Biology subjects

Chellappa, S.

Publications and source records attributed to Chellappa, S..

2 recordsLinked to original sources

bioRxiv2020

Single dose immunization with a COVID-19 DNA vaccine encoding a chimeric homodimeric protein targeting receptor binding domain (RBD) to antigen-presenting cells induces rapid, strong and long-lasting neutralizing IgG, Th1 dominated CD4+ T cells and strong CD8+ T cell responses in mice

The pandemic caused by the SARS-CoV-2 virus in 2020 has led to a global public health emergency, and non-pharmaceutical interventions required to limit the viral spread are severely affecting health and economies across the world. A vaccine providing rapid and persistent protection across populations is urgently needed to prevent disease and transmission. We here describe the development of novel COVID-19 DNA plasmid vaccines encoding homodimers consisting of a targeting unit that binds chemokine receptors on antigen-presenting cells (human MIP-1 /LD78{beta}), a dimerization unit (derived from the hinge and CH3 exons of human IgG3), and an antigenic unit (Spike or the receptor-binding domain (RBD) from SARS-CoV-2). The candidate encoding the longest RBD variant (VB2060) demonstrated high secretion of a functional protein and induced rapid and dose-dependent RBD IgG antibody responses that persisted up to at least 3 months after a single dose of the vaccine in mice. Neutralizing antibody (nAb) titers against the live virus were detected from day 7 after one dose. All tested dose regimens reached titers that were higher or comparable to those seen in sera from human convalescent COVID-19 patients from day 28. T cell responses were detected already at day 7, and were subsequently characterized to be multifunctional CD8+ and Th1 dominated CD4+ T cells. Responses remained at sustained high levels until at least 3 months after a single vaccination, being further strongly boosted by a second vaccination at day 89. These findings, together with the simplicity and scalability of plasmid DNA manufacturing, safety data on the vaccine platform in clinical trials, low cost of goods, data indicating potential long term storage at +2{degrees} to 8{degrees}C and simple administration, suggests the VB2060 candidate is a promising second generation candidate to prevent COVID-19.

microbiology↗

bioRxiv2019

Looking for a reference for large datasets: relative reliability of visual and automatic sleep scoring

Study ObjectivesNew challenges in sleep science require to describe fine grain phenomena or to deal with large datasets. Beside the human resource challenge of scoring huge datasets, the inter- and intra-expert variability may also reduce the sensitivity of such studies. Searching for a way to disentangle the variability induced by the scoring method from the actual variability in the data, visual and automatic sleep scorings of healthy individuals were examined.\n\nMethodsA first dataset (DS1, 4 recordings) scored by 6 experts plus an autoscoring algorithm was used to characterize inter-scoring variability. A second dataset (DS2, 88 recordings) scored a few weeks later was used to investigate intra-expert variability. Percentage agreements and Congers kappa were derived from epoch-by-epoch comparisons on pairwise, consensus and majority scorings.\n\nResultsOn DS1 the number of epochs of agreement decreased when the number of expert increased, in both majority and consensus scoring, where agreement ranged from 86% (pairwise) to 69% (all experts). Adding autoscoring to visual scorings changed the kappa value from 0.81 to 0.79. Agreement between expert consensus and autoscoring was 93%. On DS2 intra-expert variability was evidenced by the kappa systematic decrease between autoscoring and each single expert between datasets (0.75 to 0.70).\n\nConclusionsVisual scoring induces inter- and intra-expert variability, which is difficult to address especially in big data studies. When proven to be reliable and if perfectly reproducible, autoscoring methods can cope with intra-scorer variability making them a sensible option when dealing with large datasets.\n\nStatement of SignificanceWe confirmed and extended previous findings highlighting the intra- and inter-expert variability in visual sleep scoring. On large datasets those variability issues cannot be completely addressed by neither practical nor statistical solutions such as group training, majority or consensus scoring.\n\nWhen an automated scoring method can be proven to be as reasonably imperfect as visual scoring but perfectly reproducible, it can serve as a reliable scoring reference for sleep studies.

neuroscience↗