Immune predictors of oral poliovirus vaccine immunogenicity among infants in South India

Abstract

Identification of the causes of poor oral vaccine immunogenicity in low-income countries might lead to more effective vaccines. We measured mucosal and systemic immune parameters at the time of vaccination with oral poliovirus vaccine (OPV) in 292 Indian infants aged 6-11 months, including plasma cytokines, leukocyte counts, fecal biomarkers of environmental enteropathy and peripheral blood T-cell phenotype, focused on gut-homing regulatory CD4+ populations. We did not find a distinct immune phenotype associated with OPV immunogenicity, although viral pathogens were more prevalent in stool at the time of immunization among infants who failed to seroconvert (63.9% vs. 45.6%, p = 0.002). Using a machine-learning approach, we could predict seroconversion a priori using immune parameters and infection status with a median 58% accuracy (cross-validation IQR: 50-69%) compared with 50% expected by chance. Better identification of immune predictors of OPV immunogenicity is likely to require sampling of mucosal tissue and improved oral poliovirus infection models.npj Vaccines (2020) 5:27 ; https://doi.

Review of Poliovirus Transmission and Economic Modeling to Support Global Polio Eradication: 2020–2024

“…This section begins with a brief overview of each of the 102 studies identified by the search process summarized in Figure 1 [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ,…”

“…Statistical analyses published by IC included an analysis of immune predictors of OPV immunogenicity among infants in South India [ 49 ]. One IC study used poliovirus genetic sequences from GenBank and demonstrated the application of phylogenetic and phylogeospatial modeling to infer geospatial transmission patterns of poliovirus transmission during the 2010 Tajikistan outbreak [ 50 ].…”

Predicting Treatment Nonresponse in Hispanic/Latino Children Receiving Silver Diamine Fluoride for Caries Arrest: A Pilot Study Using Machine Learning

“…In this study, our approach considered two methods to yield a predictive model for treatment non-response using a sample of patients who had previously received silver diamine fluoride for dental caries. While research using machine learning in oral epidemiology is limited, previous studies have successfully applied ML-approaches to predict malodor using salivary microbiota [ 23 ], hypertension from gingival inflammation [ 24 ], oral cancer [ 25 ], oral poliovirus vaccine immunogenicity [ 26 ], temporomandibular joint osteoarthritis, and radiographic detection of tooth and dental restorations [ 27 ]. Notably, a recent review concluded that artificial intelligence, particularly machine learning and deep learning, can use data from the oral microbiome to predict systemic disease [ 28 ].…”