Exploring the Strategy to Improve the Clinical Pregnancy Rate of Assisted Reproduction Technology (ART) Basing on ART Nodes
Department of Genetics and Reproduction, the First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| Abstract: | In recent years, the numbers of infertility were increasing rapidly, which was affected by the environmental pollution, the pressure from work and life, the delay of childbearing age, and so on. The assisted reproduction technology (ART) was one of the key technologies for resolving infertility. However, the pregnancy rate of the ART ranged between 50% and 60%, thus, how to improve the pregnancy rate of ART was a hotpot in the field of ART. Lots of the nodes were included in the process of ART, including scientific preparation of pregnancy for the infertility couples, the checking of the reason for infertility, the suitable controlling ovulation stimulation strategy, embryo culturing, embryo transferring, pregnancy checking and prevention of abortion. The insufficient preparation of each node could cause the failure of ART. Thus, analyzing the factors which affected the pregnancy rate of ART, such as lacking the preparation of pregnancy, errors made in the process of diagnosis for infertility, unsuitable controlling ovulation stimulation strategy, unsuitable medicine usage after embryo transferring, and finding the possibly relevant solutions strategy to improve the pregnancy rate of ART, which would provide useful reference for improving the pregnancy rate of ART and has important theoretical value and application prospect in future in the field of human reproduction. |
| Keywords: | Assisted Reproduction Technology; ART Node; Pregnancy Rate |
| DOI: | 10.57237/j.mrf.2022.01.002 |
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Received: 6 September 2022
Accepted: 13 October 2022
Published Online: 1 November 2022
Funding
| 1. | 上海市胚胎源性疾病重点实验室开放课题 (She2020006) |
| 2. | 云南省科技厅面上项目 (2019FB096) |
| 3. | 昆明医科大学教改项目 (2019-JY-Y-053) |
| 4. | 云南省卫生健康委员会高层次医学学科带头人 (D-2017007) |
| 5. | 云南省高层次人才培养职称计划“名医专项” (RLMY20200017) |
| 6. | 国家自然科学基金项目 (82160281) |
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