Background: Utilization of the neonatal sepsis calculator published by Kaiser Permanente is rapidly increasing. This freely available online tool can be used in assessment of early-onset sepsis (EOS) in newborns 34 weeks' gestation or more based on maternal risk factors and neonatal examination. However, many hospitals lack standard guidelines for its use, leading to provider discomfort with practice change. Purpose: The goal of this project was to study the antibiotic use rate for EOS at a level III neonatal intensive care unit and create standardized guidelines and staff education for using the sepsis calculator. Our ultimate goal was to decrease antibiotic use for EOS in newborns 34 weeks' gestation or more. Methods: A standard quality improvement Plan-Do-Study-Act (PDSA) model was utilized with a plan to study the problem, implement the intervention, and test again for improvement. The primary outcome of interest was a decrease in the use of antibiotics for EOS in neonates 34 weeks' gestation or more. Results: Over a 4-month period, prior to sepsis calculator implementation, antibiotic use for suspected EOS was 11% and blood culture was done on 14.8% of live births. After implementation of the sepsis calculator and completion of the PDSA cycle, sepsis calculator use was greater than 95%, antibiotic use dropped significantly to 5% (P = .00069), and blood culture use dropped to 7.6% (P = .00046). Implications for Practice: Staff education and systematic intervention using a PDSA model can significantly impact patient care, decreasing the administration of antibiotics to infants at risk for sepsis. Implications for Research: Future research is needed to decrease antibiotic use in premature infants less than 34 weeks' gestation with similar risk factors and clinical features. Video Abstract available at https://journals.na.lww.com/advancesinneonatalcare/Pages/videogallery.aspx?videoId=34&autoPlay=true

Background:Genetics and environment both are critical in autism spectrum disorder (ASD), but their interaction (G × E) is less understood. Numerous studies have shown higher incidence of stress exposures during pregnancies with children later diagnosed with ASD. However, many stress-exposed mothers have unaffected children. The serotonin transporter (SERT) gene affects stress reactivity. Two independent samples have shown that the association between maternal stress exposure and ASD is greatest with maternal presence of theSERTshort (S)-allele (deletion in the promoter region). MicroRNAs play a regulatory role in the serotonergic pathway and in prenatal stress and are therefore potential mechanistic targets in this setting.Design/methods:We profiled microRNA expression in blood from mothers of children with ASD, with known stress exposure during pregnancy. Samples were divided into groups based onSERTgenotypes (LL/LS/SS) and prenatal stress level (high/low).Results:Two thousand five hundred mature microRNAs were examined. The ANOVA analysis showed differential expression (DE) of 119 microRNAs; 90 were DE in high- vs. low-stress groups (stress-dependent). Two (miR-1224-5p, miR-331-3p) were recently reported by our group to exhibit stress-dependent expression in rodent brain samples from embryos exposed to prenatal stress. Another, miR-145-5p, is associated with maternal stress. AcrossSERTgenotypes, with high stress exposure, 20 significantly DE microRNAs were detected, five were stress-dependent. These microRNAs may be candidates for stress ×SERTgenotype interactions. This is remarkable as these changes were from mothers several years after stress-exposed pregnancies.Conclusions:Our study provides evidence for epigenetic alterations in relation to a G × E model (prenatal maternal stress ×SERTgene) in ASD.

This retrospective cohort study was designed to determine the yield of genetic tests in hypotonic infants and develop a diagnostic algorithm. Out of 496 patients identified by International Classification of Diseases (ICD) 9/10 coding, 324 patients met the inclusion criteria. Diagnostic yields were 32% for karyotype, 19% for microarray, 30% for targeted genetic tests, 38% for gene panels, and 31% for exome sequencing. In addition, we considered the diagnostic contribution of ancillary tests, including neuroimaging, metabolic tests, and so forth. The combination of microarray and exome sequencing gave the highest diagnostic yield. None of the other tests added significant value in arriving at a diagnosis. Based on these results we propose that the vast majority of infants with congenital hypotonia should start with a microarray and proceed with exome sequencing, with the notable exception of infants with clearly syndromic features in whom karyotyping or targeted testing may be more appropriate.

Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput ‘omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies. We have developed a Negative Binomial Model-Based (NBMB) method to cluster samples using a stochastic version of the expectation-maximization algorithm. A simulation study involving various scenarios was completed to compare the performance of NBMB to Gaussian model-based or Gaussian mixture modeling (GMM). NBMB was also applied for the clustering of two RNA-seq studies; type 2 diabetes study (N = 96) and TCGA study of ovarian cancer (N = 295). Simulation results showed that NBMB outperforms GMM applied with different transformations in majority of scenarios with limited sample size. Additionally, we found that NBMB outperformed GMM for small clusters distance regardless of sample size. Increasing total number of genes with fixed proportion of differentially expressed genes does not change the outperformance of NBMB, but improves the overall performance of GMM. Analysis of type 2 diabetes and ovarian cancer tumor data with NBMB found good agreement with the reported disease subtypes and the gene expression patterns. This method is available in an R package on CRAN named NB.MClust. Use of Negative Binomial model based clustering is advisable when clustering over dispersed RNA-seq count data.