Coronavirus disease 2019 diagnostics: key to Africa's recovery

© Copyright 2021, Mary Ann Liebert, Inc., publishersWith the coronavirus disease of 2019 (COVID-19) becoming a full-blown outbreak in Africa, coupled with many other challenges faced on the African continent, it is apparent that Africa continues to need diagnostics to enable case identification and recovery to this and future challenges. With the slow vaccination rates across the continent, reliable diagnostic tests will be in demand, likely for years to come. Thus, access to reliable diagnostic tools to detect the severe acute respiratory syndrome of the coronavirus-2 (SARS-CoV-2), the virus responsible for COVID-19, remain a critical pillar to monitor and contain new waves of COVID-19. Increasing the local capacity to manufacture and roll-out vaccines and decentralized COVID-19 testing are paramount for fighting the pandemic in Africa.SS is supported by an award from the Massachusetts Life Sciences Center Accelerating Coronavirus Testing Solutions (A.C.T.S). JG is funded by the African Academy of Sciences (Grants numbers GCA/MNCH/Round8/207/008 and SARSCov2-4-20-010) and the Royal Society, UK, Grant number FLR\R1\201314.info:eu-repo/semantics/publishedVersio

COVID-19 vaccinology landscape in Africa

More than two years after the start of COVID-19 pandemic, Africa still lags behind in terms vaccine distribution. This highlights the predicament of Africa in terms of vaccine development, deployment, and sustainability, not only for COVID-19, but for other major infectious diseases that plague the continent. This opinion discusses the challenges Africa faces in its race to vaccinate its people, and offers recommendations on the way forward. Specifically, to get out of the ongoing vaccine shortage trap, Africa needs to diversify investment not only to COVID-19 but also other diseases that burden the population. The continent needs to increase its capacity to acquire vaccines more equitably, improve access to technologies to enable local manufacture of vaccines, increase awareness on vaccines both in rural and urban areas to significantly reduce disease incidence of COVID-19 and as well as other prevalent diseases on the African continent such as HIV and TB. Such efforts will go a long way to reduce the disease burden in Africa

A Framework for Burnt Area Mapping and Evacuation Problem Using Aerial Imagery Analysis

The study aims to develop a holistic framework for maximum area coverage of a disaster region during a bushfire event. The monitoring and detection of bushfires are essential to assess the extent of damage, its direction of spread, and action to be taken for its containment. Bushfires limit human’s access to gather data to understand the ground situation. Therefore, the application of Unmanned Aerial Vehicles (UAVs) could be a suitable and technically advanced approach to grasp the dynamics of fires and take measures to mitigate them. The study proposes an optimization model for a maximal area coverage of the fire-affected region. The advanced Artificial Bee Colony (ABC) algorithm will be applied to the swarm of drones to capture images and gather data vital for enhancing disaster response. The captured images will facilitate the development of burnt area maps, locating access points to the region, estimating damages, and preventing the further spread of fire. The proposed algorithm showed optimum responses for exploration, exploitation, and estimation of the maximum height of the drones for the coverage of wildfires and it outperformed the benchmarking algorithm. The results showed that area coverage of the affected region was directly proportional to drone height. At a maximum drone height of 121 m, the area coverage was improved by 30%. These results further led to a proposed framework for bushfire relief and rescue missions. The framework is grounded on the ABC algorithm and requires the coordination of the State Emergency Services (SES) for quick and efficient disaster response

Notch3 Is Dispensable for Thymocyte β-Selection and Notch1-Induced T Cell Leukemogenesis

Notch1 (N1) signaling induced by intrathymic Delta-like (DL) ligands is required for T cell lineage commitment as well as self-renewal during “β-selection” of TCRβ+ CD4−CD8− double negative 3 (DN3) T cell progenitors. However, over-expression of the N1 intracellular domain (ICN1) renders N1 activation ligand-independent and drives leukemic transformation during β-selection. DN3 progenitors also express Notch3 (N3) mRNA, and over-expression of ligand-independent mutant N3 (ICN3) influences β-selection and drives T cell leukemogenesis. However, the importance of ligand-activated N3 in promoting β-selection and ICN1-induced T cell leukemogenesis has not been examined. To address these questions we generated mice lacking functional N3. We confirmed that DN3 progenitors express N3 protein using a N3-specific antibody. Surprisingly however, N3-deficient DN3 thymocytes were not defective in generating DP thymocytes under steady state conditions or in more stringent competition assays. To determine if N3 co-operates with N1 to regulate β-selection, we generated N1;N3 compound mutants. However, N3 deficiency did not exacerbate the competitive defect of N1+/− DN3 progenitors, demonstrating that N3 does not compensate for limiting N1 during T cell development. Finally, N3 deficiency did not attenuate T cell leukemogenesis induced by conditional expression of ICN1 in DN3 thymocytes. Importantly, we showed that in contrast to N1, N3 has a low binding affinity for DL4, the most abundant intrathymic DL ligand. Thus, despite the profound effects of ectopic ligand-independent N3 activation on T cell development and leukemogenesis, physiologically activated N3 is dispensable for both processes, likely because N3 interacts poorly with intrathymic DL4

UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires

Australia is a regular recipient of devastating bushfires that severely impacts its economy, landscape, forests, and wild animals. These bushfires must be managed to save a fortune, wildlife, and vegetation and reduce fatalities and harmful environmental impacts. The current study proposes a holistic model that uses a mixed-method approach of Geographical Information System (GIS), remote sensing, and Unmanned Aerial Vehicles (UAV)-based bushfire assessment and mitigation. The fire products of Visible Infrared Imager Radiometer Suite (VIIRS) and Moderate-resolution Imaging Spectroradiometer (MODIS) are used for monitoring the burnt areas within the Victorian Region due to the 2020 bushfires. The results show that the aggregate of 1500 m produces the best output for estimating the burnt areas. The identified hotspots are in the eastern belt of the state that progressed north towards New South Wales. The R2 values between 0.91–0.99 indicate the fitness of methods used in the current study. A healthy z-value index between 0.03 to 2.9 shows the statistical significance of the hotspots. Additional analysis of the 2019–20 Victorian bushfires shows a widespread radius of the fires associated with the climate change and Indian Ocean Dipole (IOD) phenomenon. The UAV paths are optimized using five algorithms: greedy, intra route, inter route, tabu, and particle swarm optimization (PSO), where PSO search surpassed all the tested methods in terms of faster run time and lesser costs to manage the bushfires disasters. The average improvement demonstrated by the PSO algorithm over the greedy method is approximately 2% and 1.2% as compared with the intra route. Further, the cost reduction is 1.5% compared with the inter-route scheme and 1.2% compared with the intra route algorithm. The local disaster management authorities can instantly adopt the proposed system to assess the bushfires disasters and instigate an immediate response plan

UAVs in disaster management: application of integrated aerial Imagery and convolutional neural network for flood detection

Floods have been a major cause of destruction, instigating fatalities and massive damageto the infrastructure and overall economy of the affected country. Flood-related devastation resultsin the loss of homes, buildings, and critical infrastructure, leaving no means of communicationor travel for the people stuck in such disasters. Thus, it is essential to develop systems that candetect floods in a region to provide timely aid and relief to stranded people, save their livelihoods,homes, and buildings, and protect key city infrastructure. Flood prediction and warning systemshave been implemented in developed countries, but the manufacturing cost of such systems istoo high for developing countries. Remote sensing, satellite imagery, global positioning system,and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods.However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not beenexplored in these contexts to instigate a swift disaster management response to minimize damage toinfrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection methodbased on Convolutional Neural Network (CNN) to extract flood-related features from the imagesof the disaster zone. This method is effective in assessing the damage to local infrastructures in thedisaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, whereboth pre-and post-disaster images are collected through UAVs. For the training phase,2150 imagepatches are created by resizing and cropping the source images. These patches in the training datasettrain the CNN model to detect and extract the regions where a flood-related change has occurred.The model is tested against both pre-and post-disaster images to validate it, which has positive flooddetection results with an accuracy of 91%. Disaster management organizations can use this modelto assess the damages to critical city infrastructure and other assets worldwide to instigate properdisaster responses and minimize the damages. This can help with the smart governance of the citieswhere all emergent disasters are addressed promptl

COVID-19 vaccinology landscape in Africa

More than two years after the start of COVID-19 pandemic, Africa still lags behind in terms vaccine distribution. This highlights the predicament of Africa in terms of vaccine development, deployment, and sustainability, not only for COVID-19, but for other major infectious diseases that plague the continent. This opinion discusses the challenges Africa faces in its race to vaccinate its people, and offers recommendations on the way forward. Specifically, to get out of the ongoing vaccine shortage trap, Africa needs to diversify investment not only to COVID-19 but also other diseases that burden the population. The continent needs to increase its capacity to acquire vaccines more equitably, improve access to technologies to enable local manufacture of vaccines, increase awareness on vaccines both in rural and urban areas to significantly reduce disease incidence of COVID-19 and as well as other prevalent diseases on the African continent such as HIV and TB. Such efforts will go a long way to reduce the disease burden in Africa.The Massachusetts Life Sciences Center Accelerating Coronavirus Testing Solutions, Nina Ireland Program for Lung Health, the Chan Zuckerberg Biohub Initiative and Africa Academy of Sciences funding for COVID-19 Research & Development goals for Africa.https://www.frontiersin.org/journals/immunologyam2023BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant Patholog

Drone-as-a-Service (DaaS) for COVID-19 self-testing kits delivery in smart healthcare setups: A technological perspective

Drones have gained increasing attention in the healthcare industry for mobility and accessibility to remote areas. This perspective-based study proposes a drone-based sample collection system whereby COVID-19 self-testing kits are delivered to and collected from potential patients. This is achieved using the drone as a service (DaaS). A mobile application is also proposed to depict drone navigation and destination location to help ease the process. Through this app, the patient could contact the hospital and give details about their medical condition and the type of emergency. A hypothetical case study for Geelong, Australia, was carried out, and the drone path was optimized using the Artificial Bee Colony (ABC) algorithm. The proposed method aims to reduce person-to-person contact, aid the patient at their home, and deliver any medicine, including first aid kits, to support the patients until further assistance is provided. Artificial intelligence and machine learning-based algorithms coupled with drones will provide state-of-the-art healthcare systems technology

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients

Synthetic mycobacterial diacyl trehaloses reveal differential recognition by human T cell receptors and the C-type lectin Mincle

The cell wall of Mycobacterium tuberculosis is composed of diverse glycolipids which potentially interact with the human immune system. To overcome difficulties in obtaining pure compounds from bacterial extracts, we recently synthesized three forms of mycobacterial diacyltrehalose (DAT) that differ in their fatty acid composition, DAT1, DAT2, and DAT3. To study the potential recognition of DATs by human T cells, we treated the lipid-binding antigen presenting molecule CD1b with synthetic DATs and looked for T cells that bound the complex. DAT1- and DAT2-treated CD1b tetramers were recognized by T cells, but DAT3-treated CD1b tetramers were not. A T cell line derived using CD1b-DAT2 tetramers showed that there is no cross-reactivity between DATs in an IFN-γ release assay, suggesting that the chemical structure of the fatty acid at the 3-position determines recognition by T cells. In contrast with the lack of recognition of DAT3 by human T cells, DAT3, but not DAT1 or DAT2, activates Mincle. Thus, we show that the mycobacterial lipid DAT can be both an antigen for T cells and an agonist for the innate Mincle receptor, and that small chemical differences determine recognition by different parts of the immune system