This meta-analysis analyzed the clinical pregnancy outcomes of repeated implantation failure (RIF) patients treated with immunomodulatory therapies. Publications (published by August 16, 2021) were identified by searching the PubMed, Embase, and Web of Science databases. The quality of the studies was evaluated with the Cochrane bias risk assessment tool, and a network meta-analysis was performed with Stata 14.0. The outcomes were clinical pregnancy rate (CPR), live birth rate (LBR), and implantation rate (IR). The results of our network meta-analysis of 16 RCTs (including 2,008 participants) show that PBMCs, PRP, and SC-GCSF can significantly improve the CPR compared with LMWH (PBMCs: OR 2.15; 95% CI 1.21–3.83; PRP: OR 2.38; 95% CI 1.08–5.24; SC-GCSF: OR 2.46; 95% CI 1.05–5.72). The LBR of PRP was significantly higher than those of IU-GCSF (OR 3.81; 95% CI 1.22–11.86), LMWH (OR 4.38; 95% CI 1.50–12.90), and intralipid (OR 3.85; 95% CI 1.03–14.29), and the LBR of PBMCs was also significantly better than that of LMWH (OR 2.35; 95% CI 1.14–4.85). Furthermore, PRP treatment significantly improved the IR compared with LMWH treatment (OR 2.81; 95% CI 1.07–7.4). The limited evidence from existing RCTs suggests that PBMCs and PRP are the best therapeutic options for RIF patients. However, owing to the quantity limitation, more top-quality research is required to obtain additional high-level evidence.
Research and development on digital twins of nuclear power systems has focused on high-precision real-time simulation and the prediction of local complex three-dimensional fluid dynamics. Traditional computational fluid dynamics (CFD) methods cannot take into consideration the efficiency and accuracy of fluid dynamics. In this study, a fast-flow field-prediction framework based on proper orthogonal decomposition (POD) and deep learning is proposed. Compressed data containing the original flow field information are obtained using POD and deep neural network (DNN) is used to construct the POD-DNN flow field reduction model to achieve fast flow field prediction. The calculation accuracy and speed of the reduced-order model are analyzed in detail, considering the flow field of the nuclear compressor and key flow equipment of the nuclear power system as objects. The results show that the average relative deviation of the POD-DNN is <10% and calculation time is <1% when compared to those of CFD. This research shows that the high-fidelity model constructed using model reduction and deep learning is a feasible method for the realization of digital twins of the nuclear power system in engineering.
This chapter is mainly focused on illustrating some introductory progress on thermal hydraulic issues of supercritical water, including heat transfer characteristics, pressure loss characteristics, flow stability issues and numerical method. These works are mainly performed in Nuclear Power Institute of China (NPIC) these years, to give a basic idea of elementary but important topics in this area. An analytical method was proposed up to predict the heat transfer coefficient and friction coefficient based on the two-layer wall function. Flow instability experiments have been carried out in a two-parallel-channel system with supercritical water, aiming to provide an up-to-date knowledge of supercritical flow instability phenomena and initial validation data for numerical analysis. An in-house code has been developed in NPIC in order to better utilize and further expand the experimental results on supercritical flow instability. At last, some future research directions are suggested for reference.
Background: This study compared clinical pregnancy outcomes of repeated implantation failure (RIF) patients treated with immunotherapies using a network meta-analysis.Methods: Publications are determined by searching the Pubmed, Embase and web of science databases. The search date is from the establishment of the database till August 2021. The outcomes were clinical pregnancy rate (CPR), live birth rate (LBR) and implantation rate (IR). The Cochrane bias risk assessment tool was applied to evaluate the quality of the study, and Stata 14.0 was used for the network meta-analysis.Results: A total of 16 RCTs including 2008 participants were included. The network meta-analysis results show that PBMC, PRP and SC-GCSF can significantly improve CPR compared with LMWH (PBMC: OR = 2.15; 95% CI, 1.21-3.83; PRP: OR = 2.38; 95% CI, 1.08-5.24; SC-GCSF: OR = 2.46; 95% CI, 1.05-5.72). The LBR of PRP was significantly higher than IU-GCSF (OR = 3.81; 95% CI, 1.22-11.86), LMWH (OR = 4.38; 95% CI, 1.50-12.90) and Intralipid (OR = 3.85; 95% CI, 1.03-14.29), and the LBR of PBMC was also significantly better than that of LMWH (OR = 2.35; 95% CI, 1.14-4.85). Furthermore, PRP treatment significantly improved IR compared with LMWH treatment (OR = 2.81; 95% CI, 1.07-7.4).Discussion: Based on the limited evidence from existing RCTs, PBMC and PRP seem to be the best therapeutic options for RIF patients. However, due to research quantity restrictions, more top-quality researches are required in the future to obtain additional high-level evidence.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.