91 research outputs found
Data_Sheet_3_Keystone Species in Pregnancy Gingivitis: A Snapshot of Oral Microbiome During Pregnancy and Postpartum Period.xls
No full textIt is well known that pregnancy is under the constant influence of hormonal, metabolic and immunological factors and this may impact the oral microbiota toward pregnancy gingivitis. However, it is still not clear how the oral microbial dysbiosis can modulate oral diseases as oral microbiome during pregnancy is very poorly characterized. In addition, the recent revelation that placental microbiome is akin to oral microbiome further potentiates the importance of oral dysbiosis in adverse pregnancy outcomes. Hence, leveraging on the 16S rRNA gene sequencing technology, we present a snapshot of the variations in the oral microbial composition with the progression of pregnancy and in the postpartum period and its association with pregnancy gingivitis. Despite the stability of oral microbial diversity during pregnancy and postpartum period, we observed that the microbiome makes a pathogenic shift during pregnancy and reverts back to a healthy microbiome during the postpartum period. Co-occurrence network analysis provided a mechanistic explanation of the pathogenicity of the microbiome during pregnancy and predicted taxa at hubs of interaction. Targeting the taxa which form the ecological guilds in the underlying microbiome would help to modulate the microbial pathogenicity during pregnancy, thereby alleviating risk for oral diseases and adverse pregnancy outcomes. Our study has also uncovered the possibility of novel species in subgingival plaque and saliva as the key players in the causation of pregnancy gingivitis. The keystone species hold the potential to open up avenues for designing microbiome modulation strategies to improve host health during pregnancy.</p
Number and percentage of tuberculosis patients reported in Ethiopia stratified by type of tuberculosis.
No full textNumber and percentage of tuberculosis patients reported in Ethiopia stratified by type of tuberculosis.</p
Bayesian Poisson regression model with spatially structured and spatially unstructured random effects for notified incidence of tuberculosis in Ethiopia, 2016 to 2017.
No full textBayesian Poisson regression model with spatially structured and spatially unstructured random effects for notified incidence of tuberculosis in Ethiopia, 2016 to 2017.</p
Posterior mean of spatially structured random effects for notified incidence of TB in Ethiopia.
No full textPosterior mean of spatially structured random effects for notified incidence of TB in Ethiopia.</p
Spatial clustering of bacteriologically confirmed and all forms of tuberculosis in Ethiopia based on the Local Moran’s I statistic, between 2015 to 2017.
No full textSpatial clustering of bacteriologically confirmed and all forms of tuberculosis in Ethiopia based on the Local Moran’s I statistic, between 2015 to 2017.</p
Geographical distribution of the standardized morbidity ratios (SMR) of notified tuberculosis at district level in Ethiopia, 2016 to 2017.
No full textGeographical distribution of the standardized morbidity ratios (SMR) of notified tuberculosis at district level in Ethiopia, 2016 to 2017.</p
Tuberculosis notification rate per 100,000 population by regions in Ethiopia, 2016–2017.
No full textTuberculosis notification rate per 100,000 population by regions in Ethiopia, 2016–2017.</p
Bivariate meta-regression of <i>S</i>. <i>stercoralis</i> infections analysed by study covariates.
No full textBivariate meta-regression of S. stercoralis infections analysed by study covariates.</p
Pooled prevalence of hookworm infections within minority indigenous study populations.
No full textThe forest plot shows the pooled prevalence of hookworm infection with 95% confidence intervals (CI) and the prediction interval. The I^2 statistic is rounded to the nearest integer.</p
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