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Noor N Tahirkheli,1 Amanda S Cherry,1 Alayna P Tackett,2 Mary Anne McCaffree,3 Stephen R Gillaspy11Section of General and Community Pediatrics, Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; 2Department of Psychology, Oklahoma State University, Stillwater, OK, USA; 3Section of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USAAbstract: As the most common complication of childbirth affecting 10%–15% of women, postpartum depression (PPD) goes vastly undetected and untreated, inflicting long-term consequences on both mother and child. Studies consistently show that mothers of infants in the neonatal intensive care unit (NICU) experience PPD at higher rates with more elevated symptomatology than mothers of healthy infants. Although there has been increased awareness regarding the overall prevalence of PPD and recognition of the need for health care providers to address this health issue, there has not been adequate attention to PPD in the context of the NICU. This review will focus on an overview of PPD and psychological morbidities, the prevalence of PPD in mothers of infants admitted to NICU, associated risk factors, potential PPD screening measures, promising intervention programs, the role of NICU health care providers in addressing PPD in the NICU, and suggested future research directions.Keywords: neonatal intensive care unit, postpartum depression, mothers

Abstract Background Alterations in gene expression in peripheral blood cells have been shown to be sensitive to the presence and extent of coronary artery disease (CAD). A non-invasive blood test that could reliably assess obstructive CAD likelihood would have diagnostic utility. Results Microarray analysis of RNA samples from a 195 patient Duke CATHGEN registry case:control cohort yielded 2,438 genes with significant CAD association (p < 0.05), and identified the clinical/demographic factors with the largest effects on gene expression as age, sex, and diabetic status. RT-PCR analysis of 88 CAD classifier genes confirmed that diabetic status was the largest clinical factor affecting CAD associated gene expression changes. A second microarray cohort analysis limited to non-diabetics from the multi-center PREDICT study (198 patients; 99 case: control pairs matched for age and sex) evaluated gene expression, clinical, and cell population predictors of CAD and yielded 5,935 CAD genes (p < 0.05) with an intersection of 655 genes with the CATHGEN results. Biological pathway (gene ontology and literature) and statistical analyses (hierarchical clustering and logistic regression) were used in combination to select 113 genes for RT-PCR analysis including CAD classifiers, cell-type specific markers, and normalization genes. RT-PCR analysis of these 113 genes in a PREDICT cohort of 640 non-diabetic subject samples was used for algorithm development. Gene expression correlations identified clusters of CAD classifier genes which were reduced to meta-genes using LASSO. The final classifier for assessment of obstructive CAD was derived by Ridge Regression and contained sex-specific age functions and 6 meta-gene terms, comprising 23 genes. This algorithm showed a cross-validated estimated AUC = 0.77 (95% CI 0.73-0.81) in ROC analysis. Conclusions We have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography. Clinical trial registration information PREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov, NCT00500617