Objective: To identify the impact of embryo transfer time (total seconds from the loading of the transfer catheter to the expulsion of the embryo(s) into the uterine cavity) on clinical pregnancy (CPR), implantation (IR), and live birth (LBR) rates.
Design: Retrospective cohort study.
Setting: Academic hospital practice.
Patient(s): A total of 465 women undergoing 571 frozen-embryo transfers with the use of cryopreserved blastocysts in a single academic institution from 2007 through 2014.
Intervention(s): None.
Main Outcome Measure(s): CPR, IR, and LBR.
Result(s): The cohort was divided into tertiles according to transfer time in seconds (T1: 33-55; T2: 57-81; T3: 82-582) with mean (SD) transfer times of 47.4 (5.7), 67.1 (7.3), and 121.9 (55.1) seconds, respectively. Crude CPRs were 43.9%, 48.7%, and 48.7% among the respective tertiles, crude IRs were 36.9%, 39.9%, and 38.6%, and crude LBRs were 34.8%, 39.6%, and 36.0%. In univariate analysis, inferior cohort score, blood inside catheter, difficult mock transfer, and use of an outer sheath were negatively associated with CPR. No association was seen between physician performing the transfer (including fellows) and CPR. In multivariate regression, longer transfer time was not associated with CPR. With T1 as reference, adjusted odds ratios (95% confidence interval) were 1.28 (0.77-2.11) and 1.52 (0.85-2.71) for transfer time groups T2 and T3, respectively.
Conclusion(s): After adjusting for potential confounders, this analysis found that contrary to commonly held belief, longer embryo transfer times do not negatively affect CPR, IR, or LBR.
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http://dx.doi.org/10.1016/j.fertnstert.2017.11.031 | DOI Listing |
Natl Sci Rev
January 2025
CAS Key Laboratory of Organic Solids, Beijing National Laboratory for Molecular Sciences (BNLMS), CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.
In the face of advancements in microrobotics, intelligent control and precision medicine, artificial muscle actuation systems must meet demands for precise control, high stability, environmental adaptability and high integration miniaturization. Carbon materials, being lightweight, strong and highly conductive and flexible, show great potential for artificial muscles. Inspired by the butterfly's proboscis, we have developed a carbon-based artificial muscle, hydrogen-substituted graphdiyne muscle (HsGDY-M), fabricated efficiently using an emerging hydrogen-substituted graphdiyne (HsGDY) film with an asymmetrical surface structure.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, College of Life Sciences, Taizhou University, Taizhou, China.
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View Article and Find Full Text PDFJAMIA Open
February 2025
Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, United States.
Objectives: In the general hospital wards, machine learning (ML)-based early warning systems (EWSs) can identify patients at risk of deterioration to facilitate rescue interventions. We assess subpopulation performance of a ML-based EWS on medical and surgical adult patients admitted to general hospital wards.
Materials And Methods: We assessed the scores of an EWS integrated into the electronic health record and calculated every 15 minutes to predict a composite adverse event (AE): all-cause mortality, transfer to intensive care, cardiac arrest, or rapid response team evaluation.
Background While key to interpreting findings and assessing generalizability, implementation fidelity is underreported in mobile health (mHealth) literature. We evaluated implementation fidelity of an opt-in, hybrid, two-way texting (2wT) intervention previously demonstrated to improve 12-month retention on antiretroviral therapy (ART) among people living with HIV (PLHIV) in a quasi-experimental study in Lilongwe, Malawi. Methods Short message service (SMS) data and ART refill visit records were used to evaluate adherence to 2wT content, frequency and duration through the lens of the Conceptual Framework for Implementation Fidelity.
View Article and Find Full Text PDFMachine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the convenient and non-invasive nature of data acquisition. Our group has successfully developed a novel approach that uses convolutional neural network with transfer learning to analyze spectrogram images of the sustained vowel /a/ to identify people with Parkinson's disease.
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