The coronavirus disease (COVID-19) is caused by a positive-stranded RNA virus called severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), belonging to the Coronaviridae family. This virus originated in Wuhan City, China, and became the cause of a multiwave pandemic that has killed 3.46 million people worldwide as of May 22, 2021. The havoc intensified with the emergence of SARS-CoV-2 variants (B.1.1.7; Alpha, B.1.351; Beta, P.1; Gamma, B.1.617; Delta, B.1.617.2; Delta-plus, B.1.525; Eta, and B.1.429; Epsilon etc.) due to mutations generated during replication. More variants may emerge to cause additional pandemic waves. The most promising approach for combating viruses and their emerging variants lies in prophylactic vaccines. Several vaccine candidates are being developed using various platforms, including nucleic acids, live attenuated virus, inactivated virus, viral vectors, and protein-based subunit vaccines. In this unprecedented time, 12 vaccines against SARS-CoV-2 have been phased in following WHO approval, 184 are in the preclinical stage, and 100 are in the clinical development process. Many of them are directed to elicit neutralizing antibodies against the viral spike protein (S) to inhibit viral entry through the ACE-2 receptor of host cells. Inactivated vaccines, to the contrary, provide a wide range of viral antigens for immune activation. Being an intracellular pathogen, the cytotoxic CD8+ T Cell (CTL) response remains crucial for all viruses, including SARS-CoV-2, and needs to be explored in detail. In this review, we try to describe and compare approved vaccines against SARS-CoV-2 that are currently being distributed either after phase III clinical trials or for emergency use. We discuss immune responses induced by various candidate vaccine formulations; their benefits, potential limitations, and effectiveness against variants; future challenges, such as antibody-dependent enhancement (ADE); and vaccine safety issues and their possible resolutions. Most of the current vaccines developed against SARS-CoV-2 are showing either promising or compromised efficacy against new variants. Multiple antigen-based vaccines (multivariant vaccines) should be developed on different platforms to tackle future variants. Alternatively, recombinant BCG, containing SARS-CoV-2 multiple antigens, as a live attenuated vaccine should be explored for long-term protection. Irrespective of their efficacy, all vaccines are efficient in providing protection from disease severity. We must insist on vaccine compliance for all age groups and work on vaccine hesitancy globally to achieve herd immunity and, eventually, to curb this pandemic.
Background COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. Methodology Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. Results Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 ( p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. Conclusions We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02317-0.
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