COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large

Biosynthesis of Silver Nanoparticles Using Bersama engleriana Fruits Extracts and Their Potential Inhibitory Effect on Resistant Bacteria

The absence of novel, safe, and effective bactericide is an urgent concern worldwide and remains a challenge in scientific communities. The unique proprieties of silver nanoparticles (SNPs) synthesized from plant extracts make them a suitable candidate to overcome these limitations. Herein, we synthesized SNPs from Bersama engleriana fruit (BEfr) extracts and determined their potential antibacterial activity and mode of action. SNPs were synthesized from BEfr methanolic fruit extracts at 25 and 70 ◦C, and the antibacterial effectiveness of SNPs against bacterial strains was investigated. The surface plasmon resonance peaked at 430.18 and 434.08 nm, respectively, for SNPs synthesized at 25 and 70 ◦C, confirming SNPs synthesis. BEfr-SNPs had minimum inhibitory concentrations (MIC) range of 0.234 to >50 µg/mL, which was 30-fold greater than extract alone (MIC of 500 µg/mL). BEfrSNPs-25 ◦C was potent against six bacterial strains (S. aureus, S. enterica, MRS. aureus, K. pneumonia, and S. pyogenes), with MIC range of 0.339 to 6.25 µg/mL. The mode of action of BEfr-SNPs-25 ◦C was achieved by an MRSA bacteria strain outer membrane and DNA nucleotide linkage. These results suggest that our synthesized SNPs, especially BEfr-SNPs-25 ◦C, demonstrated an enhanced antibacterial effect and could be potential candidates for bacterial infection treatment

Modelling and Interpretation of Adsorption Isotherms

The need to design low-cost adsorbents for the detoxification of industrial effluents has been a growing concern for most environmental researchers. So modelling of experimental data from adsorption processes is a very important means of predicting the mechanisms of various adsorption systems. Therefore, this paper presents an overall review of the applications of adsorption isotherms, the use of linear regression analysis, nonlinear regression analysis, and error functions for optimum adsorption data analysis

Generation-3 Polyamidoamine Dendrimer-Silica Composite: Preparation and Cd(II) Removal Capacity

Generation-3 polyamidoamine (PAMAM) dendrimer was implanted on silica to produce a very good adsorbent (G-3 PAMAM-SGA). The composite was characterized and used for the removal of Cd(II) ions from aqueous solution. Kinetic data fit the Lagergren pseudo-second-order model and also follow the intraparticle diffusion kinetic model to an extent, which is an indication that the sorption process is controlled by both mechanisms: intraparticle/film layer and adsorption inside the pores/crevices of the composite. Equilibrium sorption data of Cd(II) on G-3 PAMAM-SGA fit the Freundlich isotherm (R2 = 0.9993) which is indicative of multilayered adsorption that occurred on heterogeneous surfaces. The ΔG° values for all temperatures studied were negative, which indicated a spontaneous and feasible process. The result implies that G-3 PAMAM-SGA is a promising adsorbent for microscale scavenging of Cd(II) ions in aqueous solutions

Equilibrium, kinetic and thermodynamic studies of the uptake of copper by layered double hydroxide

This study explored the adsorption capacity of Mg/Al layered double hydroxide (LDH) for the removal of Cu2+ from aqueous solutions after synthesis and characterization. The effect of various operational parameters such as concentration, temperature and sorption time on the adsorption of Cu2+ was investigated using batch adsorption process experiments. It was found that layered double hydroxide (LDH) can be used as adsorbent for the removal of copper ions in aqueous solution containing low concentration of the metal salt. The average values of activation energy, isosteric heat of adsorption, entropy and enthalpy were 1.447, 12.9, 0.0137 and –4.8390 kJ/mol, respectively. This shows that the adsorption of the metal ion on the adsorbent follows a physical adsorption mechanism. The kinetic results conform to pseudo-second order model (R2 = 0.9959) and second order kinetic model (R2 = 0.9952) while the adsorption characteristics of the adsorbent followed both Langmuir and Freundlich adsorption isotherm models

Immunoinformatics Studies and Design of a Potential Multi-Epitope Peptide Vaccine to Combat the Fatal Visceral Leishmaniasis

Leishmaniasis is a neglected tropical disease caused by parasitic intracellular protozoa of the genus Leishmania. The visceral form of this disease caused by Leishmania donovani continues to constitute a major public health crisis, especially in countries of endemicity. In some cases, it is asymptomatic and comes with acute and chronic clinical outcomes such as weight loss, pancytopenia, hepatosplenomegaly, and death if left untreated. Over the years, the treatment of VL has relied solely on chemotherapeutic agents, but unfortunately, these drugs are now faced with challenges. Despite all efforts, no successful vaccine has been approved for VL. This could be as a result of limited knowledge/understanding of the immune mechanisms necessary to regulate parasite growth. Using a computational approach, this study explored the prospect of harnessing the properties of a disulfide isomerase protein of L. donovani amastigotses to develop a multi-epitope subunit vaccine candidate against the parasite. We designed a 248-amino acid multi-epitope vaccine with a predicted antigenicity probability of 0.897372. Analyses of immunogenicity, allergenicity, and multiple physiochemical parameters indicated that the constructed vaccine candidate was stable, non-allergenic, and immunogenic, making it compatible with humans and hence, a potentially viable and safe vaccine candidate against Leishmania spp. Parasites

Biosynthesis of Silver Nanoparticles Using Bersama engleriana Fruits Extracts and Their Potential Inhibitory Effect on Resistant Bacteria

The absence of novel, safe, and effective bactericide is an urgent concern worldwide and remains a challenge in scientific communities. The unique proprieties of silver nanoparticles (SNPs) synthesized from plant extracts make them a suitable candidate to overcome these limitations. Herein, we synthesized SNPs from Bersama engleriana fruit (BEfr) extracts and determined their potential antibacterial activity and mode of action. SNPs were synthesized from BEfr methanolic fruit extracts at 25 and 70 °C, and the antibacterial effectiveness of SNPs against bacterial strains was investigated. The surface plasmon resonance peaked at 430.18 and 434.08 nm, respectively, for SNPs synthesized at 25 and 70 °C, confirming SNPs synthesis. BEfr-SNPs had minimum inhibitory concentrations (MIC) range of 0.234 to >50 µg/mL, which was 30-fold greater than extract alone (MIC of 500 µg/mL). BEfr-SNPs-25 °C was potent against six bacterial strains (S. aureus, S. enterica, MRS. aureus, K. pneumonia, and S. pyogenes), with MIC range of 0.339 to 6.25 µg/mL. The mode of action of BEfr-SNPs-25 °C was achieved by an MRSA bacteria strain outer membrane and DNA nucleotide linkage. These results suggest that our synthesized SNPs, especially BEfr-SNPs-25 °C, demonstrated an enhanced antibacterial effect and could be potential candidates for bacterial infection treatment

An Interpretable Machine Learning Approach for Hepatitis B Diagnosis

Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious public health problem globally. Substantial efforts have been made to apply machine learning in detecting the virus. However, the application of model interpretability is limited in the existing literature. Model interpretability makes it easier for humans to understand and trust the machine-learning model. Therefore, in this study, we used SHapley Additive exPlanations (SHAP), a game-based theoretical approach to explain and visualize the predictions of machine learning models applied for hepatitis B diagnosis. The algorithms used in building the models include decision tree, logistic regression, support vector machines, random forest, adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost), and they achieved balanced accuracies of 75%, 82%, 75%, 86%, 92%, and 90%, respectively. Meanwhile, the SHAP values showed that bilirubin is the most significant feature contributing to a higher mortality rate. Consequently, older patients are more likely to die with elevated bilirubin levels. The outcome of this study can aid health practitioners and health policymakers in explaining the result of machine learning models for health-related problems