ABSTRACT
The opportunistic pathogen Pseudomonas aeruginosa (Pa) causes severe nosocomial infections, especially in immunocompromised individuals and the elderly. Increasing drug resistance, the absence of a licensed vaccine and increased hospitalizations due to SARS-CoV-2 have made Pa a major healthcare risk. To address this, we formulated a candidate subunit vaccine against Pa (L-PaF), by fusing the type III secretion system tip and translocator proteins with LTA1 in an oil-in-water emulsion (ME). This was mixed with the TLR4 agonist (BECC438b). Lung mRNA sequencing showed that the formulation activates genes from multiple immunological pathways eliciting a protective Th1-Th17 response following IN immunization. Following infection, however, the immunized mice showed an adaptive response while the PBS-vaccinated mice experienced rapid onset of an inflammatory response. The latter displayed a hypoxic lung environment with high bacterial burden. Finally, the importance of IL-17 and immunoglobulins were demonstrated using knockout mice. These findings suggest a need for a balanced humoral and cellular response to prevent the onset of Pa infection and that our formulation could elicit such a response.
ABSTRACT
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
ABSTRACT
The impact of SARS-CoV-2 and COVID-19 disease susceptibility varies depending on the age and health status of an individual. Currently, there are more than 140 COVID-19 vaccines under development. However, the challenge will be to induce an effective immune response in the elderly population. Analysis of B cell epitopes indicates the minor role of the stalk domain of spike protein in viral neutralization due to low surface accessibility. Nevertheless, the accumulation of mutations in the receptor-binding domain (RBD) might reduce the vaccine efficacy in all age groups. We also propose the concept of chimeric vaccines based on the co-expression of SARS-CoV-2 spike and influenza hemagglutinin (HA) and matrix protein 1 (M1) proteins to generate chimeric virus-like particles (VLP). This review discusses the possible approaches by which influenza-specific memory repertoire developed during the lifetime of the elderly populations can converge to mount an effective immune response against the SARS-CoV-2 spike protein with the possibilities of designing single vaccines for COVID-19 and influenza. HighlightsImmunosenescence aggravates COVID-19 symptoms in elderly individuals.Low immunogenicity of SARS-CoV-2 vaccines in elderly population.Tapping the memory T and B cell repertoire in elderly can enhance vaccine efficiency.Chimeric vaccines can mount effective immune response against COVID-19 in elderly.Chimeric vaccines co-express SARS-CoV-2 spike and influenza HA and M1 proteins.