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Background: The optimal mode of delivery in twin pregnancies remains controversial. A recent randomized trial did not find any benefit of planned cesarean vs. vaginal delivery at 32–38 weeks gestation, but the trial was not powered to detect a moderate effect. We aimed to evaluate the impact of cesarean delivery on perinatal mortality and severe neonatal morbidity in twin pregnancies at ≥32 weeks through a large database exploration approach with the power to detect moderate risk differences. Methods: In a retrospective birth cohort study using the U.S. matched multiple births, 1995–2000 (the available largest multiple birth dataset), we compared perinatal outcomes in twins (n = 181,810 pregnancies) delivered at 32–41 weeks gestation without congenital anomalies. The primary outcome was a composite of perinatal death and severe neonatal morbidity. Cox regression was used to estimate the adjusted hazard ratio (aHR) controlling for the propensity to cesarean delivery, fetal characteristics (sex, birth weight, birth weight discordance, same-sex twin or not) and twin-cluster level dependence. Prospective risks were calculated using the fetuses-at-risk denominators. Results: The overall rates of the primary outcome were slightly lower in intended cesarean (6.20%) vs. vaginal (6.45%) deliveries. The aHRs of the primary outcome were in favor of vaginal delivery at 32 (aHR = 1.06, p = 0.03) or 33 (aHR = 1.22, p<0.001) weeks, neutral at 34–35 weeks, but in favor of cesarean delivery at 36 (aHR = 0.94, p = 0.004), 37, 38 and 39+ weeks (aHR: 0.72 to 0.78, all p<0.001). The lower risk of the primary outcome for cesarean vs. vaginal deliveries at 36+ weeks of gestation remained when the analyses were restricted to different-sex (dichorionic) twins (aHR = 0.84, 95% CI 0.80–0.88). Conclusion: Cesarean delivery may be beneficial for perinatal outcomes overall in twin pregnancies at ≥36 weeks gestation. [ABSTRACT FROM AUTHOR]

Background: Cree births in Quebec are characterized by the highest reported prevalence of macrosomia (~35%) in the world. It is unclear whether Cree births are at greater elevated risk of perinatal and infant mortality than other First Nations relative to non-Aboriginal births in Quebec, and if macrosomia may be related. Methods: This was a population-based retrospective birth cohort study using the linked birth-infant death database for singleton births to mothers from Cree (n = 5,340), other First Nations (n = 10,810) and non-Aboriginal (n = 229,960) communities in Quebec, 1996–2010. Community type was ascertained by residential postal code and municipality name. The primary outcomes were perinatal and infant mortality. Results: Macrosomia (birth weight for gestational age >90th percentile) was substantially more frequent in Cree (38.0%) and other First Nations (21.9%) vs non-Aboriginal (9.4%) communities. Comparing Cree and other First Nations vs non-Aboriginal communities, perinatal mortality rates were 1.52 (95% confidence intervals 1.17, 1.98) and 1.34 (1.10, 1.64) times higher, and infant mortality rates 2.27 (1.71, 3.02) and 1.49 (1.16, 1.91) times higher, respectively. The risk elevations in perinatal and infant death in Cree communities attenuated after adjusting for maternal characteristics (age, education, marital status, parity), but became greater after further adjustment for birth weight (small, appropriate, or large for gestational age). Conclusions: Cree communities had greater risk elevations in perinatal and infant mortality than other First Nations relative to non-Aboriginal communities in Quebec. High prevalence of macrosomia did not explain the elevated risk of perinatal and infant mortality in Cree communities. [ABSTRACT FROM AUTHOR]

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets. [ABSTRACT FROM AUTHOR]