Biology subjects

Sahay, R.

Publications and source records attributed to Sahay, R..

3 recordsLinked to original sources

bioRxiv2021

Assessment of immunogenicity and protective efficacy of ZyCoV-D DNA vaccine candidates in Rhesus macaques against SARS-CoV-2 infection

Vaccines remain the key protective measure to achieve herd immunity to control the disease burden and stop COVID-19 pandemic. We have developed and assessed the immunogenicity and protective efficacy of two formulations (1mg and 2mg) of ZyCoV-D (a plasmid DNA based vaccine candidates) administered through Needle Free Injection System (NFIS) and syringe-needle (intradermal) in rhesus macaques with three dose vaccine regimens. The vaccine candidate 2mg dose administered using Needle Free Injection System (NFIS) elicited a significant immune response with development of SARS-CoV-2 S1 spike region specific IgG and neutralizing antibody (NAb) titers during the immunization phase and significant enhancement in the levels after the virus challenge. In 2 mg NFIS group the IgG and NAb titers were maintained and showed gradual rise during the immunization period (15 weeks) and till 2 weeks after the virus challenge. It also conferred better protection to macaques evident by the viral clearance from nasal swab, throat swab and bronchoalveolar lavage fluid specimens in comparison with macaques from other immunized groups. In contrast, the animals from placebo group developed high levels of viremia and lung disease following the virus challenge. Besides this, the vaccine candidate also induced increase lymphocyte proliferation and cytokines response (IL-6, IL-5).The administration of the vaccine candidate with NFIS generated a better immunogenicity response in comparison to syringe-needle (intradermal route). The study demonstrated immunogenicity and protective efficacy of the vaccine candidate, ZyCoV-D in rhesus macaques.

microbiology↗

bioRxiv2021

Neutralization of UK-variant VUI-202012/01 with COVAXIN vaccinated human serum

We performed the plaque reduction neutralization test (PRNT50) using sera collected from the 26 recipients of BBV152/COVAXIN against hCoV-19/India/20203522 (UK-variant) and hCoV27 19/India/2020Q111 (heterologous strain). A comparable neutralization activity of the vaccinated individuals sera showed against UK-variant and the heterologous strain with similar efficiency, dispel the uncertainty of possible neutralization escape.

molecular biology↗

bioRxiv2020

Metastatic Site Prediction in Breast Cancer usingOmics Knowledge Graph and Pattern Mining withKirchhoff's Law Traversal

Predicting the anatomical site of metastasis from a primary tumour remains an unsolved problem in breast cancer (BRCA) and metastatic disease more broadly. The difficulty is structural: metastatic biology is multi-site (bone, lung, liver, brain), multi-omics (genomics, proteomics, methylomics, drug response), and multi-modal (CNV, gene expression, DNA methylation, pathways, clinical associations). Existing classifiers either collapse this heterogeneity into a single feature vector or rely on a single omics layer, both of which discard the mechanistic structure that drives metastatic tropism. We introduce Kirchhoff Knowledge Graphs (K-KG), a framework that imports the conservation laws of electrical-circuit theory into knowledge graph reasoning. Our contributions are: (1) a layered RDF Cancer Decision Network integrating 36 polyomics datasets across mutations, pathways, drugs, diseases, and reactions; (2) two novel conservation laws--the Knowledge-Graph Voltage Law (KGVL) and Knowledge-Graph Current Law (KGCL)--that govern information flow during traversal and yield a principled measure of graph completeness; (3) topological motif mining on the conserved graph, replacing expression-based feature selection by identifying triangular sub-structures whose rewiring marks metastatic transition; (4) a Graph Convolutional Neural Network whose hidden layers are the omics layers themselves, predicting site-specific metastasis as a continuous percentage rather than a binary label. On TCGA-BRCA training plus one validation and four independent test cohorts from GEO, K-KG achieves 83.8% AUC for relapse prediction and up to 0.87 AUC / 0.91 F1 for Brain-site-specific prediction, outperforming Random Forest, Neural Network, and SVM baselines by 8-20 AUC points. To our knowledge this is the first application of Kirchhoffs laws (1845, 1847) to graph-based machine learning, and the first metastasis predictor that returns a per-site contribution profile rather than a single label.

bioinformatics↗