Evidence of secondary dengue infection and potential for association with clinical depression in a subset of a randomly selected cohort in South Texas

Globally, dengue is an emerging disease resulting in an estimated 50 million new cases and 22, 000 deaths each year. Anecdotally, depression has been reported as a possible sequelae of dengue virus infection. To test the association, we performed a cross-sectional analysis in a selected sub-set of participants from the Cameron County Hispanic Cohort (CCHC) in South Texas. All study subjects in the analysis had Center for Epidemiological Studies Depression scale (CES-D) scores and were tested for dengue antibodies using stored plasma. We found that 5.0% of participants tested either positive or equivocal for anti-dengue IgG antibodies using the capture antibody test, which detects acute secondary infections. Logistic regression identified that evidence of acute secondary dengue infection was not associated with depression (Odds Ratio [OR] = 0.97, 95%Confidence Interval [CI] 0.47-1.98); however, both being female (OR = 1.53, 95%CI 1.09-2.15) and obese body mass index (BMI > 30) (OR = 1.84, 95%CI 1.19-2.84) were associated with depression. ^

BEST SUBSET SELECTION FOR CATEGORICAL DATA

When choosing among models to describe categorical data, the necessity to consider interactions makes selection more difficult. With just four variables, considering all interactions, there are 166 different hierarchical models and many more non-hierarchical models. Two procedures have been developed for categorical data which will produce the "best" subset or subsets of each model size where size refers to the number of effects in the model. Both procedures are patterned after the Leaps and Bounds approach used by Furnival and Wilson for continuous data and do not generally require fitting all models. For hierarchical models, likelihood ratio statistics (G('2)) are computed using iterative proportional fitting and "best" is determined by comparing, among models with the same number of effects, the Pr((chi)(,k)('2) (GREATERTHEQ) G(,ij)('2)) where k is the degrees of freedom for ith model of size j. To fit non-hierarchical as well as hierarchical models, a weighted least squares procedure has been developed.^ The procedures are applied to published occupational data relating to the occurrence of byssinosis. These results are compared to previously published analyses of the same data. Also, the procedures are applied to published data on symptoms in psychiatric patients and again compared to previously published analyses.^ These procedures will make categorical data analysis more accessible to researchers who are not statisticians. The procedures should also encourage more complex exploratory analyses of epidemiologic data and contribute to the development of new hypotheses for study. ^

Identification and characterization of cellular genes differentially expressed in a subset of tumors with non -functional WT1

Wilms tumor (WT) or nephroblastoma is a genetically heterogeneous pediatric renal tumor that accounts for 6–7% of all childhood cancers in the U.S. WT1, located at 11p13, is the sole WT gene cloned to date. Additional genomic regions containing genes that play a role in the development of Wilms tumor include 11p15, 7p, 16q, 1p, 17q and 19q. This heterogeneity has made it extremely difficult to develop an understanding of the pathways involved in the development of WT, even in the 5–20% of tumors that show mutations at the WT1 locus. My research addresses this gap in our current comprehension of the development of WT. ^ I have used two complementary approaches to extend the current understanding of molecular changes involved in the development of WT. In order to minimize complexities due to genetic heterogeneity, I confined my analysis to the WT1 pathway by assessing those genetically defined tumors that carry WT1 mutations. WT1 encodes a zinc finger transcription factor, and in vitro studies have identified many genes that are potentially regulated in vivo by WT1. However, there is very little in vivo data that suggests that they are transcriptionally regulated endogenously by WT1. In one approach I assessed the role of WT1 in the in vivo regulation of PDGFA and IGF2, two genes that are strong contenders for endogenous regulation by WT1. Using primary tissue samples, I found no correlation between the level of RNA expression of WT1 with either PDGFA or IGF2, suggesting that WT1 does not play a critical role in their expression in either normal kidney or WT. ^ In a parallel strategy, using differential display analysis I compared global gene expression in a subset of tumors with known homozygous inactivating WT1 mutations (WT1-tumors) to the gene expression in a panel of appropriate control tissues (fetal kidney, normal kidney, rhabdoid tumor and pediatric renal cell carcinoma). Transcripts that are aberrantly expressed in this subset of Wilms tumors are candidates for endogenous transcriptional regulation by WT1 as well as for potentially functioning in the development of WT. By this approach I identified several differentially expressed transcripts. I further characterized two of these transcripts, identifying a candidate WT gene in the process. I then performed a detailed analysis of this WT candidate gene, which maps to 7p. Future studies will shed more light on the role of these differentially expressed genes in WT. ^