Effect of delayed misoprostol dosing interval for induction of labor: a retrospective study.

BACKGROUND: Induction of labor occurs in greater than 22% of all pregnancies in the United States. Previous studies have shown that misoprostol is more effective for induction than oxytocin or dinoprostone alone. The World Health Organization recommends vaginal misoprostol 25mcg every 6 hours and the American Congress of Obstetricians and Gynecologists recommends 25mcg vaginal misoprostol every three to 6 hours. Although route of administration and dosage of misoprostol has been extensively studied, little is known about the optimal dosing interval of vaginal misoprostol. METHODS: The primary objective of this study is to determine the effect of delayed vaginal misoprostol dosing, defined as any interval longer than 4.5 h, on time to vaginal delivery. Our hypothesis is that the routine dosing interval of 4 hours shortens times to vaginal delivery compared to delayed dosing, even when adjusted for the time of delay. Secondary objectives include the effect of delayed vaginal misoprostol dosing on cesarean section rate, operative vaginal delivery rate, maternal outcomes, and neonatal outcomes. We conducted a retrospective chart review of 323 inductions of labor at one academic institution. The primary outcome was the proportion of patients who achieved a vaginal delivery within 24 h. The group who received all doses of misoprostol within a 4.5 h dosing window (Routine Dosing Interval Group) was compared with the group who had any dosing deviation (Delayed Dosing Interval Group). RESULTS: Of 133 included patients, 64 subjects received routine interval dosing and 69 subjects received delayed interval dosing. The vaginal delivery rates within 24 h were 56% (36/64) and 20% (14/69), respectively (P < 10- 4). Spontaneous vaginal delivery rates were 86% (55/64) vs. 75% (52/69), respectively (P = .13). Kaplan Meier curves demonstrated statistically significant difference in time to vaginal delivery between groups, with a Cox Proportional Hazard ratio for routine dosing interval of 1.73 (P < 10- 5) unadjusted and 1.34 (P = .01) when adjusted for dosing delay. CONCLUSIONS: This retrospective study demonstrates a significant increase in delay-adjusted time to vaginal delivery when doses of vaginal misoprostol are delayed past 4.5 h.

Learning and Imputation for Mass-spec Bias Reduction (LIMBR).

MOTIVATION: Decreasing costs are making it feasible to perform time series proteomics and genomics experiments with more replicates and higher resolution than ever before. With more replicates and time points, proteome and genome-wide patterns of expression are more readily discernible. These larger experiments require more batches exacerbating batch effects and increasing the number of bias trends. In the case of proteomics, where methods frequently result in missing data this increasing scale is also decreasing the number of peptides observed in all samples. The sources of batch effects and missing data are incompletely understood necessitating novel techniques. RESULTS: Here we show that by exploiting the structure of time series experiments, it is possible to accurately and reproducibly model and remove batch effects. We implement Learning and Imputation for Mass-spec Bias Reduction (LIMBR) software, which builds on previous block-based models of batch effects and includes features specific to time series and circadian studies. To aid in the analysis of time series proteomics experiments, which are often plagued with missing data points, we also integrate an imputation system. By building LIMBR for imputation and time series tailored bias modeling into one straightforward software package, we expect that the quality and ease of large-scale proteomics and genomics time series experiments will be significantly increased. AVAILABILITY AND IMPLEMENTATION: Python code and documentation is available for download at https://github.com/aleccrowell/LIMBR and LIMBR can be downloaded and installed with dependencies using 'pip install limbr'. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Circadian Proteomic Analysis Uncovers Mechanisms of Post-Transcriptional Regulation in Metabolic Pathways.

Transcriptional and translational feedback loops in fungi and animals drive circadian rhythms in transcript levels that provide output from the clock, but post-transcriptional mechanisms also contribute. To determine the extent and underlying source of this regulation, we applied newly developed analytical tools to a long-duration, deeply sampled, circadian proteomics time course comprising half of the proteome. We found a quarter of expressed proteins are clock regulated, but >40% of these do not arise from clock-regulated transcripts, and our analysis predicts that these protein rhythms arise from oscillations in translational rates. Our data highlighted the impact of the clock on metabolic regulation, with central carbon metabolism reflecting both transcriptional and post-transcriptional control and opposing metabolic pathways showing peak activities at different times of day. The transcription factor CSP-1 plays a role in this metabolic regulation, contributing to the rhythmicity and phase of clock-regulated proteins.

Guidelines for Genome-Scale Analysis of Biological Rhythms.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding "big data" that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them.

A Tool Set for the Genome-Wide Analysis of Neurospora crassa by RT-PCR.

Neurospora crassa is an important model organism for filamentous fungi as well as for circadian biology and photobiology. Although the community-accumulated tool set for the molecular analysis of Neurospora is extensive, two components are missing: (1) dependable reference genes whose level of expression are relatively constant across light/dark cycles and as a function of time of day and (2) a catalog of primers specifically designed for real-time PCR (RT-PCR). To address the first of these we have identified genes that are optimal for use as reference genes in RT-PCR across a wide range of expression levels; the mRNA/transcripts from these genes have potential for use as reference noncycling transcripts outside of Neurospora. In addition, we have generated a genome-wide set of RT-PCR primers, thereby streamlining the analysis of gene expression. In validation studies these primers successfully identified target mRNAs arising from 70% (34 of 49) of all tested genes and from all (28) of the moderately to highly expressed tested genes.