Visual impairment among eye health workers in a tertiary eye centre in Ghana

Objective: To determine causes of visual impairment (VI) among staff of the Eye Centre at the Korle Bu Teaching Hospital.Design: This was a cross-sectional study.Setting: The Eye Centre, Korle Bu Teaching Hospital (KBTH), from October 2016 to March 2017 on all consenting members of staff.Participants: Eighty-four (79.3%) of 106 consenting staff members participated in this study.Data collection/Intervention: A detailed history (demographic, ocular, medical co-morbid conditions), ocular examination and relevant diagnostic investigations were conducted. Interventions initiated included treatment for glaucoma, dry eye and allergic conjunctivitis and spectacles prescription for refractive errors.Main outcomes: Prevalence of avoidable causes of VI (glaucoma, cataract, refractive errors). Secondary outcomes included prevalence of unavoidable causes of VI. Results Eighty-four (79.3%) members of staff participated in this study. Most of the participants were females, 54(64.3 %). Age ranged from 23 to 60 years with an average of 35.8±9.9 years (mean ± SD). Prevalence of VI was 9.5 % (8/84), all due to uncorrected refractive error. Other known causes of VI included open angle glaucoma in 12(14.3 %), macular scar of unknown cause, 1(1.2 %) and sutural cataract, 1(1.2 %) but were all visually insignificant.Conclusions: The prevalence of VI among the staff of the Eye Centre of the KBTH was 9.5 %, all due to refractive errors. Other known causes of avoidable visual impairment and blindness encountered were glaucoma (14.3 %), macular scar (1.2 %) and cataract (1.2 %), all asymptomatic. Routine eye screening should be part of periodic medical examination for employees

Parenting practices and family relationships during the COVID-19 lockdown in Ghana

The effects of the COVID-19 pandemic have been far reaching across almost every sphere of life. Families, which are the basic units of society, have not been spared the ravages of the pandemic. Changes in family daily routines as a result of COVID-19 can affect spousal relationships, parenting and childcare practices. However, the extent to which the pandemic has affected parenting practices and family relationships in Ghana is not known. The goal of this study was to assess how parenting practices and family relationships have been influenced during the COVID-19 pandemic in Ghana. Data for this paper was drawn from an online questionnaire response from 463 participants in Ghana as a subset analysis from a multi-country study on personal and family coping system with COVID-19 pandemic in the global south. The mean score for pre-COVID-19 relationship with partner (36.86) was higher (p<0.0001) than the mean score for during COVID-19 relationship with partner (35.32) indicating that COVID-19 has had negative influence on relationships. The mean score for pre-COVID-19 parenting (32.78) was higher (p<0.0001) compared to the mean score for during COVID-19 parenting (31.40) indicating negative influence on parenting. We have predicted that participants whose coping levels were “Well” on the average, are likely to be doing well in relationship with partners and parenting practices during the COVID-19 period The challenging public health containment measures of the COVID-19 pandemic have negatively influenced the relationship between partners and parenting practices in Ghana

Chromosome evolution and the genetic basis of agronomically important traits in greater yam

The nutrient-rich tubers of the greater yam, Dioscorea alata L., provide food and income security for millions of people around the world. Despite its global importance, however, greater yam remains an orphan crop. Here, we address this resource gap by presenting a highly contiguous chromosome-scale genome assembly of D. alata combined with a dense genetic map derived from African breeding populations. The genome sequence reveals an ancient allotetraploidization in the Dioscorea lineage, followed by extensive genome-wide reorganization. Using the genomic tools, we find quantitative trait loci for resistance to anthracnose, a damaging fungal pathogen of yam, and several tuber quality traits. Genomic analysis of breeding lines reveals both extensive inbreeding as well as regions of extensive heterozygosity that may represent interspecific introgression during domestication. These tools and insights will enable yam breeders to unlock the potential of this staple crop and take full advantage of its adaptability to varied environments

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images

Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression

Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. The model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. The overall accuracy of the fitted model was assessed to be 92%. Through the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection

A Molecular Modeling Approach to Identify Potential Antileishmanial Compounds Against the Cell Division Cycle (cdc)-2-Related Kinase 12 (CRK12) Receptor of Leishmania donovani

The huge burden of leishmaniasis caused by the trypanosomatid protozoan parasite Leishmania is well known. This illness was included in the list of neglected tropical diseases targeted for elimination by the World Health Organization. However, the increasing evidence of resistance to existing antimonial drugs has made the eradication of the disease difficult to achieve, thus warranting the search for new drug targets. We report here studies that used computational methods to identify inhibitors of receptors from natural products. The cell division cycle-2-related kinase 12 (CRK12) receptor is a plausible drug target against Leishmania donovani. This study modelled the 3D molecular structure of the L. donovani CRK12 (LdCRK12) and screened for small molecules with potential inhibitory activity from African flora. An integrated library of 7722 African natural product-derived compounds and known inhibitors were screened against the LdCRK12 using AutoDock Vina after performing energy minimization with GROMACS 2018. Four natural products, namely sesamin (NANPDB1649), methyl ellagic acid (NANPDB1406), stylopine (NANPDB2581), and sennecicannabine (NANPDB6446) were found to be potential LdCRK12 inhibitory molecules. The molecular docking studies revealed two compounds NANPDB1406 and NANPDB2581 with binding affinities of −9.5 and −9.2 kcal/mol, respectively, against LdCRK12 which were higher than those of the known inhibitors and drugs, including GSK3186899, amphotericin B, miltefosine, and paromomycin. All the four compounds were predicted to have inhibitory constant (Ki) values ranging from 0.108 to 0.587 μM. NANPDB2581, NANPDB1649 and NANPDB1406 were also predicted as antileishmanial with Pa and Pi values of 0.415 and 0.043, 0.391 and 0.052, and 0.351 and 0.071, respectively. Molecular dynamics simulations coupled with molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) computations reinforced their good binding mechanisms. Most compounds were observed to bind in the ATP binding pocket of the kinase domain. Lys488 was predicted as a key residue critical for ligand binding in the ATP binding pocket of the LdCRK12. The molecules were pharmacologically profiled as druglike with inconsequential toxicity. The identified molecules have scaffolds that could form the backbone for fragment-based drug design of novel leishmanicides but warrant further studies to evaluate their therapeutic potential

Plant Growth and Nutritional Quality Attributes of Basella alba Applied with Variable Rates of Nitrogen Fertilizer at Different Planting Dates under Canadian Maritime Climatic Conditions

Nitrogen (N) fertilization at critical planting time is important to optimize productivity and reduce nitrate accumulation in edible portions of green leafy vegetable plants. A field experiment was performed to determine the effects of variations in N rate and planting time on plant growth, yield, and nutritional quality attributes of Basella alba under Atlantic maritime climatic conditions. The N rates were 0 (control), 40 (low), 80 (medium), and 120 kg ha−1 (high) at planting times 15 June–3 August (early season), 6 July–20 August (mid-season), and 4 August–8 September (late season). Plant height, number of branches, and stem girth were increased after 45 days after sowing in early and mid-season plantings, but leaf length decreased during the same time by 32.8% in the late planting. The average yield obtained in early, mid-, and late plantings were 171, 464, and 328 g plant−1, respectively. Low N gave the highest yield in early planting while medium N gave higher yields in mid- and late plantings. However, the medium N increased nitrate accumulation in B. alba by 7% compared to the high N rate. In general, there was no significant effect of N on B. alba total phenolic and total carotenoid contents. Overall, the highest yield was obtained during the warmest summer months of mid- and late plantings. Therefore, there is a potential to grow B. alba as a summer vegetable under Canadian Atlantic maritime conditions. However, it is recommended to reduce the rate of N fertilizer application during high-temperature conditions. Future studies are required to investigate phosphorus and potassium fertilization and nitrate accumulation in B. alba and potential health risks

Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)

Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%)

Antibiotic resistance and mecA characterization of Staphylococcus hominis from filarial lymphedema patients in the Ahanta West District, Ghana: A cross‐sectional study

Abstract Background and Aim Filarial infections affect over 150 million people in the tropics. One of the major forms of filarial pathologies is lymphedema; a condition where the immune response is significantly altered, resulting in changes in the normal flora. Staphylococcus hominis, a human skin commensal, can also be pathogenic in immunocompromised individuals. Therefore, there is the possibility that S. hominis could assume a different behavior in filarial lymphedema patients. To this end, we investigated the levels of antibiotic resistance and extent of mecA gene carriage in S. hominis among individuals presenting with filarial lymphedema in rural Ghana. Method We recruited 160 individuals with stages I–VII lymphedema, in a cross‐sectional study in the Ahanta West District of the Western Region of Ghana. Swabs from lymphedematous limb ulcers, pus, and cutaneous surfaces were cultured using standard culture‐based techniques. The culture isolates were subjected to Matrix‐Assisted Laser Desorption/Ionization Time of Flight (MALDI‐TOF) mass spectrometry for bacterial identification. Antimicrobial susceptibility testing (AST) was performed using the Kirby–Bauer method. mecA genes were targeted by polymerase chain reaction for strains that were cefoxitin resistant. Results In all, 112 S. hominis were isolated. The AST results showed resistance to chloramphenicol (87.5%), tetracycline (83.3%), penicillin (79.2%), and trimethoprim/sulphamethoxazole (45.8%). Of the 112 strains of S. hominis, 51 (45.5%) were resistant to cefoxitin, and 37 (72.5%) of the cefoxitin‐resistant S. hominis haboured the mecA gene. Conclusion This study indicates a heightened level of methicillin‐resistant S. hominis isolated among filarial lymphedema patients. As a result, opportunistic infections of S. hominis among the already burdened filarial lymphedema patients in rural Ghana may have reduced treatment success with antibiotics