to the attainment of sustainable energy for all in 2030. Some of the Challenges discovered were energy user challenges, financial challenges, and energy losses. Some solutions and opportunities are the use of distributed generation, smart meters and the implemented of a smart grid system. In the long run, the authors propose that viable renewable energy sources in each State of the federation be tapped for Power Generation this would enable each State to the self-reliant and contributors to the Nigerian Power Generation Pool. It is believed that this actions would promote economic, social and technological benefits for the every stakeholder.

Identification of HER2 inhibitors from curcumin derivatives using combination of in silico screening and molecular dynamics simulation.

Breast cancer remains a major world health challenge in women. Some Breast cancers are human epidermal growth factor receptor 2 (HER2) positive. Since this protein promotes the growth of cancer cells, it remains a therapeutic target for novel drugs. This study uses in silico model to predict HER2 inhibitors from curcumin derivatives via QSAR, e-pharmacophore, ADMET as well as structure-based virtual screening using Schrodinger suite. The molecular dynamics simulation of lead compounds, reference ligand and co-crystalized ligand was performed using GROMACS. At the end, eight active curcumin derivatives were predicted as inhibitors of HER2 with high binding affinity and better interaction compared with the reference drug (Neratinib) but lower binding affinity compared with the co-crystalized ligand (TAK-285). After prediction of the bioactivity of the molecules using AutoQSAR, the hit compounds showed appreciable inhibitory pIC50 compared with the reference and co-crystalized ligands against HER2. The pharmacokinetics profile predicted the eight hit compounds as drug-like and drug candidates. The MD simulation predicted the stability of the two top-scored compounds (10763284 and 78321412) in complex with HER2 for the final 80 ns of the trajectory period after initial equilibration with higher H-bond interactions in the protein-reference drug complex compared to the hit compounds-HER2 complexes. This study revealed that curcumin derivatives especially (1E,6E)-1,8-bis(4-hydroxy-3-methoxyphenyl)octa-1,6-diene-3,5-dione and (1E,6E)-4-ethyl-1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,6-diene-3,5-dione were identified to demonstrate inhibitory activity against HER2 which is comparable to neratinib. Conclusively, the lead compounds require further in vitro and in vivo experimental validation for the discovery of new HER2 antagonists for breast cancer management.Communicated by Ramaswamy H. Sarma.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications.

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

Adsorption study of Methylene blue dye: an effluents from local textile industry using Pennisteum pupureum (elephant grass).

This study evaluates the adsorptive capacity of elephant grass (EG) in the removal of Methylene blue (MB) dye from wastewater sourced from two major local dyeing industries in Ogun State, Nigeria. Batch adsorption method was used to determine the optimum conditions, characterization of the adsorbent, equilibrium Isotherm models, kinetics and thermodynamics studies were conducted to evaluate the nature of the adsorption process. The optimum adsorption conditions obtained for the standard solution of MB dye were; pH 7, Temp 40 °C, contact time 180 min and adsorbent dosage 2.0 g. The presence of oxygen containing functional groups and shift or disappearance of bands in the FTIR suggested the suitability of EG for the process. The SEM of EG revealed presence and disappearance of pores before and after the adsorption process. The mechanism of this adsorption is complex, the adsorption data is best fitted to Langmuir isotherm, the mean adsorption energy E (≤6.455 kJ/mol), and activation energy (10.84 kJ/mol) represents physical process but, the thermodynamic studies revealed spontaneity (ΔG° -15.93 to -14.26 kJ mol-1), randomness, and endothermic (ΔH° 40.1 kJ/mol) nature, representing chemisorption. Therefore, local dyers around the study sites can make use of the freely available EG for the remediation of their wastewater.

Elephant grass (EG) abundantly thrives on the study sites where dye wastewater is released by local dyers. This serves as the impetus for this research as no other plants thrive on the dye-polluted environment. A favorable adsorption was obtained with EG as adsorbent for the Methylene blue (MB) dye that is a major constituent of the wastewater from the two major local dyeing industries investigated. Therefore, this study provides scientific support for the local dyers around the study sites to make use of the freely available EG for the remediation of their wastewater.

Sexual Orientation Among Unmarried Final Year University Students in Osun State: Distribution, Determinants and Level of Satisfaction.

BACKGROUND: Sexual Orientation is an enduring pattern of romantic or sexual attraction, with the attractions broadly incorporated under heterosexuality and non-heterosexuality. Societal and other factors seem to affect individuals' perception of their self-identified orientation. Thus, this study sets out to document the distribution of sexual orientation, level of self-reported satisfaction, and factors associated with sexual orientation among unmarried university students. METHODS: A cross-sectional study involving 550 respondents sampled using a multi-stage sampling technique. Data collected via a self-administered questionnaire and analyzed via IBM SPSS version 20.0. RESULTS: Most respondents (93.6%) were heterosexuals, 4.7% bisexuals, 1.3% lesbian women and 0.40% gay men. More heterosexuals (80.4%) were fully satisfied with their self-identified sexual orientation compared to 57.2% of non-heterosexuals. Respondents who had religious upbringing and those whose parents were currently married were less likely to have sexual orientation outside of heterosexuality. CONCLUSION: The most prevalent sexual orientation was heterosexuality, with more heterosexuals also expressing satisfaction than others. With the level of dissatisfaction expressed by those who are not heterosexuals as well as the factors associated with heterosexuality, it is plausible to conclude that societal values and/or prejudices constitute major factors in individuals self-identifying with some particular sexual orientation and the satisfaction derived therein.

Dataset on statistical analysis of jet A-1 fuel laboratory properties for on-spec into-plane operations.

Safety is of utmost essence in the aviation sector, both on-ground and in the air. Aviation Turbine Kerosene (ATK) commonly referred to as jet fuel is one of the major resources of the aviation sector, contributing significantly to the operating cost of an airline. Flight safety is a top-notch requirement in air transportation management. Jet fuel quality affects flight safety, and this makes it mandatory to ensure that, at all points in the jet A-1 aviation fuel supply chain, the jet fuel is contamination free and on-spec. Jet fuel quality is determined via various mandatory Joint Inspection Group (JIG) based quality analysis test procedures; both baseline and extensive lab tests by third party labs. Acceptable parameter range has been established for each jet fuel property, the electrical conductivity of jet A-1 fuel must be between 50 and 600 pS/m and the density at 15 °C must be between 0.775 and 0.840 g/cm3. Beyond this range, the fuel is deemed off-spec and unsafe for into-plane fuelling operations. This data article presents daily jet fuel test records for jet-A1 fuel. The dataset contains the date of the test, the conductivity, the specific gravity at ambient temperature, the converted specific gravity at 15 °C, and the temperature of the jet fuel sample under study. All the tests were performed at standard laboratory conditions using approved and certified equipment. The dataset provides an opportunity for developing a predictive model that can be used for jet fuel properties prediction on a given day, based on previous data trends and analysis using data pattern recognition, as an indication of the variation of jet fuel properties with daily weather variation.

The role of gender on academic performance in STEM-related disciplines: Data from a tertiary institution.

This data article presents data of academic performances of undergraduate students in Science, Technology, Engineering and Mathematics (STEM) disciplines in Covenant University, Nigeria. The data shows academic performances of Male and Female students who graduated from 2010 to 2014. The total population of samples in the observation is 3046 undergraduates mined from Biochemistry (BCH), Building technology (BLD), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Computer Science (CIS), Civil Engineering (CVE), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mathematics (MAT), Microbiology (MCB), Mechanical Engineering (MCE), Management and Information System (MIS), Petroleum Engineering (PET), Industrial Physics-Electronics and IT Applications (PHYE), Industrial Physics-Applied Geophysics (PHYG) and Industrial Physics-Renewable Energy (PHYR). The detailed dataset is made available in form of a Microsoft Excel spreadsheet in the supplementary material of this article.

Datasets linking ethnic perceptions to undergraduate students learning outcomes in a Nigerian Tertiary Institution.

This data article represents academic performances of undergraduate students in a select Nigerian Private Tertiary institution from 2008 to 2013. The 2413 dataset categorizes students with respect to their origins (ethnicity), pre-university admission scores and Cumulative Grade Point Averages earned at the end of their study at the university. We present a descriptive statistics showing mean, median, mode, maximum, minimum, range, standard deviation and variance in the performances of these students and a boxplot representation of the performances of these students with respect to their origins.

Dataset showing steel cold rolling process parameters for a 6-high cold rolling mill in Nigeria.

The data contained in this article was acquired from the automatic gauge control system for a steel cold rolling mill production line in Nigeria. Accuracy is the one of the most important indices of productivity during a milling process. A total of 486 data points were obtained from selected feedback sensors located on the rolling mill machine via the control panel Human Machine Interface (HMI). The selected rolling parameters were gathered at different time intervals for different sample coils strips during the different milling stages. The data shows parameters such as actual thickness measured, x-ray gauge temperature, mill speed at both entry and exit and the mill power. This dataset could be used to analyze and improve the accuracy of the Automatic gauge control system and reduction in error in thickness variation.

Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university.

Empirical measurement, monitoring, analysis, and reporting of learning outcomes in higher institutions of developing countries may lead to sustainable education in the region. In this data article, data about the academic performances of undergraduates that studied engineering programs at Covenant University, Nigeria are presented and analyzed. A total population sample of 1841 undergraduates that studied Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) within the year range of 2002-2014 are randomly selected. For the five-year study period of engineering program, Grade Point Average (GPA) and its cumulative value of each of the sample were obtained from the Department of Student Records and Academic Affairs. In order to encourage evidence-based research in learning analytics, detailed datasets are made publicly available in a Microsoft Excel spreadsheet file attached to this article. Descriptive statistics and frequency distributions of the academic performance data are presented in tables and graphs for easy data interpretations. In addition, one-way Analysis of Variance (ANOVA) and multiple comparison post-hoc tests are performed to determine whether the variations in the academic performances are significant across the seven engineering programs. The data provided in this article will assist the global educational research community and regional policy makers to understand and optimize the learning environment towards the realization of smart campuses and sustainable education.

Learning analytics: Dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university.

In Nigerian universities, enrolment into any engineering undergraduate program requires that the minimum entry criteria established by the National Universities Commission (NUC) must be satisfied. Candidates seeking admission to study engineering discipline must have reached a predetermined entry age and met the cut-off marks set for Senior School Certificate Examination (SSCE), Unified Tertiary Matriculation Examination (UTME), and the post-UTME screening. However, limited effort has been made to show that these entry requirements eventually guarantee successful academic performance in engineering programs because the data required for such validation are not readily available. In this data article, a comprehensive dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university is presented and carefully analyzed. A total sample of 1445 undergraduates that were admitted between 2005 and 2009 to study Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) at Covenant University, Nigeria were randomly selected. Entry age, SSCE aggregate, UTME score, Covenant University Scholastic Aptitude Screening (CUSAS) score, and the Cumulative Grade Point Average (CGPA) of the undergraduates were obtained from the Student Records and Academic Affairs unit. In order to facilitate evidence-based evaluation, the robust dataset is made publicly available in a Microsoft Excel spreadsheet file. On yearly basis, first-order descriptive statistics of the dataset are presented in tables. Box plot representations, frequency distribution plots, and scatter plots of the dataset are provided to enrich its value. Furthermore, correlation and linear regression analyses are performed to understand the relationship between the entry requirements and the corresponding academic performance in engineering programs. The data provided in this article will help Nigerian universities, the NUC, engineering regulatory bodies, and relevant stakeholders to objectively evaluate and subsequently improve the quality of engineering education in the country.

Incorporating a healthy reimbursable snack in an afterschool homework program for middle school students: a case study.

BACKGROUND: This study tested the feasibility and acceptability of adding a reimbursable snack that meets the Institute of Medicine nutrition recommendations to an afterschool homework program for middle school students. METHODS: Snack menu was developed and administered to students attending an afterschool homework program over 12 weeks. In spring 2009, two cross-sections of middle school students completed study measures, including snack preferences. Key school personnel completed follow-up interviews assessing program feasibility. RESULTS: Survey evaluations from 110 students at baseline and 113 at posttest suggested improved preferences for healthy snacks. Teacher supervisors (n = 3) and the assistant principal rated the pilot project as feasible and beneficial, whereas school food service (n = 3) rated the program unsustainable because of administration logistics and costs. CONCLUSIONS: The addition of healthy snacks to afterschool programs was liked by students and teachers. However, policies that support simpler accountability procedures may be needed for school-based afterschool snack programs to be sustainable.

A pilot study to expand the school breakfast program in one middle school.

School Breakfast Program (SBP) eaters weigh less and have healthier diets than nonSBP eaters. However, SBP is underused nationally, especially among low income youth. To explore the feasibility of expanding access to the SBP to improve participation among sixth grade students in one middle school in Minneapolis, Minnesota. A grab-n-go SBP menu, hallway delivery service and in-classroom eating strategies were implemented and evaluated with a cohort of sixth grade students (n=239) for 6weeks during spring 2010. Process measures were collected from students and teachers and through direct observations. The school district provided objective SBP participation data at baseline and post intervention. Students were very satisfied with eating in the classrooms (64%). Teachers (n=10) rated eating in the classroom as not messy, not disruptive and student behavior as excellent or good (100%). There was a significant increase in SBP participation from 0.74days per week to 1.21days per week (p<0.0001). Improvements were more pronounced among students eligible for free and reduced priced school meals. A school environment that supports convenient SBP menu and serving and eating locations was feasible and increased SBP participation in this suburban middle school.

Gunshot injuries in a Nigerian hospital

Background: Gunshot injuries are major problems worldwide from the medical and economic perspectives and are associated with profound morbidity and significant mortality. Many previousstudies were focused on specific sites of injury but this study was aimed at the pattern and presentation of the gunshot injuries. Patients and Methods: This was a combined retrospective and prospective study of gunshot injuries in a Nigerian hospital. The medical records of patients with gunshot injuries between January and December 2004 were reviewed. Prospectivedata collection was done between January and October 2005. Results: Total number of patients was 38 and male: female ratio was 18:1. Thirty (78.9) were below the age of 40 years. The locally made dane gun was the instrument of attack in 24 (63.1) and 19 (50.0) of the patients were victims of armed robbery. The most frequently injured sites were the extremities of which the lower limb was 28 (73.7) and the upper limb was in 10 (26.3) patients. Conclusion: It was concluded that gunshot injuries occur more below age of forty years and the limbs were more injured. Armed robbery attack contributed to about half of the attacks and injuries results mainly from locally made dane guns. It was found that pistols were usually loaded with multiple pellets thus there could be multiple entry and exit wounds in patients attacked with pistol in our environment