The recently emerged novel coronavirus, "severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)", caused a highly contagious disease called coronavirus disease 2019 (COVID-19). The virus was ...first reported from Wuhan city in China in December, 2019, which in less than three months spread throughout the globe and was declared a global pandemic by the World Health Organization (WHO) on 11th of March, 2020. So far, the ongoing pandemic severely damaged the world's most developed countries and is becoming a major threat for low- and middle-income countries. The poorest continent, Africa with the most vulnerable populations to infectious diseases, is predicted to be significantly affected by the ongoing COVID-19 outbreak. Therefore, in this review we collected and summarized the currently available literature on the epidemiology, etiology, vulnerability, preparedness and economic impact of COVID-19 in Africa, which could be useful and provide necessary information on ongoing COVID-19 pandemics in the continent. We also briefly summarized the concomitance of the COVID-19 pandemic and global warming.
Antibiotics are known as emergent pollutants because of their toxicological properties. Due to continuous discharge and persistence in the aquatic environment, antibiotics are detected almost in ...every environmental matrix. Therefore antibiotics that are polluting the aquatic environment have gained significant research interest for their removal. Several techniques have been used to remove pollutants, but appropriate technology is still to be found. This review addresses the use of modified and cheap materials for antibiotic removal from the environment.
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A paradigm shift of candidiasis from Candida albicans to non‐albicans Candida species has fundamentally increased with the advent of C. auris. C. auris, despite being a newly emerged ...multidrug‐resistant fungal pathogen, is associated with severe invasive infections and outbreaks with high mortality rates. Initially reported from Japan in 2009, C. auris have now been found in different countries on all the continents except Antarctica. Due to its capability of nosocomial transmission and forming adherent biofilms on clinically important substrates, a high number of related hospital outbreaks have been reported worldwide. As C. auris is a multidrug‐resistant pathogen and is prone to misidentification by available conventional methods, it becomes difficult to detect and manage C. auris infection and also limits the therapeutic options against this deadly pathogen. The emergence of multidrug‐resistant C. auris advocates and amplifies the vigilance of early diagnosis and appropriate treatment of fungal infections. In this review, we discussed the nine‐year‐old history of C. auris—its trends in global emergence, epidemiological relatedness, isolation, mortality, associated risk factors, virulence factors, drug resistance and susceptibility testing, diagnostic challenges, microbiological characteristics, therapeutic options and infection prevention and control associated with this pathogen.
Candida auris, a decade old Candida species, has been identified globally as a significant nosocomial multidrug resistant (MDR) pathogen responsible for causing invasive outbreaks. Biofilms and ...overexpression of efflux pumps such as Major Facilitator Superfamily and ATP Binding Cassette are known to cause multidrug resistance in Candida species, including C. auris. Therefore, targeting these factors may prove an effective approach to combat MDR in C. auris. In this study, 25 clinical isolates of C. auris from different hospitals of South Africa were used. All the isolates were found capable enough to form biofilms on 96-well flat bottom microtiter plate that was further confirmed by MTT reduction assay. In addition, these strains have active drug efflux mechanism which was supported by rhodamine-6-G extracellular efflux and intracellular accumulation assays. Antifungal susceptibility profile of all the isolates against commonly used drugs was determined following CLSI recommended guidelines. We further studied the role of farnesol, an endogenous quorum sensing molecule, in modulating development of biofilms and drug efflux in C. auris. The MIC for planktonic cells ranged from 62.5-125 mM, and for sessile cells was 125 mM (4h biofilm) and 500 mM (12h and 24h biofilm). Furthermore, farnesol (125 mM) also suppresses adherence and biofilm formation by C. auris. Farnesol inhibited biofilm formation, blocked efflux pumps and downregulated biofilm- and efflux pump- associated genes. Modulation of C. auris biofilm formation and efflux pump activity by farnesol represent a promising approach for controlling life threatening infections caused by this pathogen.
Machine learning (ML) based forecasting mechanisms have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of actions. The ML models ...have long been used in many application domains which needed the identification and prioritization of adverse factors for a threat. Several prediction methods are being popularly used to handle forecasting problems. This study demonstrates the capability of ML models to forecast the number of upcoming patients affected by COVID-19 which is presently considered as a potential threat to mankind. In particular, four standard forecasting models, such as linear regression (LR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and exponential smoothing (ES) have been used in this study to forecast the threatening factors of COVID-19. Three types of predictions are made by each of the models, such as the number of newly infected cases, the number of deaths, and the number of recoveries in the next 10 days. The results produced by the study proves it a promising mechanism to use these methods for the current scenario of the COVID-19 pandemic. The results prove that the ES performs best among all the used models followed by LR and LASSO which performs well in forecasting the new confirmed cases, death rate as well as recovery rate, while SVM performs poorly in all the prediction scenarios given the available dataset.
With a limited arsenal of available antifungal drugs and drug-resistance emergence, strategies that seek to reduce
Candida
immune evasion and virulence could be a promising alternative option. ...Harnessing metal homeostasis against
C
.
albicans
has gained wide prominence nowadays as a feasible antifungal strategy. Herein, the effect of magnesium (Mg) deprivation on the immune evasion mechanisms of
C
.
albicans
is demonstrated. We studied host pathogen interaction by using the THP-1 cell line model and explored the avenue that macrophage-mediated killing was enhanced under Mg deprivation, leading to altered cytokine (TNFα, IL-6 and IL10) production and reduced pyroptosis. Insights into the mechanisms revealed that hyphal damage inside the macrophage was diminished under Mg deprivation. Additionally, Mg deprivation led to cell wall remodelling; leading to enhanced β-1,3-glucan exposure, crucial for immune recognition, along with concomitant alterations in chitin and mannan levels. Furthermore, vacuole homeostasis was disrupted under Mg deprivation, as revealed by abrogated morphology and defective acidification of the vacuole lumen. Together, we demonstrated that Mg deprivation affected immune evasion mechanisms by: reduced hyphal damage, enhanced β-1,3-glucan exposure and altered vacuole functioning. The study establishes that Mg availability is indispensable for successful
C
.
albicans
immune evasion and specific Mg dependent pathways could be targeted for therapy.
Biopolymers have received widespread attention due to their beneficial characteristics, such as like easy processing, biodegradability and biocompatibility. Concurrently, inorganic polyoxometalates ...(POMs), a class of metal-oxygen anionic and nanosized clusters of early transition metals, have a wide range of attractive functions and are used in biomedical and industrial fields. In this communication, we report a simple approach to create ammonium metavanadate (AMV)-biopolymer composite hydrogel beads that combine the advantages of biopolymers and POM clusters. Crosslinking was achieved through electrostatic interactions between cationic chitosan, chitosan/gelatin, chitosan/methylcellulose and AMV (NH
VO
). The as-prepared hydrogel beads were yellow in colour and exhibited a high mechanical strength. They were characterized using FT-IR spectroscopy and SEM, to confirm hydrogel formation and evaluate their surface morphology. It was demonstrated that the fabricated hydrogel blend possessed tuneable physicochemical properties, good swelling behaviour (with a maximum swelling of 432%), excellent luminescence and adsorption, and remarkable biomedical properties. Batch adsorption experiments demonstrated that the beads had an equilibrium adsorption capacity of 539 mg g
for the removal of Congo red dye from aqueous solutions, which was more efficient than the most reported natural biosorbents. Due to their luminescence properties these hydrogel beads showed excellent selective sensing behaviour toward ascorbic acid with a LOD of 1.06 μM. The hydrogels were also assessed for their antibacterial activity, and were tested against
,
,
, and
. The cytotoxicity results showed that the embedded POMs exhibited dose-dependent cytotoxicity against the embryonic kidney cell line (HEK).
β-Lactams are the most widely used and effective antibiotics for the treatment of infectious diseases. Unfortunately, bacteria have developed several mechanisms to combat these therapeutic agents. ...One of the major resistance mechanisms involves the production of β-lactamase that hydrolyzes the β-lactam ring thereby inactivating the drug. To overcome this threat, the small molecule β-lactamase inhibitors (e.g., clavulanic acid, sulbactam and tazobactam) have been used in combination with β-lactams for treatment. However, the bacterial resistance to this kind of combination therapy has evolved recently. Therefore, multiple attempts have been made to discover and develop novel broad-spectrum β-lactamase inhibitors that sufficiently work against β-lactamase producing bacteria. β-lactamase inhibitory proteins (BLIPs) (e.g., BLIP, BLIP-I and BLIP-II) are potential inhibitors that have been found from soil bacterium
spp. BLIPs bind and inhibit a wide range of class A β-lactamases from a diverse set of Gram-positive and Gram-negative bacteria, including TEM-1, PC1, SME-1, SHV-1 and KPC-2. To the best of our knowledge, this article represents the first systematic review on β-lactamase inhibitors with a particular focus on BLIPs and their inherent properties that favorably position them as a source of biologically-inspired drugs to combat antimicrobial resistance. Furthermore, an extensive compilation of binding data from β-lactamase⁻BLIP interaction studies is presented herein. Such information help to provide key insights into the origin of interaction that may be useful for rationally guiding future drug design efforts.
•Raw time series data is transformed into meaningful representations through the application of various techniques, including statistical feature extraction, spectral analysis, time series ...decomposition, and ensemble empirical mode decomposition (EEMD).•This study presents novel Hybrid Graph Neural Network (GNN) which leverages temporal and feature-based relationships to capture complex patterns.•The attention mechanism is utilised to capture the significance of various nodes and edges within the graph.•Hybrid GNN with analytic hierarchy process (AHP) outperforms and correlates well with ground truth rankings in extensive empirical experiments on diverse region electrical demand and price time series.
In the energy sector, it is important to meticulously choose an accurate forecasting model because making informed decisions is crucial for optimal grid operation. This article proposes a hybrid graph neural network (GNN) that successfully captures complex patterns by combining interactions based on features and time. The proposed architecture uses diverse decomposition methods, such as statistical, dynamic, and spectral, to uncover hidden patterns. The integration of attention and graph convolution layers improves the flow of information, and the cross-modal fusion layer competently combines nodes and edges. This configuration can efficaciously assimilate disparate features, providing an advantage over other approaches. The hybrid GNN with an analytic hierarchy process (AHP) performs better than the existing models when tested exhaustively on diverse regional energy demand and pricing time series. It captures complex patterns more effectively than K-nearest neighbour (KNN), random forest regressor (RFR), and GNN-based model suggestion methods. For the nonstationary price data from New South Wales and the Punjab energy demand time series, the Kendall’s Tau coefficient is 0.73 and 0.81 and the Spearman’s Rank coefficient is 0.74 and 0.91, respectively. This paper advances the field of time series forecasting by offering a novel strategy for improving model proposals by efficiently merging hybrid GNN with multiple feature modalities.
Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its ...widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.
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