Department of Biomedical Engineering
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Item Development of a standard phantom for diffusion-weighted magnetic resonance imaging quality control studies: A review(Polish Journal of Medical Physics and Engineering, 2022) Manson, E.N.; Mumuni, A.N.; I, Shirazu; Hasford, F.; Inkoom, S.; Aikins, M.P.Various materials and compounds have been used in the design of diffusion-weighted magnetic resonance imaging (DW MRI) phantoms to mimic biological tissue properties, including diffusion. This review thus provides an overview of the preparations of the various DW-MRI phantoms available in relation to the limitations and strengths of materials/solutions used to fill them. The narrative review conducted from relevant databases shows that synthesizing all relevant compounds from individual liquids, gels, and solutions based on their identified strengths could contribute to the development of a novel multifunctional DW-MRI phantom. The proposed multifunctional material at varied concentrations, when filled into a multi-compartment Perspex container of cylindrical or spherical geometry, could serve as a standard DW-MRI phantom. The standard multifunctional phantom could potentially provide DW-MRI quality control test parameters in one study sessionItem Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media(Hindawi, 2022) Gupta, S.; Kumar, S.; Bangare, S.L.; Nuhmani, S.; Alguno, A.C.; Samori, I.A.Aiming at the inadequacy of the group decision-making method with the current attribute value as interval language information, an interval binary semantic decision-making method is proposed, which considers the decision maker’s psychological behavior. The scope of this research is that this paper is based on localized amplification method. The localized amplification method used in this research may amplify physiological movement after removing unwanted noise, allowing the movement trend to be seen with the naked eye, improving the CNN network’s mental identification accuracy. These two algorithms analyze the input picture from various perspectives, allowing the CNN network to extract more information and enhance identification accuracy. A new distance formula with interval binary semantics closer to decision-makers thinking habits is defined; time degree is introduced. An optimization model is established to solve the time series weights by considering the comprehensive consistency of expert evaluation. Based on prospect theory, a prospect deviation value is constructed and minimized weight optimization model, using the interactive multiple attribute decision community making (TODIM) method based on the new distance measure to calculate the total overall dominance of the schemes to rank the schemes. Taking the selection and evaluation of supply chain collaboration partners as an example, the effectiveness and rationality of the proposed method are verified.Item IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud(2022) Mir, M.H.; Jamwal, S.; Mehbodniya, A.; Garg, T.; Iqbal, U.; Samori, I.A.COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, na¨ıve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.Item Fuzzy Logic-Based Systems for the Diagnosis of Chronic Kidney Disease(Hindawi, 2022) Murugesan, G.; Ahmed, T.I.; Bhola, J.; Shabaz, M.; Singla, J.; Rakhra, M.; More, S.; Samori, I.A.Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual’s health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient’s stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient’s kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.Item Computer-aided identification of potential inhibitors against Necator americanus glutathione S-transferase 3(Elsevier, 2022) Kwofie, S.K.; Asiedu, S.O.; Koranteng, R.; Quarshie, E.; Tiburu, E.K.; Miller III, M.A.; Adinortey, M.B.; Wilson, M.D.Hookworm infection is caused by the blood-feeding hookworm gastrointestinal nematodes. Its harmful effects include anemia and retarded growth and are common in the tropics. A current control method involves the mass drug administration of synthetic drugs, mainly albendazole and mebendazole. There are however concerns of low efficacy and drug resistance due to their repeated and excessive use. Although, Necator americanus glutathione S-transferase 3 (Na-GST-3) is a notable target, using natural product libraries for computational elucidation of promising leads is underexploited. This study sought to use pharmacoinformatics techniques to identify compounds of natural origins with the potential to be further optimized as promising inhibitors. A compendium of 3182 African natural products together with five known helminth GST inhibitors including Cibacron blue was screened against the active sites of the Na-GST-3 structure (PDB ID: 3W8S). The hit compounds were profiled to ascertain the mechanisms of binding, anthelmintic bioactivity, physicochemical and pharmacokinetic properties. The AutoDock Vina docking protocol was validated by obtaining 0.731 as the area under the curve calculated via the receiver operating characteristics curve. Four compounds comprising ZINC85999636, ZINC35418176, ZINC14825190, and Dammarane Triterpene13 were identified as potential lead compounds with binding energies less than −9.0 kcal/mol. Furthermore, the selected compounds formed key intermolecular interactions with critical residues Tyr95, Gly13 and Ala14. Notably, ZINC85999636, ZINC14825190, and dammarane triterpene13 were predicted as anthelmintics, whilst all the four molecules shared structural similarities with known inhibitors. Molecular modelling showed that the compounds had reasonably good binding free energies. More so, they had high binding affinities when screened against other variants of the Na-GST, namely Na-GST-1 and Na-GST-2. Ligand quality assessment using ligand efficiency dependent lipophilicity, ligand efficiency, ligand efficiency scale and fit quality scale showed the molecules are worthy candidates for further optimization. The inhibitory potentials of the molecules warrant in vitro studies to evaluate their effect on the heme regulation mechanisms.Item Prediction of antischistosomal small molecules using machine learning in the era of big data(Springer, 2021) Kwofie, S.K.; Agyenkwa‑Mawuli, K.; Broni, E.; Miller III, W.A.; Wilson, M.D.Schistosomiasis is a neglected tropical disease caused by helminths of the Schistosoma genus. Despite its high morbidity and socio-economic burden, therapeutics are just a handful with praziquantel being the main drug. Praziquantel is an old drug registered for human use in 1982 and has since been administered en masse for chemotherapy, risking the development of resistance, thus the need for new drugs with diferent mechanisms of action. This review examines the use of machine learning (ML) in this era of big data to aid in the prediction of novel antischistosomal molecules. It frst discusses the chal lenges of drug discovery in schistosomiasis. Explanations are then ofered for big data, its characteristics and then, some open databases where large biochemical data on schistosomiasis can be obtained for ML model development are examined. The concepts of artifcial intelligence, ML, and deep learning and their drug applications are explored in schistosomiasis. The use of binary classifcation in predicting antischistosomal compounds and some algorithms that have been applied including random forest and naive Bayesian are discussed. For this review, some deep learning algorithms (deep neural networks) are proposed as novel algorithms for predicting antischistosomal molecules via binary classifcation. Databases specifcally designed for housing bioactivity data on antischistosomal molecules enriched with functional genomic datasets and ontologies are thus urgently needed for developing predictive ML models.Item Identification of novel potential inhibitors of varicella-zoster virus thymidine kinase from ethnopharmacologic relevant plants through an in-silico approach(Taylor & Francis Group, 2021) Kwofie, S.K.; Annan, D.G.; Adinortey, C.A.; Boison, D.; Kwarko, G.B.; Abban, R.A.; Adinortey, M.B.Although Varicella or chickenpox infection which is caused by the varicella-zoster virus (VZV) has sig nificantly been managed through vaccination, it remains an infection that poses threats to the nearest future due to therapeutic drawbacks. The focus of this research was geared towards in silico screening for the identification of novel compounds in plants of ethnopharmacological relevance in the treat ment of chicken pox in West Africa. The work evaluated 65 compounds reported to be present in Achillea millefolium, Psidium guajava and Vitex doniana sweet to identify potential inhibitors of thymi dine kinase, the primary drug target of varicella zoster virus. Out of the 65 compounds docked, 42 of these compounds were observed to possess binding energies lower than 7.0 kcal/mol, however only 20 were observed to form hydrogen bond interactions with the protein. These interactions were eluci dated using LigPlotþ and MM-PBSA analysis with residue Ala134 predicted as critical for binding. Pharmacological profiling predicted three potential lead compounds comprising myricetin, apigenin- 4’ -glucoside and Abyssinone V to possess good pharmacodynamics properties and negligibly toxic. The molecules were predicted as antivirals including anti-herpes and involved in mechanisms comprising inhibition of polymerase, ATPase and membrane integrity, which were corroborated previously in other viruses. These drug-like compounds are plausible biotherapeutic moieties for further biochemical and cell-based assaying to discover their potential for use against chickenpox.Item Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors(Springer, 2021) Agyapong, O.; Miller, W.A.; Wilson, M.D.; Kwofie, S.K.Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance and high toxicity of currently used tubulin-binding agents have necessitated the pursuit of novel drug candidates with increased therapeutic potency. The design of novel drug candidates can be achieved using efficient computational techniques to support existing efforts. Proteochemometric (PCM) modeling is a computational technique that can be employed to elucidate the bioactivity relations between related targets and multiple ligands. We have developed a PCM-based Support Vector Machine (SVM) approach for predicting the bioactivity between tubulin receptors and small, drug-like molecules. The bioactivity datasets used for training the SVM algorithm were obtained from the Binding DB database. The SVM-based PCM model yielded a good overall predictive performance with an area under the curve (AUC) of 87%, Matthews correlation coefficient (MCC) of 72%, overall accuracy of 93%, and a classification error of 7%. The algorithm allows the prediction of the likelihood of new interactions based on confidence scores between the query datasets, comprising ligands in SMILES format and protein sequences of tubulin targets. The algorithm has been implemented as a web server known as TubPred, accessible via http://35.167.90.225:5000/.Item Cheminformatics-Based Identification of Potential Novel Anti-SARS-CoV-2 Natural Compounds of African Origin(MDPI, 2021) Kwofie, S.K.; Broni, E.; Asiedu, S.O.; Kwarko, G.B.; Dankwa, B.; Enninful, K.S.; Tiburu, E.K.; Wilson, M.D.The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome virus 2 (SARS-CoV-2) has impacted negatively on public health and socioeconomic status, globally. Although, there are currently no specific drugs approved, several existing drugs are being repurposed, but their successful outcomes are not guaranteed. Therefore, the search for novel therapeutics remains a priority. We screened for inhibitors of the SARS-CoV-2 main protease and the receptor-binding domain of the spike protein from an integrated library of African natural products, compounds generated from machine learning studies and antiviral drugs using AutoDock Vina. The binding mechanisms between the compounds and the proteins were characterized using LigPlot+ and molecular dynamics simulations techniques. The biological activities of the hit compounds were also predicted using a Bayesian-based approach. Six potential bioactive molecules NANPDB2245, NANPDB2403, fusidic acid, ZINC000095486008, ZINC0000556656943 and ZINC001645993538 were identified, all of which had plausible binding mechanisms with both viral receptors. Molecular dynamics simulations, including molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations revealed stable protein-ligand complexes with all the compounds having acceptable free binding energies <−15 kJ/mol with each receptor. NANPDB2245, NANPDB2403 and ZINC000095486008 were predicted as antivirals; ZINC000095486008 as a membrane permeability inhibitor; NANPDB2403 as a cell adhesion inhibitor and RNA-directed RNA polymerase inhibitor; and NANPDB2245 as a membrane integrity antagonist. Therefore, they have the potential to inhibit viral entry and replication. These drug-like molecules were predicted to possess attractive pharmacological profiles with negligible toxicity. Novel critical residues identified for both targets could aid in a better understanding of the binding mechanisms and design of fragment-based de novo inhibitors. The compounds are proposed as worthy of further in vitro assaying and as scaffolds for the development of novel SARS-CoV-2 therapeutic molecules.Item A Molecular Modeling Approach to Identify Potential Antileishmanial Compounds Against the Cell Division Cycle (cdc)-2-Related Kinase 12 (CRK12) Receptor of Leishmania donovani(MDPI, 2021) Broni, E.; Kwofie, S.K.; Asiedu, S.O.; Miller III, W.A.; Wilson, M.D.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.Item Pharmacophore-Guided Identification of Natural Products as Potential Inhibitors of Mycobacterium ulcerans Cystathionine γ-synthase MetB(MDPI, 2021) Kwofie, S.K.; Dolling, N.N.O.; Donkoh, E.; Laryea, G.M.; Mosi, L.; Miller III, W.A.; Adinortey, M.B.; Wilson, M.D.Abstract: Buruli ulcer caused by Mycobacterium ulcerans (M. ulcerans) is identified by a pain-free cyst or edema which develops into a massive skin ulcer if left untreated. There are reports of chemore sistance, toxicity, noncompliance, and poor efficacy of current therapeutic options. Previously, we used cheminformatics approaches to identify potential antimycobacterial compounds targeting major receptors in M. ulcerans. In this paper, we sought to identify potential bioactive compounds by targeting Cystathionine gamma-synthase (CGS) MetB, a key receptor involved in methionine syn thesis. Inhibition of methionine synthesis restricts the growth of M. ulcerans. Two potent inhibitors Juglone (IC50 0.7 +/− 0.7 µmol/L) and 9-hydroxy-alpha-lapachone (IC50 0.9 +/− 0.1 µmol/L) were used to generate 3D chemical feature pharmacophore model via LigandScout with a score of 0.9719. The validated model was screened against a pre-filtered library of 2530 African natural products. Compounds with fit scores above 66.40 were docked against the structure of CGS to generate hits. Three compounds, namely Gentisic 5-O glucoside (an isolate of African tree Alchornea cordifolia), Isos cutellarein (an isolate of Theobroma plant) and ZINC05854400, were identified as potential bioactive molecules with high binding affinities of −7.1, −8.4 and −8.4 kcal/mol against CGS, respectively. Novel structural insight into the binding mechanisms was elucidated using LigPlot+ and molecular dynamics simulations. All three molecules were predicted to possess antibacterial, anti-ulcerative, and dermatological properties. These compounds have the propensity to disrupt the methionine synthesis mechanisms with the potential of stagnating the growth of M. ulcerans. As a result of rea sonably good pharmacological profiling, the three drug-like compounds are potential novel scaffolds that can be optimized into antimycobacterial molecules.Item Capturing Dioclea Reflexa Seed Bioactives on Halloysite Nanotubes and pH Dependent Release of Cargo against Breast (MCF-7) Cancers In Vitro(MDPI, 2021) Balapangu, S.; Nyankson, E.; Asimeng, B.O.; Asiamah, R.; Arthur, P.K.; Tiburu, E.K.In this work, optimization parameters were developed to capture plant metabolites from Dioclea Reflexa (DR) seed ex-tracts onto halloysites nanotubes (HNTs). A one-step pool of the crude extracts at neutral pH from the HNT lumen failed to elicit a reduction in breast cancer, Michigan Cancer Foundation-7 (MCF-7) cell viability. However, the pH-dependent elution of metabolites revealed that the acidic pH samples exhibited profound antiproliferative effects on the cancer cells compared to the basic pH metabolites using both trypan blue dye exclusion assay and 3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) viability test. pH~5.2 samples demonstrated by half-maximal inhibitory concentration (IC50) of 0.8 mg and a cyclic voltammetry oxidation peak potential and current of 234 mV and 0.45 µA, respectively. This indicates that the cancer cells death could be attributed to membrane polarization/depolarization effects of the sample. Fluorescence-activated cell sorting (FACS) studies confirmed that the plant metabolites affected breast cancer apoptotic signaling pathways of cell death. The studies proved that plant metabolites could be captured using simplified screening procedures for rapid drug discovery purposes. Such procedures, however, would require the integration of affordable analytical tools to test and isolate individual metabolites. Our approach could be an important strategy to create a library and database of bioactive plant metabolites based on pH values.Item Mechanical and Structural Characterization of Pineapple Leaf Fiber(MDPI, 2021) Gaba, E.W.; Asimeng, B.O.; Kaufmann, E.F.; Katu, S.K.; Foster, E.J.; Tiburu, E.K.Evidence-based research had shown that elevated alkali treatment of pineapple leaf fiber (PALF) compromised the mechanical properties of the fiber. In this work, PALF was subjected to differential alkali concentrations: 1, 3, 6, and 9% wt/wt to study the influence on the mechanical and crystal properties of the fiber. The crystalline and mechanical properties of untreated and alkalitreated PALF samples were investigated by X-ray diffractometry (XRD), Fourier transform infrared spectroscopy (FTIR), and tensile testing analysis. The XRD results indicated that crystal properties of the fibers were modified with 6% wt/wt alkali-treated PALF recording the highest crystallinity and crystallite size of 76% and 24 nm, respectively. The FTIR spectra suggested that all alkali-treated PALF samples underwent lignin and hemicellulose removal to varying degrees. An increase in the crystalline properties improved the mechanical properties of the PALF treated with alkali at 6% wt/wt, which has the highest tensile strength (1620 MPa). Although the elevated alkali treatment resulted in decreased mechanical properties of PALF, crystallinity generally increased. The findings revealed that the mechanical properties of PALF not only improve with increasing crystallinity and crystallite size, but are also dependent on the intermediate bond between adjacent cellulose chains.Item The Influence of Pineapple Leaf Fiber Orientation and Volume Fraction on Methyl Methacrylate-Based Polymer Matrix for Prosthetic Socket Application(Polymers, 2021) Gaba, E.W.; Asimeng, B.O.; Foster, E.J.; Tiburu, E.K.; Kaufmann, E.E.This work reports on the use of low-cost pineapple leaf fiber (PALF) as an alternative reinforcing material to the established, commonly used material for prosthetic socket fabrication which is carbon-fiber-reinforced composite (CFRC) due to the high strength and stiffness of carbon fiber. However, the low range of loads exerted on a typical prosthetic socket (PS) in practice suggests that the use of CFRC may not be appropriate because of the high material stiffness which can be detrimental to socket-limb load transfer. Additionally, the high cost of carbon fiber avails opportunities to look for an alternative material as a reinforcement for composite PS development. PALF/Methyl Methacrylate-based (MMA) composites with 0 , 45 and 90 fiber orientations were made with 5–50 v/v fiber volume fractions. The PALF/MMA composites were subjected to a three-point flexural test to determine the effect of fiber volume fraction and fiber orientation on the flexural properties of the composite. The results showed that 40% v/v PALF/MMA composite with 0 fiber orientation recorded the highest flexural strength (50 MPa) and stiffness (1692 MPa). Considering the average load range exerted on PS, the flexural performance of the novel composite characterized in this work could be suitable for socket-limb load transfer for PS fabrication.Item A Molecular Modeling Approach to Identify Potential Antileishmanial Compounds Against the Cell Division Cycle (cdc)-2-Related Kinase 12 (CRK12) Receptor of Leishmania donovani(biomolecules, 2021) Broni, E.; Kwofie, S.K.; Kwofie, S.K.; Miller III, W.A.; Wilson, M.D.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 theWorld 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 M. 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. Themoleculeswere pharmacologically profiled as druglikewith 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.Item High frequency of the Duffy‑negative genotype and absence of Plasmodium vivax infections in Ghana(Malaria Journal, 2021) Brown, C.A.; Pappoe‑Ashong, P.J.; Duah, N.; Ghansah, A.; Asmah, H.; Afari, E.; Koram, K.A.Background: Recent studies from different malaria-endemic regions including western Africa have now shown that Plasmodium vivax can infect red blood cells (RBCs) and cause clinical disease in Duffy-negative people, though the Duffy-negative phenotype was thought to confer complete refractoriness against blood invasion with P. vivax. The actual prevalence of P. vivax in local populations in Ghana is unknown and little information is available about the distribution of Duffy genotypes. The aim of this study was to assess the prevalence of P. vivax in both asymptomatic and symptomatic outpatients and the distribution of Duffy genotypes in Ghana. Methods: DNA was extracted from dried blood spots (DBS) collected from 952 subjects (845 malaria patients and 107 asymptomatic persons) from nine locations in Ghana. Plasmodium species identification was carried out by nested polymerase chain reaction (PCR) amplification of the small-subunit (SSU) rRNA genes. For P. vivax detection, a second PCR of the central region of the Pvcsp gene was carried out. Duffy blood group genotyping was performed by allele-specific PCR to detect the presence of the FYES allele. Results: No cases of P. vivax were detected in any of the samples by both PCR methods used. Majority of infections (542, 94.8%) in the malaria patient samples were due to P. falciparum with only 1 infection (0.0017%) due to Plasmodium malariae, and 2 infections (0.0034%) due to Plasmodium ovale. No case of mixed infection was identified. Of the samples tested for the FYES allele from all the sites, 90.5% (862/952) had the FYES allele. All positive samples were genotyped as FY*B-33/FY*B-33 (Duffy-negative homozygous) and therefore classified as Fy(a−b−). Conclusions: No cases of P. vivax were detected by both PCRs and majority of the subjects tested carried the FYES allele. The lack of P. vivax infections observed can be attributed to the high frequency of the FYES allele that silences erythroid expression of the Duffy. These results provide insights on the host susceptibility for P. vivax infections that had not been investigated in Ghana before.Item Plasmonic Nanocomposite Implants for Interstitial Thermotherapy: Experimental and Computational Analysis(MDPI, 2021) Konku-Asase, Y.K.; Kan-Dapaah, K.The ferromagnetic implant (thermoseeds) technique offers desirable features for interstitial thermotherapy. However, its efficacy has been reported to be limited by issues that are related to the properties of the metal alloys that are used to fabricate them and the high number of thermoseeds needed to achieve therapeutic temperature levels. Here, we present the results of a combination of experimental and computational analysis of plasmonic nanocomposite implants (photoseeds)—a combination of Au nanoparticles (NPs) and poly-dimethylsiloxane (PDMS)—as a model material. We performed structural and optical characterization of the Au NPs and repared Au-PDMS nanocomposites, followed by an elucidation of the heat generation capabilities of the Au-PDMS photoseeds in aqueous solution and in-vitro cancer cell suspension. Based on the experimental results, we developed a three-dimensional (3D) finite element method (FEM) model to predict in-vivo thermal damage profiles in breast tissue. The optical absorbance of the Au-PDMS photoseeds were increasing with the concentration of Au NPs. The photothermal measurements and the in-vivo predictions showed that the photothermal properties of the photoseeds, characteristics of the laser sources, and the duration of heating can be tuned to achieve therapeutic temperature levels under in-vitro and in-vivo conditions. Collectively, the results demonstrate the feasibility of using photoseeds for interstitial thermotherapy.Item Molecular docking and dynamics simulations studies of OmpATb identifies four potential novel natural product-derived anti-Mycobacterium tuberculosis compounds(Computers in Biology and Medicine, 2020-05-03) Kwofie, S.K.; Adobor, C.; Quansah, E.; Bentil, J.; Ampadu, M.; Miller, W.A.; Wilson, M.D.The outer membrane protein A (OmpATb) of Mycobacterium tuberculosis is a virulence factor that neutralizes the host pH to impede the uptake of hydrophilic antitubercular drugs. Identifying natural compounds with the potential to inhibit OmpATb could allow circumvention of the porin-like activities of OmpATb. Four potential leads comprising ZINC000003958185, ZINC000000157405, ZINC000000001392 and ZINC000034268676 were obtained by virtual screening of 6394 diverse natural products. Characterization of the binding interactions of the potential leads with OmpATb revealed nine critical residues comprising ARG86, LEU110, LEU113, LEU114, ALA115, PHE142, SER145, VAL146, and PHE151. Molecular dynamics simulations also revealed very stable protein-lead complexes. Most residues contributed lower binding energies to the overall molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) binding free energies of the interactions between the molecules and OmpATb protein. Induced Fit Docking (IFD) of the compounds regenerated poses of the molecular docking using AutoDock Vina. These molecules could be starting templates for designing inhibitors to bypass the pore mediating activities of OmpATb. Based on structural similarity, ZINC000034268676 was suggested as a potential scaffold for designing efflux pump inhibitors of the gate mediating activities of OmpATb and may enhance the uptake of hydrophilic drugs to reduce the duration time of tuberculosis treatment. Furthermore, structurally similar compounds available in the DrugBank database with a similarity threshold of 0.7 have been reported to exhibit antitubercular and anti-mycobacterial activities. These biomolecules can be further characterized experimentally to corroborate their antitubercular activity. Also, the skeletons of the molecules can be adopted as sub-structures for the design of future anti-mycobacterial drugs.Item Vitamin Analysis, Trace Elements Content, and Their Extractabilities in Tetrapleura tetraptera(Hindawi Journal of Chemistry, 2020-01-11) Mensah, R.Q.; Oteng, P.; Otchere, J.K.; Adusei, S.; Tei-Mensah, E.Tetrapleura tetraptera is widely cherished in African traditional homes because of its alleged therapeutic and nutritional properties. +is present study aimed at determining the levels of vitamin A, C, E, and beta-carotene and trace element (Fe, Cu, Mn, Co, Se, and Zn) concentrations and their extractabilities in the pulp, seeds, and whole fruit (mixture of pulp and seeds) of T. tetraptera. +e total trace element concentration of Fe, Cu, Co, Mn, and Zn and their extractabilities (%) were determined using flame atomic absorption spectrometer (FAAS), whereas UV-VIS spectrophotometer was used to determine selenium concentration. +e trace element content (mg/kg) based on dry weight in the pulp, seeds, and whole fruit was Fe (162.00 ± 7.14, 115.00 ± 12.00, and 154.00 ± 25.20, respectively), Zn (31.60 ± 4.77, 43.40 ± 5.29, and 41.50 ± 8.97, respectively), Cu (16.10 ± 4.98, 11.90 ± 8.40, and 17.20 ± 14.50, respectively), Mn (55.30 ± 2.41, 156.00 ± 10.20, and 122.00 ± 5.29, respectively), Co (38.10 ± 6.40, 21.10 ± 7.15, and 44.00 ± 14.90, respectively), and Se (1.49 ± 0.17, 2.43 ± 0.28, and 2.97 ± 0.27 μg/g, respectively). +e mineral extractabilities (%) in the pulp, seeds, and whole fruit of T. tetraptera were established to be in the order Co > Zn > Fe > Cu > Se > Mn. Also, the chromatographic method (HPLC) was used to evaluate vitamin E concentration, and vitamin C and concentration of beta-carotene were calculated from the obtained concentration of vitamin A using a conversion factor by the titrimetric method. From the results of vitamin analysis, a significant difference (p < 0.05) was observed among the pulp, seeds, and whole fruit for vitamin C and E. However, no significant difference (p > 0.05) was perceived among these plant parts for vitamin A and beta-carotene. +is study has therefore revealed that the pulp, seeds, and whole fruit of T. tetraptera contain varying concentrations of vitamins and trace elements and has given many vital insights on which part of T. tetraptera to consume, as concentrations of these nutrients differ in the discrete parts of the fruitItem Molecular Informatics Studies of the Iron-Dependent Regulator (ideR) Reveal Potential Novel Anti-Mycobacterium ulcerans Natural Product-Derived Compounds(Molecules, 2019-06-21) Kwofie, S.K.; Enninful, K.S.; Yussif, J.A.; Asante, L.A.; Adjei, M.; Kan-Dapaah, K.; Tiburu, E.K.; Mensah, W.A.; Miller, W.A.III.; Mosi, L.; Wilson, M.D.Buruli ulcer is a neglected tropical disease caused by the bacterium Mycobacterium ulcerans. Its virulence is attributed to the dermo-necrotic polyketide toxin mycolactone, whose synthesis is regressed when its iron acquisition system regulated by the iron-dependent regulator (ideR) is deactivated. Interfering with the activation mechanism of ideR to inhibit the toxin’s synthesis could serve as a possible cure for Buruli ulcer. The three-dimensional structure of the ideR for Mycobacterium ulcerans was generated using homology modeling. A library of 832 African natural products (AfroDB), as well as five known anti-mycobacterial compounds were docked against the metal binding site of the ideR. The area under the curve (AUC) values greater than 0.7 were obtained for the computed Receiver Operating Characteristics (ROC) curves, validating the docking protocol. The identified top hits were pharmacologically profiled using Absorption, Distribution, Metabolism, Elimination and Toxicity (ADMET) predictions and their binding mechanisms were characterized. Four compounds with ZINC IDs ZINC000018185774, ZINC000095485921, ZINC000014417338 and ZINC000005357841 emerged as leads with binding energies of −7.7 kcal/mol, −7.6 kcal/mol, −8.0 kcal/mol and −7.4 kcal/mol, respectively. Induced Fit Docking (IFD) was also performed to account for the protein’s flexibility upon ligand binding and to estimate the best plausible conformation of the complexes. Results obtained from the IFD were consistent with that of the molecular docking with the lead compounds forming interactions with known essential residues and some novel critical residues Thr14, Arg33 and Asp17. A hundred nanoseconds molecular dynamic simulations of the unbound ideR and its complexes with the respective lead compounds revealed changes in the ideR’s conformations induced by ZINC000018185774. Comparison of the lead compounds to reported potent inhibitors by docking them against the DNA-binding domain of the protein also showed the lead compounds to have very close binding affinities to those of the potent inhibitors. Interestingly, structurally similar compounds to ZINC000018185774 and ZINC000014417338, as well as analogues of ZINC000095485921, including quercetin are reported to possess anti-mycobacterial activity. Also, ZINC000005357841 was predicted to possess anti-inflammatory and anti-oxidative activities, which are relevant in Buruli ulcer and iron acquisition mechanisms, respectively. The leads are molecular templates which may serve as essential scaffolds for the design of future anti-mycobacterium ulcerans agents.