Publications
Department of Medicine faculty members published more than 3,000 peer-reviewed articles in 2022.
2011
2011
BACKGROUND
Pediatric Clostridium difficile infection (CDI)-related hospitalizations are increasing. We sought to describe the epidemiology of pediatric CDI at a quaternary care hospital.
METHODS
Nested case-control study within a cohort of children <18 years tested for C. difficile between January and August 2008. The study included patients who were ≥ 1 year with a positive test and diarrhea; those without diarrhea (ie, presumed colonization) were excluded. Two unmatched controls per case were randomly selected from patients ≥ 1 year with a negative test. Potential predictors of CDI included age, gender, comorbidities, prior hospitalization, receipt of C. difficile-active antibiotics in the prior 24 hours, and recent (≤ 4 weeks) exposure to antibiotics or acid-blocking medications. Multivariate logistic regression models were created to identify independent predictors of CDI.
RESULTS
Of 1891 tests performed, 263 (14%) were positive in 181 children. Ninety-five patients ≥ 1 year with CDI were compared with 238 controls. In multivariate analyses, predictors of CDI included solid organ transplant (odds ratio [OR], 8.09; 95% confidence interval [CI], 2.10-31.12), lack of prior hospitalization (OR, 8.43; 95% CI, 4.39-16.20), presence of gastrostomy or jejunostomy (G or J) tube (OR, 3.32; 95% CI 1.71-6.42), and receipt of fluoroquinolones (OR, 17.04; 95% CI, 5.86-49.54) or nonquinolone antibiotics (OR, 2.23; 95% CI, 1.18-4.20) in the past 4 weeks. Receipt of C. difficile-active antibiotics within 24 hours before testing was associated with a lower odds of CDI (OR, 0.22; 95% CI, 0.09-0.58).
CONCLUSIONS
Recent antibiotic exposure and certain comorbid conditions (solid organ transplant, presence of a gastrostomy or jejunostomy tube) were associated with CDI. Diagnostic testing has less utility in patients being treated with C. difficile-active antibiotics.
View on PubMed2011
MOTIVATION
The analysis of gene coexpression is at the core of many types of genetic analysis. The coexpression between two genes can be calculated by using a traditional Pearson's correlation coefficient. However, unobserved confounding effects may cause inflation of the Pearson's correlation so that uncorrelated genes appear correlated. Many general methods have been suggested, which aim to remove the effects of confounding from gene expression data. However, the residual confounding which is not accounted for by these generic correction procedures has the potential to induce correlation between genes. Therefore, a method that specifically aims to calculate gene coexpression between gene expression arrays, while accounting for confounding effects, is desirable.
RESULTS
In this article, we present a statistical model for calculating gene coexpression called mixed model coexpression (MMC), which models coexpression within a mixed model framework. Confounding effects are expected to be encoded in the matrix representing the correlation between arrays, the inter-sample correlation matrix. By conditioning on the information in the inter-sample correlation matrix, MMC is able to produce gene coexpressions that are not influenced by global confounding effects and thus significantly reduce the number of spurious coexpressions observed. We applied MMC to both human and yeast datasets and show it is better able to effectively prioritize strong coexpressions when compared to a traditional Pearson's correlation and a Pearson's correlation applied to data corrected with surrogate variable analysis (SVA).
AVAILABILITY
The method is implemented in the R programming language and may be found at http://genetics.cs.ucla.edu/mmc.
CONTACT
[email protected]; [email protected].
View on PubMed2011
2011