ABSTRACT
Background: Ad26.COV2.S is a single-shot vaccine that has demonstrated clinical efficacy against symptomatic COVID-19. In this study, we report the durability of immune responses in 20 rhesus macaques received single-shot Ad26.COV2.S and the immunogenicity of a booster shot at 8-10 months following the initial immunization. Methods: Animals were immunized by intramuscular route with 1011 vp (N=10) or 5x1010 vp (N=10) Ad26.COV2.S and were followed for either 230 or 315 days. Animals were then boosted with 5x1010 vp Ad26.COV2.S (N=10). Humoral immune responses including RBD-specific Ig ELISA and pseudovirus-based virus neutralization response were monitored. Circulating RBD-specific memory B cells and bone marrow plasma cells were assessed by multiparameter flow cytometry. Results: Ad26.COV2.S elicited robust and comparable RBD-specific binding and neutralizing antibody responses in animals that received the 1011 vp and 5x1010 vp doses, which peaked on days 28-56, and then showed a biphasic decay. All animals showed binding antibody responses for the duration of follow-up, and 17 of 20 animals showed neutralizing antibody responses by day 230-315. RBD-specific memory B cell response peaked on day 14-28 followed by a gradual decline, and remained detectable in 17 of 20 animals by day 230-315. On day 315 following vaccination, bone marrow RBD-specific PCs were detected in the majority of vaccinated macaques, including in all animals that received the 1011 vp dose. Following Ad26.COV2.S boost immunization, RBD-specific binding antibody responses increased 31-69 fold compared with pre-boost levels against the ancestral (WA1/2020), alpha (B.1.1.7), beta (B.1.351), kappa (B.1.617.1), and delta (B.1.617.2) SARS-CoV-2 variants. Neutralizing antibody responses increased 23-43 fold compared with pre-boost levels against the ancestral, alpha, beta, gamma (P.1), kappa, and delta SARS-CoV-2 variants. Antigen-specific memory B cell response also increased 8 fold following the boost immunization. Conclusion: Ad26.COV2.S elicited durable antibody and B cell responses, and a late boost with Ad26.COV2.S resulted in a dramatic increase in humoral immunity that were highly cross-reactive across multiple SARS-CoV-2 variants in rhesus macaques. These data contribute to our understanding of Ad26.COV2.S durability and boostability, and provide important data to inform COVID-19 vaccine boosting strategies in humans.
ABSTRACT
Background: The CVnCoV (CureVac) mRNA vaccine for SARS-CoV-2 has recently been evaluated in a Phase IIb/III efficacy trial in humans. CV2CoV is a second-generation mRNA vaccine with optimized non-coding regions and enhanced antigen expression. Methods: Here we report a head-to-head study of the immunogenicity and protective efficacy of CVnCoV and CV2CoV in nonhuman primates. We immunized 18 cynomolgus macaques with two doses of 12 ug of lipid nanoparticle formulated CVnCoV, CV2CoV, or sham (N=6/group). Results: CV2CoV induced substantially higher binding and neutralizing antibodies, memory B cell responses, and T cell responses as compared with CVnCoV. CV2CoV also induced more potent neutralizing antibody responses against SARS-CoV-2 variants, including B.1.351 (beta), B.1.617.2 (delta), and C.37 (lambda). While CVnCoV provided partial protection against SARS-CoV-2 challenge, CV2CoV afforded robust protection with markedly lower viral loads in the upper and lower respiratory tract. Antibody responses correlated with protective efficacy. Conclusion: These data demonstrate that optimization of non-coding regions can greatly improve the immunogenicity and protective efficacy of an mRNA SARS-CoV-2 vaccine in nonhuman primates.
ABSTRACT
Big Data is being used in various aspects of technology. The quality of the data being used is essential and needs to be accurate, reliable, and free of defects. The difficulty in improving the quality of big data can be overcome by leveraging computing resources and advanced techniques. In this paper, we propose a solution that utilizes a machine learning (ML) model combined with a data quality model to improve the quality of data. An auto encoder neural network that detects the anomalies in the data is used as the Machine Learning model. This is followed by using the data quality model to ensure the data meets appropriate data quality characteristics. The results obtained from our solution show that the quality of data can be improved efficiently and effortlessly which in turn aids researchers to achieve better results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
While the COVID-19 pandemic still rages on in the United States, leaving in its wake hundreds of thousands of infected patients, families shattered by the untimely death of their loved ones, an economy in free fall that hit all-time highs barely a few months ago, and a fearful citizenry unsure of what the future holds, the effect it has had on residency and fellowship training programs across the country may appear inconsequential to the general populace. However, if you are a graduating trainee confronted with this unusual set of circumstances, fear of the virus is not the only thing that is foremost in your mind. We discuss the unique challenges our pain fellowship program continues to deal with during this pandemic and particularly its impact on our fellows. It is entirely likely these concerns are mirrored in academic programs all over the United States.