Evaluation of the Arabin cervical pessary for prevention of preterm birth in women with a twin pregnancy and short cervix (STOPPIT-2):An open-label randomised trial and updated meta-analysis

BackgroundPreterm-labour-associated preterm birth is a common cause of perinatal mortality and morbidity in twin pregnancy. We aimed to test the hypothesis that the Arabin pessary would reduce preterm-labour-associated preterm birth by 40% or greater in women with a twin pregnancy and a short cervix.Methods and findingsWe conducted an open-label randomised controlled trial in 57 hospital antenatal clinics in the UK and Europe. From 1 April 2015 to 14 February 2019, 2,228 women with a twin pregnancy underwent cervical length screening between 18 weeks 0 days and 20 weeks 6 days of gestation. In total, 503 women with cervical length ≤ 35 mm were randomly assigned to pessary in addition to standard care (n = 250, mean age 32.4 years, mean cervical length 29 mm, with pessary inserted in 230 women [92.0%]) or standard care alone (n = 253, mean age 32.7 years, mean cervical length 30 mm). The pessary was inserted before 21 completed weeks of gestation and removed at between 35 and 36 weeks or before birth if earlier. The primary obstetric outcome, spontaneous onset of labour and birth before 34 weeks 0 days of gestation, was present in 46/250 (18.4%) in the pessary group compared to 52/253 (20.6%) following standard care alone (adjusted odds ratio [aOR] 0.87 [95% CI 0.55-1.38], p = 0.54). The primary neonatal outcome-a composite of any of stillbirth, neonatal death, periventricular leukomalacia, early respiratory morbidity, intraventricular haemorrhage, necrotising enterocolitis, or proven sepsis, from birth to 28 days after the expected date of delivery-was present in 67/500 infants (13.4%) in the pessary group compared to 76/506 (15.0%) following standard care alone (aOR 0.86 [95% CI 0.54-1.36], p = 0.50). The positive and negative likelihood ratios of a short cervix (≤35 mm) to predict preterm birth before 34 weeks were 2.14 and 0.83, respectively. A meta-analysis of data from existing publications (4 studies, 313 women) and from STOPPIT-2 indicated that a cervical pessary does not reduce preterm birth before 34 weeks in women with a short cervix (risk ratio 0.74 [95% CI 0.50-1.11], p = 0.15). No women died in either arm of the study; 4.4% of babies in the Arabin pessary group and 5.5% of babies in the standard treatment group died in utero or in the neonatal period (p = 0.53). Study limitations include lack of power to exclude a smaller than 40% reduction in preterm labour associated preterm birth, and to be conclusive about subgroup analyses.ConclusionsThese results led us to reject our hypothesis that the Arabin pessary would reduce the risk of the primary outcome by 40%. Smaller treatment effects cannot be ruled out.Trial registrationISRCTN Registry ISRCTN 02235181. ClinicalTrials.gov NCT02235181

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press