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Updated: Feb 3, 2026

An Efficient Sieving Method to Isolate Intact Glomeruli from Adult Rat Kidney
Published on: November 1, 2018
A high throughput lentivirus sieving assay identifies neutralization resistant Envelope sequences and predicts in
Nami Iwamoto1, Rosemarie Mason1, Jianfei Hu2
1ImmunoTechnology Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, United States.
Abstract:
An effective prophylactic vaccine against human immunodeficiency virus (HIV) will likely require a potent antibody response that can neutralize the virus at the mucosal portal of entry. The elicitation of potent broadly-neutralizing anti-sera will be an iterative process, optimizing candidates that only block a fraction of potential viral strains. This effect, termed "sieving", is evidence of a partially efficacious vaccine. Understanding the mechanisms of resistance of the breakthrough viruses is important for improving vaccines. We developed a high-throughput assay that can be used on vaccine-elicited antisera or monoclonal antibodies. Using the SIVsmE660 swarm stock and sera from a large NHP vaccine/challenge study, our in vitro sieving assay identified the same viral subspecies as in the animal study-those with a canonical C1 amino acid variants conferring global neutralization resistance to antibodies. Using a genetically divergent swarm stock, we identified five other amino acid variants that confer global resistance; the C1 mutations in this stock were not selected, also in agreement with in vivo challenge studies. Thus, the in vitro sieving assay can be used with genetically diverse challenge stocks to predict the coverage of a vaccine-elicited sera and possibly inform candidate vaccine development efforts.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

