64 research outputs found
Comparison of the oral microbiome in mouthwash and whole saliva samples
<div><p>Population-based epidemiologic studies can provide important insight regarding the role of the microbiome in human health and disease. Buccal cells samples using commercial mouthwash have been obtained in large prospective cohorts for the purpose of studying human genomic DNA. We aimed to better understand if these mouthwash samples are also a valid resource for the study of the oral microbiome. We collected one saliva sample and one Scope mouthwash sample from 10 healthy subjects. Bacterial 16S rRNA genes from both types of samples were amplified, sequenced, and assigned to bacterial taxa. We comprehensively compared these paired samples for bacterial community composition and individual taxonomic abundance. We found that mouthwash samples yielded similar amount of bacterial DNA as saliva samples (<i>p</i> from Student’s t-test for paired samples = 0.92). Additionally, the paired samples had similar within sample diversity (<i>p</i> from = 0.33 for richness, and <i>p</i> = 0.51 for Shannon index), and clustered as pairs for diversity when analyzed by unsupervised hierarchical cluster analysis. No significant difference was found in the paired samples with respect to the taxonomic abundance of major bacterial phyla, <i>Bacteroidetes</i>, <i>Firmicutes</i>, <i>Proteobacteria</i>, <i>Fusobacteria</i>, and <i>Actinobacteria</i> (FDR adjusted q values from Wilcoxin signed-rank test = 0.15, 0.15, 0.87, 1.00 and 0.15, respectively), and all identified genera, including genus <i>Streptococcus</i> (q = 0.21), <i>Prevotella</i> (q = 0.25), <i>Neisseria</i> (q = 0.37), <i>Veillonella</i> (q = 0.73), <i>Fusobacterium</i> (q = 0.19), and <i>Porphyromonas</i> (q = 0.60). These results show that mouthwash samples perform similarly to saliva samples for analysis of the oral microbiome. Mouthwash samples collected originally for analysis of human DNA are also a resource suitable for human microbiome research.</p></div
Correlation of the centered Log-Ratio (clr) transformed count of major bacteria phyla and genera in the paired mouthwash-saliva samples.
<p>Correlation of clr-transformed counts in mouthwash and saliva samples of major bacterial phyla (Panel A) and genera (Panel B). The x-axis represents the transformed counts in mouthwash samples, and the y-axis represents transformed counts in saliva samples. The straight line is the line of equality. All FDR adjusted q values from Wilcoxon signed-rank test for the comparison of the taxonomic abundance in paired samples were >0.05.</p
Alpha-diversity of oral bacterial communities in the paired mouthwash-saliva samples.
<p>Bar plots of number of observed OTUs (a) and Shannon Index (b) in paired mouthwash-saliva samples in 10 subjects. These indices were calculated for 500 iterations of rarefied OTU table with minimum sequencing depth of 38,400 among all study subjects, with the average over the iterations taken for each participant. No differences were found between mouthwash and saliva samples in α-diversity (<i>p</i> from paired t-test = 0.33 for richness, and 0.51 for Shannon index).</p
Beta-diversity of oral bacterial communities in the paired mouthwash-saliva samples.
<p>Hierarchical cluster analysis using JSD distance. AU (approximately unbiased) <i>p</i>-values, the unbiased bootstrap probability, ranged from 0.97 to 1.00 for all paired samples in hierarchical cluster analysis with number of 1,000 bootstrap replications. Cluster with AU ≥ 0.95 are considered to be strongly supported by data. S01-S10 indicate study subject 1 to 10. “M” indicates mouthwash sample and “S” indicates salivary sample.</p
A mathematical model of case-ascertainment bias: Applied to case-control studies nested within a randomized screening trial
<div><p>When some individuals are screen-detected before the beginning of the study, but otherwise would have been diagnosed symptomatically during the study, this results in different case-ascertainment probabilities among screened and unscreened participants, referred to here as lead-time-biased case-ascertainment (LTBCA). In fact, this issue can arise even in risk-factor studies nested within a randomized screening trial; even though the screening intervention is randomly allocated to trial arms, there is no randomization to potential risk-factors and uptake of screening can differ by risk-factor strata. Under the assumptions that neither screening nor the risk factor affects underlying incidence and no other forms of bias operate, we simulate and compare the underlying cumulative incidence and that observed in the study due to LTBCA. The example used will be constructed from the randomized Prostate, Lung, Colorectal, and Ovarian cancer screening trial. The derived mathematical model is applied to simulating two nested studies to evaluate the potential for screening bias in observational lung cancer studies. Because of differential screening under plausible assumptions about preclinical incidence and duration, the simulations presented here show that LTBCA due to chest x-ray screening can significantly increase the estimated risk of lung cancer due to smoking by 1% and 50%. Traditional adjustment methods cannot account for this bias, as the influence screening has on observational study estimates involves events outside of the study observation window (enrollment and follow-up) that change eligibility for potential participants, thus biasing case ascertainment.</p></div
Reasons Identified by Participants for Participating in Clinical Research.
<p>Reasons Identified by Participants for Participating in Clinical Research.</p
Simulated <i>RR</i>s for the studies sampled from the entire PLCO enrollment period (93–01) in the usual-care group (a and c) and only those affected by the procedural modification (95–01) the intervention group (b and d) using a lognormal distribution for the preclinical duration with modes of 1, 3, 5, and 10 years and standard deviations of 1, 3, 5 years and a constant chest x-ray sensitivity of 46%.
<p>To test model sensitivity to overdiagnosis, in the bottom two tables a 20% overdiagnosis rate was applied. Because of imperfect screening sensitivity, the overdiagnosis rate in the simulated sample population is actually less than 20%. Unbiased RR = 1. a) Usual-care group sampled from entire enrollment period: Simulation results using smoked variable: Lognormal distribution for preclinical duration with no overdiagnosis and chest x-ray sensitivity of 86%. b) Intervention group sampled after procedural modification: Simulation results using smoked variable: Lognormal distribution for preclinical duration with no overdiagnosis and chest x-ray sensitivity of 86%. c) Usual-care group sampled from entire enrollment period: Simulation results using smoked variable: Lognormal distribution for preclinical duration with 20% overdiagnosis and chest x-ray sensitivity of 86%. d) Intervention group sampled after procedural modification: Simulation results using smoked variable: Lognormal distribution for preclinical duration with 20% overdiagnosis and chest x-ray sensitivity of 86%.</p
Simulated relative risks for smoking under the double null hypothesis (i.e., smoking and screening are independent of lung cancer) for studies sampling from the entire PLCO enrollment period (1993–2001) in the usual-care group (left; from Table 1a) or from those affected by the procedural modification (1995–2001) in the intervention group (right; from Table 1b).
<p>Both studies select cases and sample noncases between study time T3 and study time T5. The 12 relative risks were simulated using a combination of four preclinical duration distribution parameters for the mode (1,3,5,10) and three standard deviations (sd) (1,3,5). The relative risks are comparing the categories “ever smoked” to “never smoked.” The simulation is based on study sample specific age distributions and screening proportion and rates among those screened. Unbiased RR = 1.</p
Relationship of age (age range 0–85+) to incidence rate (per 100,000) of lung cancer based on average SEER 9 registry data from 1986 to 2005.
<p>A continuous age-specific incidence intensity function was fit to the point estimates from the SEER data using non-linear minimization (dotted line). The square points identify the data points used to create the preclinical incidence function in the population before the beginning of the study and the circular points identify the data points used to create the preclinical incidence function in the population during the study. Since preclinical incidence is unobservable to get a representative preclinical incidence function the continuous incidence function represented above is shifted backward by the assumed mean of the preclinical duration distribution. For example, if the mean of the preclinical duration distribution is 5, the incidence observed for a 55 year old becomes the preclinical incidence for a 50 year old.</p
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