Trade-off between Transcriptome Plasticity and Genome Evolution in Cephalopods.

RNA editing, a post-transcriptional process, allows the diversification of proteomes beyond the genomic blueprint; however it is infrequently used among animals for this purpose. Recent reports suggesting increased levels of RNA editing in squids thus raise the question of the nature and effects of these events. We here show that RNA editing is particularly common in behaviorally sophisticated coleoid cephalopods, with tens of thousands of evolutionarily conserved sites. Editing is enriched in the nervous system, affecting molecules pertinent for excitability and neuronal morphology. The genomic sequence flanking editing sites is highly conserved, suggesting that the process confers a selective advantage. Due to the large number of sites, the surrounding conservation greatly reduces the number of mutations and genomic polymorphisms in protein-coding regions. This trade-off between genome evolution and transcriptome plasticity highlights the importance of RNA recoding as a strategy for diversifying proteins, particularly those associated with neural function. PAPERCLIP.

ElemeNT 2023: an enhanced tool for detection and curation of core promoter elements.

MOTIVATION: Prediction and identification of core promoter elements and transcription factor binding sites is essential for understanding the mechanism of transcription initiation and deciphering the biological activity of a specific locus. Thus, there is a need for an up-to-date tool to detect and curate core promoter elements/motifs in any provided nucleotide sequences. RESULTS: Here, we introduce ElemeNT 2023-a new and enhanced version of the Elements Navigation Tool, which provides novel capabilities for assessing evolutionary conservation and for readily evaluating the quality of high-throughput transcription start site (TSS) datasets, leveraging preferential motif positioning. ElemeNT 2023 is accessible both as a fast web-based tool and via command line (no coding skills are required to run the tool). While this tool is focused on core promoter elements, it can also be used for searching any user-defined motif, including sequence-specific DNA binding sites. Furthermore, ElemeNT's CORE database, which contains predicted core promoter elements around annotated TSSs, is now expanded to cover 10 species, ranging from worms to human. In this applications note, we describe the new workflow and demonstrate a case study using ElemeNT 2023 for core promoter composition analysis of diverse species, revealing motif prevalence and highlighting evolutionary insights. We discuss how this tool facilitates the exploration of uncharted transcriptomic data, appraises TSS quality, and aids in designing synthetic promoters for gene expression optimization. Taken together, ElemeNT 2023 empowers researchers with comprehensive tools for meticulous analysis of sequence elements and gene expression strategies. AVAILABILITY AND IMPLEMENTATION: ElemeNT 2023 is freely available at https://www.juven-gershonlab.org/resources/element-v2023/. The source code and command line version of ElemeNT 2023 are available at https://github.com/OritAdato/ElemeNT. No coding skills are required to run the tool.

Experimental evolution links post-transcriptional regulation to Leishmania fitness gain.

The protozoan parasite Leishmania donovani causes fatal human visceral leishmaniasis in absence of treatment. Genome instability has been recognized as a driver in Leishmania fitness gain in response to environmental change or chemotherapy. How genome instability generates beneficial phenotypes despite potential deleterious gene dosage effects is unknown. Here we address this important open question applying experimental evolution and integrative systems approaches on parasites adapting to in vitro culture. Phenotypic analyses of parasites from early and late stages of culture adaptation revealed an important fitness tradeoff, with selection for accelerated growth in promastigote culture (fitness gain) impairing infectivity (fitness costs). Comparative genomics, transcriptomics and proteomics analyses revealed a complex regulatory network associated with parasite fitness gain, with genome instability causing highly reproducible, gene dosage-independent and -dependent changes. Reduction of flagellar transcripts and increase in coding and non-coding RNAs implicated in ribosomal biogenesis and protein translation were not correlated to dosage changes of the corresponding genes, revealing a gene dosage-independent, post-transcriptional mechanism of regulation. In contrast, abundance of gene products implicated in post-transcriptional regulation itself correlated to corresponding gene dosage changes. Thus, RNA abundance during parasite adaptation is controled by direct and indirect gene dosage changes. We correlated differential expression of small nucleolar RNAs (snoRNAs) with changes in rRNA modification, providing first evidence that Leishmania fitness gain in culture may be controlled by post-transcriptional and epitranscriptomic regulation. Our findings propose a novel model for Leishmania fitness gain in culture, where differential regulation of mRNA stability and the generation of modified ribosomes may potentially filter deleterious from beneficial gene dosage effects and provide proteomic robustness to genetically heterogenous, adapting parasite populations. This model challenges the current, genome-centric approach to Leishmania epidemiology and identifies the Leishmania transcriptome and non-coding small RNome as potential novel sources for the discovery of biomarkers that may be associated with parasite phenotypic adaptation in clinical settings.

Genome instability drives epistatic adaptation in the human pathogen Leishmania.

How genome instability is harnessed for fitness gain despite its potential deleterious effects is largely elusive. An ideal system to address this important open question is provided by the protozoan pathogen Leishmania, which exploits frequent variations in chromosome and gene copy number to regulate expression levels. Using ecological genomics and experimental evolution approaches, we provide evidence that Leishmania adaptation relies on epistatic interactions between functionally associated gene copy number variations in pathways driving fitness gain in a given environment. We further uncover posttranscriptional regulation as a key mechanism that compensates for deleterious gene dosage effects and provides phenotypic robustness to genetically heterogenous parasite populations. Finally, we correlate dynamic variations in small nucleolar RNA (snoRNA) gene dosage with changes in ribosomal RNA 2'-O-methylation and pseudouridylation, suggesting translational control as an additional layer of parasite adaptation. Leishmania genome instability is thus harnessed for fitness gain by genome-dependent variations in gene expression and genome-independent compensatory mechanisms. This allows for polyclonal adaptation and maintenance of genetic heterogeneity despite strong selective pressure. The epistatic adaptation described here needs to be considered in Leishmania epidemiology and biomarker discovery and may be relevant to other fast-evolving eukaryotic cells that exploit genome instability for adaptation, such as fungal pathogens or cancer.

Identification and functional implications of pseudouridine RNA modification on small noncoding RNAs in the mammalian pathogen Trypanosoma brucei.

Trypanosoma brucei, the parasite that causes sleeping sickness, cycles between an insect and a mammalian host. However, the effect of RNA modifications such as pseudouridinylation on its ability to survive in these two different host environments is unclear. Here, two genome-wide approaches were applied for mapping pseudouridinylation sites (Ψs) on small nucleolar RNA (snoRNA), 7SL RNA, vault RNA, and tRNAs from T. brucei. We show using HydraPsiSeq and RiboMeth-seq that the Ψ on C/D snoRNA guiding 2'-O-methylation increased the efficiency of the guided modification on its target, rRNA. We found differential levels of Ψs on these noncoding RNAs in the two life stages (insect host and mammalian host) of the parasite. Furthermore, tRNA isoform abundance and Ψ modifications were characterized in these two life stages demonstrating stage-specific regulation. We conclude that the differential Ψ modifications identified here may contribute to modulating the function of noncoding RNAs involved in rRNA processing, rRNA modification, protein synthesis, and protein translocation during cycling of the parasite between its two hosts.

Neonatal intensive care admission for term neonates and subsequent childhood mortality: a retrospective linkage study.

BACKGROUND: Neonatal intensive care unit (NICU) admission among term neonates is a rare event. The aim of this study was to study the association of the NICU admission of term neonates on the risk of long-term childhood mortality. METHODS: A single-center case-control retrospective study between 2005 and 2019, including all in-hospital ≥ 37 weeks' gestation singleton live-born neonates. The center perinatal database was linked with the birth and death certificate registries of the Israeli Ministry of Internal Affairs. The primary aim of the study was to study the association between NICU admission and childhood mortality throughout a 15-year follow-up period. RESULTS: During the study period, 206,509 births were registered; 192,527 (93.22%) term neonates were included in the study; 5292 (2.75%) were admitted to NICU. Throughout the follow-up period, the mortality risk for term neonates admitted to the NICU remained elevated; hazard ratio (HR), 19.72 [14.66, 26.53], (p < 0.001). For all term neonates, the mortality rate was 0.16% (n = 311); 47.9% (n = 149) of those had records of a NICU admission. The mortality rate by time points (ratio1:10,0000 births) related to the age at death during the follow-up period was as follows: 29, up to 7 days; 20, 7-28 days; 37, 28 days to 6 months; 21, 6 months to 1 year; 19, 1-2 years; 9, 2-3 years; 10, 3-4 years; and 27, 4 years and more. Following the exclusion of congenital malformations and chromosomal abnormalities, NICU admission remained the most significant risk factor associated with mortality of the study population, HRs, 364.4 [145.3; 913.3] for mortality in the first 7 days of life; 19.6 [12.1; 32.0] for mortality from 28 days through 6 months of life and remained markedly elevated after age 4 years; HR, 7.1 [3.0; 17.0]. The mortality risk related to the NICU admission event, adjusted for admission diagnoses remained significant; HR = 8.21 [5.43; 12.4]. CONCLUSIONS: NICU admission for term neonates is a pondering event for the risk of long-term childhood mortality. This group of term neonates may benefit from focused health care.

DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts.

SUMMARY: Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, 'distilled' models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine-learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91 and 0.87, respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e. shuffled sequences classified as proteins by DistilProtBert), we may be able to identify de novo potential natural-like proteins based on random shuffling of amino acid sequences. AVAILABILITY AND IMPLEMENTATION: https://github.com/yarongef/DistilProtBert.

Pseudouridines on Trypanosoma brucei mRNAs are developmentally regulated: Implications to mRNA stability and protein binding.

The parasite Trypanosoma brucei cycles between an insect and a mammalian host and is the causative agent of sleeping sickness. Here, we performed high-throughput mapping of pseudouridines (Ψs) on mRNA from two life stages of the parasite. The analysis revealed ~273 Ψs, including developmentally regulated Ψs that are guided by homologs of pseudouridine synthases (PUS1, 3, 5, and 7). Mutating the U that undergoes pseudouridylation in the 3' UTR of valyl-tRNA synthetase destabilized the mRNA level. To investigate the mechanism by which Ψ affects the stability of this mRNA, proteins that bind to the 3' UTR were identified, including the RNA binding protein RBSR1. The binding of RBSR1 protein to the 3' UTR was stronger when lacking Ψ compared to transcripts carrying the modification, suggesting that Ψ can inhibit the binding of proteins to their target and thus affect the stability of mRNAs. Consequently, Ψ modification on mRNA adds an additional level of regulation to the dominant post-transcriptional control in these parasites.

Visualizing the structure and motion of the long noncoding RNA HOTAIR.

Long noncoding RNA molecules (lncRNAs) are estimated to account for the majority of eukaryotic genomic transcripts, and have been associated with multiple diseases in humans. However, our understanding of their structure-function relationships is scarce, with structural evidence coming mostly from indirect biochemical approaches or computational predictions. Here we describe direct visualization of the lncRNA HOTAIR (HOx Transcript AntIsense RNA) using atomic force microscopy (AFM) in nucleus-like conditions at 37°. Our observations reveal that HOTAIR has a discernible, although flexible, shape. Fast AFM scanning enabled the quantification of the motion of HOTAIR, and provided visual evidence of physical interactions with genomic DNA segments. Our report provides a biologically plausible description of the anatomy and intrinsic properties of HOTAIR, and presents a framework for studying the structural biology of lncRNAs.

Normal labor curve in twin gestation.

BACKGROUND: Failure to progress is one of the leading indications for cesarean delivery in trials of labor in twin gestations. However, assessment of labor progression in twin labors is managed according to singleton labor curves. OBJECTIVE: This study aimed to establish a partogram for twin deliveries that reflects normal and abnormal labor progression and customized labor curves for different subgroups of twin labors. STUDY DESIGN: This was a multicenter, retrospective cohort analysis of twin deliveries that were recorded in 3 tertiary medical centers between 2003 and 2017. Eligible parturients were those with twin gestations at ≥34 weeks' gestation with cephalic presentation of the presenting twin and ≥2 cervical examinations during labor. Exclusion criteria were elective cesarean delivery without a trial of labor, major fetal anomalies, and fetal demise. The study group comprised twin gestations, whereas singleton gestations comprised the control group. Statistical analysis was performed using Python 3.7.3 and SPSS, version 27. Categorical variables were analyzed using chi-square tests. Student t test and Mann-Whitney U test were applied to analyze the differences in continuous variables, as appropriate. RESULTS: A total of 1375 twin deliveries and 142,659 singleton deliveries met the inclusion criteria. Duration of the active phase of labor was significantly longer in twin labors than in singleton labors in both nulliparous and multiparous parturients; the 95th percentile duration was 2 hours longer in nulliparous twin labors and >3.5 hours longer in multiparous twin labors than in singleton labors. The cervical dilation progression rate was significantly slower in twin deliveries than in singleton deliveries with a mean rate in twin deliveries of 1.89 cm/h (95th percentile, 0.51 cm/h) and a mean rate of 2.48 cm/h (95th percentile, 0.73 cm/h) in singleton deliveries (P<.001). In addition, epidural use further slowed labor progression in twin deliveries. The second stage of labor was also markedly longer in twin deliveries, both in nulliparous and multiparous women (95th percentile, 3.04 vs 2.83 hours, P=.002). CONCLUSION: Twin labors are characterized by a slower progression of the active phase and second stage of labor compared with singleton labors in nulliparous and multiparous parturients. Epidural analgesia further slows labor progression in twin labors. Implementation of these findings in clinical management might lower cesarean delivery rates among cases with protracted labor in twin gestations.

Prediction model for obstetric anal sphincter injury using machine learning.

INTRODUCTION AND HYPOTHESIS: Obstetric anal sphincter injury (OASI) is a complication with substantial maternal morbidity. The aim of this study was to develop a machine learning model that would allow a personalized prediction algorithm for OASI, based on maternal and fetal variables collected at admission to labor. MATERIALS AND METHODS: We performed a retrospective cohort study at a tertiary university hospital. Included were term deliveries (live, singleton, vertex). A comparison was made between women diagnosed with OASI and those without such injury. For formation of a machine learning-based model, a gradient boosting machine learning algorithm was implemented. Evaluation of the performance model was achieved using the area under the receiver-operating characteristic curve (AUC). RESULTS: Our cohort comprised 98,463 deliveries, of which 323 (0.3%) were diagnosed with OASI. Applying a machine learning model to data recorded during admission to labor allowed for individualized risk assessment with an AUC of 0.756 (95% CI 0.732-0.780). According to this model, a lower number of previous births, fewer pregnancies, decreased maternal weight and advanced gestational week elevated the risk for OASI. With regard to parity, women with one previous delivery had approximately 1/3 of the risk for OASI compared to nulliparous women (OR = 0.3 (0.23-0.39), p < 0.001), and women with two previous deliveries had 1/3 of the risk compared to women with one previous delivery (OR = 0.35 (0.21-0.60), p < 0.001). CONCLUSION: Our machine learning-based model stratified births to high or low risk for OASI, making it an applicable tool for personalized decision-making upon admission to labor.

Small nucleolar RNAs controlling rRNA processing in Trypanosoma brucei.

In trypanosomes, in contrast to most eukaryotes, the large subunit (LSU) ribosomal RNA is fragmented into two large and four small ribosomal RNAs (srRNAs) pieces, and this additional processing likely requires trypanosome-specific factors. Here, we examined the role of 10 abundant small nucleolar RNAs (snoRNAs) involved in rRNA processing. We show that each snoRNA involved in LSU processing associates with factors engaged in either early or late biogenesis steps. Five of these snoRNAs interact with the intervening sequences of rRNA precursor, whereas the others only guide rRNA modifications. The function of the snoRNAs was explored by silencing snoRNAs. The data suggest that the LSU rRNA processing events do not correspond to the order of rRNA transcription, and that srRNAs 2, 4 and 6 which are part of LSU are processed before srRNA1. Interestingly, the 6 snoRNAs that affect srRNA1 processing guide modifications on rRNA positions that span locations from the protein exit tunnel to the srRNA1, suggesting that these modifications may serve as check-points preceding the liberation of srRNA1. This study identifies the highest number of snoRNAs so far described that are involved in rRNA processing and/or rRNA folding and highlights their function in the unique trypanosome rRNA maturation events.

Pseudouridines on Trypanosoma brucei spliceosomal small nuclear RNAs and their implication for RNA and protein interactions.

The parasite Trypanosoma brucei, the causative agent of sleeping sickness, cycles between an insect and a mammalian host. Here, we investigated the presence of pseudouridines (Ψs) on the spliceosomal small nuclear RNAs (snRNAs), which may enable growth at the very different temperatures characterizing the two hosts. To this end, we performed the first high-throughput mapping of spliceosomal snRNA Ψs by small RNA Ψ-seq. The analysis revealed 42 Ψs on T. brucei snRNAs, which is the highest number reported so far. We show that a trypanosome protein analogous to human protein WDR79, is essential for guiding Ψ on snRNAs but not on rRNAs. snoRNA species implicated in snRNA pseudouridylation were identified by a genome-wide approach based on ligation of RNAs following in vivo UV cross-linking. snRNA Ψs are guided by single hairpin snoRNAs, also implicated in rRNA modification. Depletion of such guiding snoRNA by RNAi compromised the guided modification on snRNA and reduced parasite growth at elevated temperatures. We further demonstrate that Ψ strengthens U4/U6 RNA-RNA and U2B"/U2A' proteins-U2 snRNA interaction at elevated temperatures. The existence of single hairpin RNAs that modify both the spliceosome and ribosome RNAs is unique for these parasites, and may be related to their ability to cycle between their two hosts that differ in temperature.

Transporting an Artificial Intelligence Model to Predict Emergency Cesarean Delivery: Overcoming Challenges Posed by Interfacility Variation.

Research using artificial intelligence (AI) in medicine is expected to significantly influence the practice of medicine and the delivery of health care in the near future. However, for successful deployment, the results must be transported across health care facilities. We present a cross-facilities application of an AI model that predicts the need for an emergency caesarean during birth. The transported model showed benefit; however, there can be challenges associated with interfacility variation in reporting practices.

Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries.

BACKGROUND: The process of childbirth is one of the most crucial events in the future health and development of the offspring. The vulnerability of parturients and fetuses during the delivery process led to the development of intrapartum monitoring methods and to the emergence of alternative methods of delivery. However, current monitoring methods fail to accurately discriminate between cases in which intervention is unnecessary, partly contributing to the high rates of cesarean deliveries worldwide. Machine learning methods are applied in various medical fields to create personalized prediction models. These methods are used to analyze abundant, complex data with intricate associations to aid in decision making. Initial attempts to predict vaginal delivery vs cesarean deliveries using machine learning tools did not utilize the vast amount of data recorded during labor. The data recorded during labor represent the dynamic process of labor and therefore may be invaluable for dynamic prediction of vaginal delivery. OBJECTIVE: We aimed to create a personalized machine learning-based prediction model to predict successful vaginal deliveries using real-time data acquired during the first stage of labor. STUDY DESIGN: Electronic medical records of labor occurring during a 12-year period in a tertiary referral center were explored and labeled. Four different models were created using input from multiple maternal and fetal parameters. Initial risk assessments for vaginal delivery were calculated using data available at the time of admission to the delivery unit, followed by models incorporating cervical examination data and fetal heart rate data, and finally, a model that integrates additional data available during the first stage of labor was created. RESULTS: A total of 94,480 cases in which a trial of labor was attempted were identified. Based on approximately 180 million data points from the first stage of labor, machine learning models were developed to predict successful vaginal deliveries. A model using data available at the time of admission to the delivery unit yielded an area under the curve of 0.817 (95% confidence interval, 0.811-0.823). Models that used real-time data increased prediction accuracy. A model that includes real-time cervical examination data had an initial area under the curve of 0.819 (95% confidence interval, 0.813-0.825) at first examination, which increased to an area under the curve of 0.917 (95% confidence interval, 0.913-0.921) by the end of the first stage. Adding the real-time fetal heart monitor data provided an area under the curve of 0.824 (95% confidence interval, 0.818-0.830) at first examination, which increased to an area under the curve of 0.928 (95% confidence interval, 0.924-0.932) by the end of the first stage. Finally, adding additional real-time data increased the area under the curve initially to 0.833 (95% confidence interval, 0.827-0.838) at the first cervical examination and up to 0.932 (95% confidence interval, 0.928-0.935) by the end of the first stage. CONCLUSION: Real-time data acquired throughout the process of labor significantly increased the prediction accuracy for vaginal delivery using machine learning models. These models enable translation and quantification of the data gathered in the delivery unit into a clinical tool that yields a reliable personalized risk score and helps avoid unnecessary interventions.

Prediction of vaginal birth after cesarean deliveries using machine learning.

BACKGROUND: Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients who undergo a trial of labor after cesarean delivery to reduce the burden of repeated cesarean deliveries. Complication rates are lowest when a vaginal birth is achieved and highest when an unplanned cesarean delivery is performed, which emphasizes the need to assess, in advance, the likelihood of a successful vaginal birth after cesarean delivery. Vaginal birth after cesarean delivery calculators have been developed in different populations; however, some limitations to their implementation into clinical practice have been described. Machine-learning methods enable investigation of large-scale datasets with input combinations that traditional statistical analysis tools have difficulty processing. OBJECTIVE: The aim of this study was to evaluate the feasibility of using machine-learning methods to predict a successful vaginal birth after cesarean delivery. STUDY DESIGN: The electronic medical records of singleton, term labors during a 12-year period in a tertiary referral center were analyzed. With the use of gradient boosting, models that incorporated multiple maternal and fetal features were created to predict successful vaginal birth in parturients who undergo a trial of labor after cesarean delivery. One model was created to provide a personalized risk score for vaginal birth after cesarean delivery with the use of features that are available as early as the first antenatal visit; a second model was created that reassesses this score after features are added that are available only in proximity to delivery. RESULTS: A cohort of 9888 parturients with 1 previous cesarean delivery was identified, of which 75.6% of parturients (n=7473) attempted a trial of labor, with a success rate of 88%. A machine-learning-based model to predict when vaginal delivery would be successful was developed. When features that are available at the first antenatal visit are used, the model showed a receiver operating characteristic curve with area under the curve of 0.745 (95% confidence interval, 0.728-0.762) that increased to 0.793 (95% confidence interval, 0.778-0.808) when features that are available in proximity to the delivery process were added. Additionally, for the later model, a risk stratification tool was built to allocate parturients into low-, medium-, and high-risk groups for failed trial of labor after cesarean delivery. The low- and medium-risk groups (42.4% and 25.6% of parturients, respectively) showed a success rate of 97.3% and 90.9%, respectively. The high-risk group (32.1%) had a vaginal delivery success rate of 73.3%. Application of the model to a cohort of parturients who elected a repeat cesarean delivery (n=2145) demonstrated that 31% of these parturients would have been allocated to the low- and medium-risk groups had a trial of labor been attempted. CONCLUSION: Trial of labor after cesarean delivery is safe for most parturients. Success rates are high, even in a population with high rates of trial of labor after cesarean delivery. Application of a machine-learning algorithm to assign a personalized risk score for a successful vaginal birth after cesarean delivery may help in decision-making and contribute to a reduction in cesarean delivery rates. Parturient allocation to risk groups may help delivery process management.

Contemporary patterns of labor in nulliparous and multiparous women.

BACKGROUND: Controversy surrounds the definition of "normal" and "abnormal" labor. OBJECTIVE: In this study, we used contemporary labor charts to explore labor patterns in large obstetric population (2011-2016). STUDY DESIGN: Detailed information from electronic medical records of live singleton deliveries at term (≥37 weeks of gestation) was extracted. Cases of elective cesarean deliveries, nonvertex presentation, and cesarean deliveries during the first stage of labor were excluded. RESULTS: Overall, 35,146 deliveries were included, of whom 15,948 deliveries (45.3%) were of nulliparous women. Median cervical dilation at admission was not significantly different between nulliparous (median, 4 cm; interquartile range, 3-5 cm) and multiparous women (median, 4 cm; interquartile range, 3-6 cm). In all, 99.3% of the women delivered vaginally. For nulliparous women, the median duration of the first stage of labor was 274 minutes (interquartile range, 145-441 minutes; 95th percentile, 747.5 minutes). Likewise, for multiparous women, the corresponding duration was 133 minutes (interquartile range, 56-244 minutes; 95th percentile, 494 minutes). During the latent phase (cervical dilation at admission, ≤4 cm), the time elapsed to the second stage of labor was 120-140 minutes longer in nulliparous women, whereas the gap between the groups decreased dramatically with advanced cervical dilation on admission. Nulliparous and multiparous women appeared to progress at a similar pace during the latent phase; however, after 5 cm, labor accelerated faster in multiparous women. Epidural anesthesia lengthens duration first and second stages of labor in all parities. Partograms according to cervical dilation at presentation are proposed. CONCLUSION: Cervical dilation rate is relatively constant between nulliparous and multiparous pregnant women during the latent phase. Time interval of the first stage was far slower than previously described, which allowed labor to continue for a longer period during this stage. These findings may reduce the rate of intrapartum iatrogenic interventions.

The large repertoire of 2'-O-methylation guided by C/D snoRNAs on Trypanosoma brucei rRNA.

The parasite Trypanosoma brucei cycles between insect and mammalian hosts, and is the causative agent of sleeping sickness. Here, we performed genome-wide mapping of 2'-O-methylations (Nms) on trypanosome rRNA using three high-throughput sequencing methods; RibOxi-seq, RiboMeth-seq and 2'-OMe-seq. This is the first study using three genome-wide mapping approaches on rRNA from the same species showing the discrepancy among the methods. RibOxi-seq detects all the sites, but RiboMeth-seq is the only method to evaluate the level of a single Nm site. The sequencing revealed at least ninety-nine Nms guided by eighty-five snoRNAs among these thirty-eight Nms are trypanosome specific sites. We present the sequence and target of the C/D snoRNAs guiding on rRNA. This is the highest number of Nms detected to date on rRNA of a single cell parasite. Based on RiboMeth-seq, several Nm sites were found to be differentially regulated at the two stages of the parasite life cycle, the insect procyclic form (PCF) versus the bloodstream form (BSF) in the mammalian host.

Cervical dilation at the time of epidural catheter insertion is not associated with the degree of prolongation of the first or second stages of labor, or the rate of instrumental vaginal delivery.

INTRODUCTION: Epidural analgesia (EA) is an established option for efficient intrapartum analgesia. Meta-analyses have shown that EA differentially affects the first stage of labor but prolongs the second. The question of EA timing remains open. We aimed to investigate whether EA prolongs delivery in total and whether the EA administration timing vis-à-vis cervical dilation at catheter insertion is associated with a modulation of its effects on the duration of the first and second stages, as well as the rate of instrumental vaginal delivery in primiparas and multiparas. MATERIAL AND METHODS: A retrospective electronic medical records-based study of 18 870 singleton term deliveries occurring in our institution from 2003 to 2015. Cervical dilation was determined within a half-hour of EA administration. We examined whether cervical dilation at EA administration correlated with the duration of the first and/or second stage, with the rate of prolonged second stage, and with the rate of interventional delivery. The study group was stratified to 10 subgroups defined by 1-cm intervals of cervical dilation at EA administration. Logistic regression modeling was applied to analyze the association between EA timing and rate of instrumental delivery while controlling for possible confounders. RESULTS: In primiparas, receiving EA correlated with longer medians of active first stage (+51 minutes; P < .001) and second stage (+55 minutes; P < .001). In multiparas, median increases in active first stage (+43 minutes; P < .001) and second stage (+8 minutes; P < .001) were noted. The timing of EA, vis-à-vis cervical dilation (1-10 cm) was not associated with a substantial modulation of these effects. Logistic regression showed that cervical dilation at EA was not associated with a higher instrumental vaginal delivery rate. CONCLUSIONS: Epidural analgesia prolonged the first and second stages of labor vs no epidural. Having EA was associated with a higher instrumental delivery rate but not with higher rates of maternal or neonatal complications, in primi- and multiparas. Importantly, the timing of EA, vis-à-vis cervical dilation, was not associated with substantial changes in the duration of labor stages or the instrumental delivery rate. Thus, EA may be offered early in the first stage of labor.

Unplanned Cesarean Delivery in the Second Stage of Labor Holds Higher Odds of Complications than in the First Stage, while Similar in Primiparas and Multiparas.

BACKGROUND: While endeavors to reduce cesarean delivery (CD) rates are given priority worldwide, it is important to evaluate if these efforts place parturients and neonates at risk. CD performed in the second stage of labor carries higher risks of maternal and fetal complications and is a more challenging surgical procedure than that performed in the first stage or before labor. In a population with a low CD rate, we sought to evaluate the rate of maternal and fetal complications associated with unplanned CD (UCD) performed in the second vs. the first stage of labor, in primiparas and multiparas, as well as the risk factors leading to and the complications associated with UCD in the second stage of labor in this low-CD rate setting. METHODS: This was a retrospective, electronic medical record-based study of 7,635 term and preterm singletons born via UCD in the period 2003-2015. Maternal and neonatal background and outcome parameters were compared between groups. Logistic regression modeling was applied to adjust for clinically and statistically significant risk factors. RESULTS: UCD was more likely to be performed in the second stage of labor in mothers delivering larger fetuses (head circumference and body weight ≥90 centile) and those with persistent occiput posterior (POP) presentation. UCD in the second stage was strongly associated with serious maternal complications (excessive hemorrhage and fever) compared to UCD performed in the first stage, in both primiparas and multiparas. CONCLUSIONS: UCD performed in the second stage of labor, while less frequent than first-stage UCD, is more likely with larger neonates and POP presentation, and is associated with a higher rate of maternal complications in primiparas and multiparas. Complication rates in our low-CD-rate population did not exceed those reported in the literature from high-CD-rate areas.