scholarly journals The gut microbiome and rotavirus vaccine immunogenicity in rural Zimbabwean infants

2021 ◽  
Author(s):  
Ruairi Robertson ◽  
James A Church ◽  
Thad J Edens ◽  
Kuda Mutasa ◽  
Hyun Min Geum ◽  
...  
Keyword(s):  
Random Forest ◽  
Low Income ◽  
Gut Microbiome ◽  
Oral Vaccine ◽  
Bifidobacterium Longum ◽  
Low Income Countries ◽  
Rotavirus Vaccine ◽  
Forest Models ◽  
Random Forest Models ◽  
Stool Samples

Background: Oral rotavirus vaccine (RVV) immunogenicity is considerably lower in low- versus high-income populations; however, the mechanisms underlying this remain unclear. Previous evidence suggests that the gut microbiota may contribute to differences in oral vaccine efficacy. Methods: We performed whole metagenome shotgun sequencing on stool samples and measured anti-rotavirus immunoglobulin A in plasma samples from a subset of infants enrolled in a cluster randomized 2×2 factorial trial of improved water, sanitation and hygiene and infant feeding in rural Zimbabwe (SHINE trial: NCT01824940). We examined taxonomic and functional microbiome composition using random forest models, differential abundance testing and regression analyses to explored associations with RVV immunogenicity. Results: Among 158 infants with stool samples and anti-rotavirus IgA titres, 34 were RVV seroconverters. The median age at stool collection was 43 days. The infant microbiome was dominated by Bifidobacterium longum. The gut microbiome differed significantly between early (≤42 days) and later samples (>42 days) however, we observed no meaningful differences in alpha diversity, beta diversity, species composition or functional metagenomic composition by RVV seroconversion status. Bacteroides thetaiotaomicron was the only species associated with anti-rotavirus IgA titre. Random forest models poorly classified seroconversion status by both composition and functional microbiome variables. Conclusions: RVV immunogenicity is low in this rural Zimbabwean setting, however it is not explained by the composition or function of the early-life gut microbiome. Further research is warranted to examine the mechanisms of poor oral RVV efficacy in low-income countries.

scholarly journals Infrequent Transmission of Monovalent Human Rotavirus Vaccine Virus to Household Contacts of Vaccinated Infants in Malawi

2019 ◽  
Vol 219 (11) ◽  
pp. 1730-1734 ◽  
Author(s):  
Aisleen Bennett ◽  
Louisa Pollock ◽  
Khuzwayo C Jere ◽  
Virginia E Pitzer ◽  
Benjamin Lopman ◽  
...  
Keyword(s):  
Low Income ◽  
Indirect Effects ◽  
Horizontal Transmission ◽  
Vaccine Virus ◽  
Low Income Countries ◽  
Rotavirus Vaccine ◽  
Fecal Shedding ◽  
Household Contacts ◽  
Human Rotavirus ◽  
Stool Samples

Abstract Horizontal transmission of rotavirus vaccine virus may contribute to indirect effects of rotavirus vaccine, but data are lacking from low-income countries. Serial stool samples were obtained from Malawian infants who received 2 doses of monovalent human rotavirus vaccine (RV1) (days 4, 6, 8, and 10 after vaccination) and from their household contacts (8–10 days after vaccine). RV1 vaccine virus in stool was detected using semiquantitative real-time reverse-transcription polymerase chain reaction. RV1 fecal shedding was detected in 41 of 60 vaccinated infants (68%) and in 2 of 147 household contacts (1.4%). Horizontal transmission of vaccine virus within households is unlikely to make a major contribution to RV1 indirect effects in Malawi.

scholarly journals Treatment-Specific Composition of Gut Microbiota Is Associated with Disease Remission in a Pediatric Crohn’s Disease Cohort

2018 ◽  
Author(s):  
Daniel Sprockett ◽  
Natalie Fischer ◽  
Rotem Sigall Boneh ◽  
Dan Turner ◽  
Jarek Kierkus ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Random Forest ◽  
Crohn's Disease ◽  
Gut Microbiota ◽  
Disease Remission ◽  
Beneficial Effects ◽  
Forest Models ◽  
Random Forest Models ◽  
Stool Samples ◽  
The Impact

AbstractBackgroundThe beneficial effects of antibiotics in Crohn’s disease (CD) depend in part on the gut microbiota but are inadequately understood. We investigated the impact of metronidazole (MET) and metronidazole plus azithromycin (MET+AZ) on the microbiota in pediatric CD, and the use of microbiota features as classifiers or predictors of disease remission.Methods16S rRNA-based microbiota profiling was performed on stool samples from 67 patients in a multinational, randomized, controlled, longitudinal, 12-week trial of MET vs. MET+AZ in children with mild to moderate CD. Profiles were analyzed together with disease activity, and then used to construct Random Forest models to classify remission or predict treatment response.ResultsBoth MET and MET+AZ significantly decreased diversity of the microbiota and caused large treatment-specific shifts in microbiota structure at week 4. Disease remission was associated with a treatment-specific microbiota configuration. Random Forest models constructed from microbiota profiles pre- and during antibiotic treatment with metronidazole accurately classified disease remission in this treatment group (AUC of 0.879, 95% CI 0.683, 0.9877; sensitivity 0.7778; specificity 1.000, P < 0.001). A Random Forest model trained on preantibiotic microbiota profiles predicted disease remission at week 4 with modest accuracy (AUC of 0.8, P = 0.24).ConclusionsMET and MET+AZ antibiotic regimens in pediatric CD lead to distinct gut microbiota structures at remission. It may be possible to classify and predict remission based in part on microbiota profiles, but larger cohorts will be needed to realize this goal.SummaryWe investigated the impact of metronidazole and metronidazole plus azithromycin on the gut microbiota in pediatric Crohn’s disease. Disease remission was associated with a treatment-specific microbiota configuration, and could be predicted based on pre-antibiotic microbiota profiles.

scholarly journals Immunogenicity of an oral rotavirus vaccine administered with prenatal nutritional support in Niger: A cluster randomized clinical trial

PLoS Medicine ◽  
2021 ◽  
Vol 18 (8) ◽  
pp. e1003720
Author(s):  
Sheila Isanaka ◽  
Souna Garba ◽  
Brian Plikaytis ◽  
Monica Malone McNeal ◽  
Ousmane Guindo ◽  
...  
Keyword(s):  
Immune Response ◽  
Low Income ◽  
Mixed Model ◽  
Oral Vaccine ◽  
Low Income Countries ◽  
Enzyme Linked Immunosorbent Assay ◽  
Rotavirus Vaccine ◽  
Rotavirus Vaccines ◽  
Nutrient Supplement ◽  
Cluster Randomized

Background Nutritional status may play a role in infant immune development. To identify potential boosters of immunogenicity in low-income countries where oral vaccine efficacy is low, we tested the effect of prenatal nutritional supplementation on immune response to 3 doses of a live oral rotavirus vaccine. Methods and findings We nested a cluster randomized trial within a double-blind, placebo-controlled randomized efficacy trial to assess the effect of 3 prenatal nutritional supplements (lipid-based nutrient supplement [LNS], multiple micronutrient supplement [MMS], or iron–folic acid [IFA]) on infant immune response (n = 53 villages and 1,525 infants with valid serology results: 794 in the vaccine group and 731 in the placebo group). From September 2015 to February 2017, participating women received prenatal nutrient supplement during pregnancy. Eligible infants were then randomized to receive 3 doses of an oral rotavirus vaccine or placebo at 6–8 weeks of age (mean age: 6.3 weeks, 50% female). Infant sera (pre-Dose 1 and 28 days post-Dose 3) were analyzed for anti-rotavirus immunoglobulin A (IgA) using enzyme-linked immunosorbent assay (ELISA). The primary immunogenicity end point, seroconversion defined as ≥3-fold increase in IgA, was compared in vaccinated infants among the 3 supplement groups and between vaccine/placebo groups using mixed model analysis of variance procedures. Seroconversion did not differ by supplementation group (41.1% (94/229) with LNS vs. 39.1% (102/261) with multiple micronutrients (MMN) vs. 38.8% (118/304) with IFA, p = 0.91). Overall, 39.6% (n = 314/794) of infants who received vaccine seroconverted, compared to 29.0% (n = 212/731) of infants who received placebo (relative risk [RR]: 1.36; 95% confidence interval [CI]: 1.18, 1.57, p < 0.001). This study was conducted in a high rotavirus transmission setting. Study limitations include the absence of an immune correlate of protection for rotavirus vaccines, with the implications of using serum anti-rotavirus IgA for the assessment of immunogenicity and efficacy in low-income countries unclear. Conclusions This study showed no effect of the type of prenatal nutrient supplementation on immune response in this setting. Immune response varied depending on previous exposure to rotavirus, suggesting that alternative delivery modalities and schedules may be considered to improve vaccine performance in high transmission settings. Trial registration ClinicalTrials.gov NCT02145000.

scholarly journals Data-Driven Wildfire Risk Prediction in Northern California

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 109
Author(s):  
Ashima Malik ◽  
Megha Rajam Rao ◽  
Nandini Puppala ◽  
Prathusha Koouri ◽  
Venkata Anil Kumar Thota ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Curves ◽  
Data Driven ◽  
Northern California ◽  
Combined Model ◽  
Wildfire Risk ◽  
Study Results ◽  
Forest Models ◽  
Random Forest Models

Over the years, rampant wildfires have plagued the state of California, creating economic and environmental loss. In 2018, wildfires cost nearly 800 million dollars in economic loss and claimed more than 100 lives in California. Over 1.6 million acres of land has burned and caused large sums of environmental damage. Although, recently, researchers have introduced machine learning models and algorithms in predicting the wildfire risks, these results focused on special perspectives and were restricted to a limited number of data parameters. In this paper, we have proposed two data-driven machine learning approaches based on random forest models to predict the wildfire risk at areas near Monticello and Winters, California. This study demonstrated how the models were developed and applied with comprehensive data parameters such as powerlines, terrain, and vegetation in different perspectives that improved the spatial and temporal accuracy in predicting the risk of wildfire including fire ignition. The combined model uses the spatial and the temporal parameters as a single combined dataset to train and predict the fire risk, whereas the ensemble model was fed separate parameters that were later stacked to work as a single model. Our experiment shows that the combined model produced better results compared to the ensemble of random forest models on separate spatial data in terms of accuracy. The models were validated with Receiver Operating Characteristic (ROC) curves, learning curves, and evaluation metrics such as: accuracy, confusion matrices, and classification report. The study results showed and achieved cutting-edge accuracy of 92% in predicting the wildfire risks, including ignition by utilizing the regional spatial and temporal data along with standard data parameters in Northern California.

scholarly journals The Rotavirus Vaccine Landscape, an Update

Pathogens ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 520
Author(s):  
Roberto Cárcamo-Calvo ◽  
Carlos Muñoz ◽  
Javier Buesa ◽  
Jesús Rodríguez-Díaz ◽  
Roberto Gozalbo-Rovira
Keyword(s):  
Developing Countries ◽  
Mortality Rate ◽  
Low Income ◽  
State Of The Art ◽  
The State ◽  
Cold Chain ◽  
Low Income Countries ◽  
Rotavirus Vaccine ◽  
Oral Vaccines

Rotavirus is the leading cause of severe acute childhood gastroenteritis, responsible for more than 128,500 deaths per year, mainly in low-income countries. Although the mortality rate has dropped significantly since the introduction of the first vaccines around 2006, an estimated 83,158 deaths are still preventable. The two main vaccines currently deployed, Rotarix and RotaTeq, both live oral vaccines, have been shown to be less effective in developing countries. In addition, they have been associated with a slight risk of intussusception, and the need for cold chain maintenance limits the accessibility of these vaccines to certain areas, leaving 65% of children worldwide unvaccinated and therefore unprotected. Against this backdrop, here we review the main vaccines under development and the state of the art on potential alternatives.

Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces

2012 ◽  
Vol 8 (2) ◽  
pp. 44-63 ◽  
Author(s):  
Baoxun Xu ◽  
Joshua Zhexue Huang ◽  
Graham Williams ◽  
Qiang Wang ◽  
Yunming Ye
Keyword(s):  
Random Forest ◽  
High Dimensional Data ◽  
Real Life ◽  
Classification Performance ◽  
Feature Weighting ◽  
Random Forest Model ◽  
High Dimensional ◽  
Forest Model ◽  
Forest Models ◽  
Random Forest Models

The selection of feature subspaces for growing decision trees is a key step in building random forest models. However, the common approach using randomly sampling a few features in the subspace is not suitable for high dimensional data consisting of thousands of features, because such data often contains many features which are uninformative to classification, and the random sampling often doesn’t include informative features in the selected subspaces. Consequently, classification performance of the random forest model is significantly affected. In this paper, the authors propose an improved random forest method which uses a novel feature weighting method for subspace selection and therefore enhances classification performance over high-dimensional data. A series of experiments on 9 real life high dimensional datasets demonstrated that using a subspace size of features where M is the total number of features in the dataset, our random forest model significantly outperforms existing random forest models.

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