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It remains an enigma whether gestational hypertension (GH) and pre-eclampsia (PE) are distinct entities or different spectrum of the same disease. We aimed to compare the risk factors and outcomes between GH and PE. A total of 7,633 pregnant women recruited between 12 and 20 weeks of gestation in the Ottawa and Kingston Birth Cohort from 2002 to 2009 were included in the analysis. Cox proportional hazards model was used to identify and compare the risk factors for GH and PE by treating gestational age at delivery as the survival time. Logistic regression model was used to compare outcome. Subgroup analysis was performed for early- and late-onset PE. GH and PE shared most risk factors including overweight and obesity, nulliparity, PE history, type 1 and 2 diabetes, and twin birth. Effect size of PE history (RR = 14.1 for GH vs. RR = 6.4 for PE) and twin birth (RR = 4.8 for GH vs. RR = 10.3 for PE) showed substantial difference. Risk factors modified gestational age at delivery in patients with GH and PE in similar pattern. Subgroup analysis showed that early- and late-onset PE shared some risk factors with different effect sizes, whereas folic acid supplementation showed protective effect for early-onset PE only. PE was strongly associated with several adverse outcomes including cesarean section, placental abruption, small for gestational age, preterm birth, and 5 min Apgar score < 7, whereas GH was associated with increased risk of preterm birth only. GH and PE shared common risk factors. Differences in effect sizes of risk factors and outcomes indicate that the conditions may have different pathophysiology and mechanism.

Background Lanthipeptides belong to the ribosomally synthesized and post-translationally modified peptide group of natural products and have a variety of biological activities ranging from antibiotics to antinociceptives. These peptides are cyclized through thioether crosslinks and can bear other secondary post-translational modifications. While lanthipeptide biosynthetic gene clusters can be identified by the presence of genes encoding characteristic enzymes involved in the post-translational modification process, locating the precursor peptides encoded within these clusters is challenging due to their short length and high sequence variability, which limits the high-throughput exploration of lanthipeptide biosynthesis. To address this challenge, we enhanced the predictive capabilities of Rapid ORF Description & Evaluation Online (RODEO) to identify members of all four known classes of lanthipeptides. Results Using RODEO, we mined over 100,000 bacterial and archaeal genomes in the RefSeq database. We identified nearly 8500 lanthipeptide precursor peptides. These precursor peptides were identified in a broad range of bacterial phyla as well as the Euryarchaeota phylum of archaea. Bacteroidetes were found to encode a large number of these biosynthetic gene clusters, despite making up a relatively small portion of the genomes in this dataset. A number of these precursor peptides are similar to those of previously characterized lanthipeptides, but even more were not, including potential antibiotics. One such new antimicrobial lanthipeptide was purified and characterized. Additionally, examination of the biosynthetic gene clusters revealed that enzymes installing secondary post-translational modifications are more widespread than initially thought. Conclusion Lanthipeptide biosynthetic gene clusters are more widely distributed and the precursor peptides encoded within these clusters are more diverse than previously appreciated, demonstrating that the lanthipeptide sequence-function space remains largely underexplored.

Structural and biochemical studies show that the biosynthesis of the food preservative nisin involves the tRNA-dependent glutamylation of serine and threonine. Mechanisms of lantibiotic biosynthesis Nisin, a member of the lantibiotic family of thioether-bridge containing antibiotics, has been used widely in the food industry for more than 40 years without substantial development of resistance. This property has become of particular interest in light of the emergence of resistance to many clinically used antibiotics. Wilfred van der Donk and colleagues present the X-ray structure of the lantibiotic dehydratase NisB, an enzyme involved in nisin biosynthesis, and use biochemical data to show that NisB utilizes glutamyl-tRNA Glu in the critical activation of Ser/Thr residues. These findings provide a basis for the functional characterization of the many lantibiotic-like dehydratases involved in the biosynthesis of other classes of natural products. Lantibiotics are a class of peptide antibiotics that contain one or more thioether bonds. The lantibiotic nisin is an antimicrobial peptide that is widely used as a food preservative to combat food-borne pathogens 1 . Nisin contains dehydroalanine and dehydrobutyrine residues that are formed by the dehydration of Ser/Thr by the lantibiotic dehydratase NisB (ref. 2 ). Recent biochemical studies revealed that NisB glutamylates Ser/Thr side chains as part of the dehydration process 3 . However, the molecular mechanism by which NisB uses glutamate to catalyse dehydration remains unresolved. Here we show that this process involves glutamyl-tRNA Glu to activate Ser/Thr residues. In addition, the 2.9-Å crystal structure of NisB in complex with its substrate peptide NisA reveals the presence of two separate domains that catalyse the Ser/Thr glutamylation and glutamate elimination steps. The co-crystal structure also provides insights into substrate recognition by lantibiotic dehydratases. Our findings demonstrate an unexpected role for aminoacyl-tRNA in the formation of dehydroamino acids in lantibiotics, and serve as a basis for the functional characterization of the many lantibiotic-like dehydratases involved in the biosynthesis of other classes of natural products.

In this article we describe the production and screening of a genetically encoded library of 106 lanthipeptides in Escherichia coli using the substrate-tolerant lanthipeptide synthetase ProcM. This plasmid-encoded library was combined with a bacterial reverse two-hybrid system for the interaction of the HIV p6 protein with the UEV domain of the human TSG101 protein, which is a critical protein–protein interaction for HIV budding from infected cells. Using this approach, we identified an inhibitor of this interaction from the lanthipeptide library, whose activity was verified in vitro and in cell-based virus-like particle-budding assays. Given the variety of lanthipeptide backbone scaffolds that may be produced with ProcM, this method may be used for the generation of genetically encoded libraries of natural product–like lanthipeptides containing substantial structural diversity. Such libraries may be combined with any cell-based assay to identify lanthipeptides with new biological activities.

Background Induction at 38–40 weeks of gestation has been broadly suggested for women with gestational diabetes mellitus (GDM), yet its benefits and risks remain unclear. This study aimed to systematically review and meta-analyze existing evidence on the effect of induction at term gestation among women with GDM. Methods We searched MEDLINE, EMBASE, Cochrane Libraries, and Web of Science from inception to June 2021. We included randomized controlled trials (RCTs) and observational studies comparing induction with expectant management among GDM term pregnancies. Primary outcomes included caesarean section (CS) and macrosomia. All screening and extraction were conducted independently and in duplicates. Meta-analyses with random-effects models were conducted to generate the pooled odds ratios (ORs) and 95% confidence intervals (CIs) using the Mantel-Haenszel method. Methodological quality was assessed independently by two reviewers using the Cochrane Risk of Bias Tool for RCTs and the Newcastle-Ottawa Scale for observational studies. Results Of the 4,791 citations, 11 studies were included (3 RCTs and 8 observational studies). Compared to expectant management, GDM women with induction had a significantly lower odds for macrosomia (RCTs 0.49 [0.30–0.81]); observational studies 0.64 [0.54–0.77]), but not for CS (RCTs 0.95 [0.64–1.43]); observational studies 1.03 [0.79–1.34]). Induction was associated with a lower odds of severe perineal lacerations in observational studies (0.59 [0.39–0.88]). No significant difference was observed for other maternal or neonatal morbidities, or perinatal mortality between groups. Conclusions For GDM women, induction may reduce the risk of macrosomia and severe perineal lacerations compared to expectant management. Further rigorous studies with large sample sizes are warranted to better inform clinical implications.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history, resulting in many delayed or missed diagnoses. While population-based screening would be ideal for early identification, available screening tools have limited accuracy. This study aims to determine whether machine learning models applied to health administrative and birth registry data can identify young children (aged 18 months to 5 years) who are at increased likelihood of developing ASD. We assembled the study cohort using individually linked maternal-newborn data from the Better Outcomes Registry and Network (BORN) Ontario database. The cohort included all live births in Ontario, Canada between April 1st, 2006, and March 31st, 2018, linked to datasets from Newborn Screening Ontario (NSO), Prenatal Screening Ontario (PSO), and Canadian Institute for Health Information (CIHI) (Discharge Abstract Database (DAD) and National Ambulatory Care Reporting System (NACRS)). The NSO and PSO datasets provided screening biomarker values and outcomes, while DAD and NACRS contained diagnosis codes and intervention codes for mothers and offspring. Extreme Gradient Boosting models and large-scale ensembled Transformer deep learning models were developed to predict ASD diagnosis between 18 and 60 months of age. Leveraging explainable artificial intelligence methods, we determined the impactful factors that contribute to increased likelihood of ASD at both an individual- and population-level. The final study cohort included 707,274 mother-offspring pairs, with 10,956 identified cases of ASD. The best-performing ensemble of Transformer models achieved an area under the receiver operating characteristic curve of 69.6% for predicting ASD diagnosis, a sensitivity of 70.9%, a specificity of 56.9%. We determine that our model can be used to identify an enriched pool of children with the greatest likelihood of developing ASD, demonstrating the feasibility of this approach.This study highlights the feasibility of employing machine learning models and routinely collected health data to systematically identify young children at high likelihood of developing ASD. Ensemble transformer models applied to health administrative and birth registry data offer a promising avenue for universal ASD screening. Such early detection enables targeted and formal assessment for timely diagnosis and early access to resources, support, or therapy.

The study aims to examine the blood pressure (BP) trajectory during pregnancy and its association with pre-gravid BP level. In a pre-conception cohort study, newly-married women in Liuyang, China underwent pre-gravid measurements and were followed throughout the pregnancy. BP was measured at pre-conception and again throughout pregnancy. The functional principal component analysis was used to examine the trajectory of BP changes during pregnancy. A total of 1282 women with a singleton pregnancy who had both pre-conception and gestational BP measurements performed were included in the final analysis. The results showed that BP decreased significantly in early pregnancy and increased thereafter, without BP drop around 20 weeks of gestation. Pre-gravid BP level was inversely associated with the BP drop in early pregnancy, such that women with higher pre-gravid BP had greater BP drop at the beginning, while women with the lowest pre-gravid BP level demonstrated no obvious BP drop throughout the entire pregnancy.

To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester. All first trimester ultrasound scans with a diagnosis of a cystic hygroma between 11 and 14 weeks gestation at our tertiary care centre in Ontario, Canada were studied. Ultrasound scans with normal nuchal translucency were used as controls. The dataset was partitioned with 75% of images used for model training and 25% used for model validation. Images were analyzed using a DenseNet model and the accuracy of the trained model to correctly identify cases of cystic hygroma was assessed by calculating sensitivity, specificity, and the area under the receiver-operating characteristic (ROC) curve. Gradient class activation heat maps (Grad-CAM) were generated to assess model interpretability. The dataset included 289 sagittal fetal ultrasound images;129 cystic hygroma cases and 160 normal NT controls. Overall model accuracy was 93% (95% CI: 88-98%), sensitivity 92% (95% CI: 79-100%), specificity 94% (95% CI: 91-96%), and the area under the ROC curve 0.94 (95% CI: 0.89-1.0). Grad-CAM heat maps demonstrated that the model predictions were driven primarily by the fetal posterior cervical area. Our findings demonstrate that deep-learning algorithms can achieve high accuracy in diagnostic interpretation of cystic hygroma in the first trimester, validated against expert clinical assessment.

ABSTRACTBackgroundMaternal obesity is associated with stillbirth, but uncertainty persists around the effects of higher obesity classes. We sought to compare the risk of stillbirth associated with maternal obesity alone versus maternal obesity and additional or undiagnosed factors contributing to high-risk pregnancy. MethodsWe conducted a retrospective cohort study using the Better Outcomes Registry and Network (BORN) for singleton hospital births in Ontario between 2012 and 2018. We used multivariable Cox proportional hazard regression and logistic regression to evaluate the relationship between prepregnancy maternal body mass index (BMI) class and stillbirth (reference was normal BMI). We treated maternal characteristics and obstetrical complications as independent covariates. We performed mediator analyses to measure the direct and indirect effects of BMI on stillbirth through major common-pathway complications. We used fully adjusted and partially adjusted models, representing the impact of maternal obesity alone and maternal obesity with other risk factors on stillbirth, respectively. ResultsWe analyzed data on 681 178 births between 2012 and 2018, of which 1956 were stillbirths. Class I obesity was associated with an increased incidence of stillbirth (adjusted hazard ratio [HR] 1.55, 95% confidence interval [CI] 1.35–1.78). This association was stronger for class III obesity (adjusted HR 1.80, 95% CI 1.44–2.24), and strongest for class II obesity (adjusted HR 2.17, 95% CI 1.83–2.57). Plotting point estimates for odds ratios, stratified by gestational age, showed a marked increase in the relative odds for stillbirth beyond 37 weeks’ gestation for those with obesity with and without other risk factors, compared with those with normal BMI. The impact of potential mediators was minimal. InterpretationMaternal obesity alone and obesity with other risk factors are associated with an increased risk of stillbirth. This risk increases with gestational age, especially at term.