The Effectiveness of Routine Cerclage in In Vitro Fertilization (IVF) Twins.

INTRODUCTION: The incidence of twin pregnancies is on the rise worldwide due to assisted reproductive technologies. Cervical dilatation is a frequent complication and can be considered a cause of premature births in twin pregnancies. In vitro fertilization (IVF) twins are more prone to preterm delivery. Routine cervical cerclage with normal cervical length is not recommended in twins, but studies were not done on IVF twins. So, this study aims to evaluate the effectiveness of routine transvaginal cerclage in twin IVF pregnancies with normal cervical length on maternofetal outcome. MATERIALS AND METHODS: A retrospective case-control study was conducted at a maternity hospital in eastern India from January 2016 to December 2019 with over 21800 deliveries. Two groups were taken, cases were those IVF twins with normal cervical length in whom cervical cerclage was done as they have referred to our hospital from the IVF centers for cerclage and in control no-cervical cerclage as they are referred from other IVF centers where cerclage was not a routine protocol. We have excluded cerclage done for history, ultrasound indicated, uterine anomalies, and monochorionic twins. Fifteen participants were recruited in both cases and controls. RESULTS: In our study 2 (6.66%), 4 (13%), and 26 (86%) were IVF twins delivered at a gestational age of <28 weeks, 28-32 weeks, and 32-37 weeks, respectively. The mean age of participants, mean gestation age at delivery, and birth weight in cases and control were 35.27 ± 5.98 years (min: 23; max: 45), 32.40 ± 5.54 years (min: 25; max: 44); 34 weeks 2 days ± 3.28 (min: 31; max: 37), 33 weeks 5 days ± 1.66 (min: 25; max: 37); 1961.33 ± 340 gram, 1899.33 ± 437.48 gram, respectively with no statistical significant difference (p = 0.186, p = 1, p = 0.668, respectively) Conclusion: Routine transvaginal cerclage is not effective in twin IVF pregnancies with normal cervical length for preventing preterm births. IVF twin women usually present at a late age and their birth weight are also low.

Electronic Excited States from Physically Constrained Machine Learning.

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly with physically grounded operations. We present an example of an integrated modeling approach in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those on which it is trained and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parametrization corresponding to a minimal atom-centered basis. These results emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency and providing a blueprint for developing ML-augmented electronic-structure methods.