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find Author "许佳文" 3 results
  • 辅助生殖技术与子代神经系统疾病的相关性研究回顾

    辅助生殖技术是目前治疗不孕症的主要手段之一。由于辅助生殖技术涉及对卵泡发育、精卵结合、胚胎形成、转运、种植过程的人为干预,自 1978 年首例试管婴儿诞生以来,其安全性,尤其是对子代健康的影响一直受到人们的关注。神经系统疾病是常见的出生缺陷之一。由于神经系统疾病通常影响儿童的精神、情绪、智力、运动、语言等关键能力,且神经系统的损伤往往不可修复,因此婴幼儿一旦罹患神经系统疾病对家庭和社会来说无疑是巨大的打击与负担。该文就辅助生殖技术与几种常见的子代神经系统疾病如智力低下、脑瘫、癫痫的关系进行了综述,并对辅助生殖技术对子代神经系统的安全性进行评价,从而进一步指导临床不孕症的治疗及围产期的母儿监护。

    Release date:2017-05-18 01:09 Export PDF Favorites Scan
  • Diagnosis and treatment of emergency complications after oocyte retrieval with assisted reproductive technology

    Objective To retrospectively analyze the emergency complications of the patients after oocyte retrieval with assisted reproductive technology (ART), and analyze the corresponding strategies. Methods The clinical data of patients after oocyte retrieval with ART between January and December 2016 were retrospectively anayzed. The postoperative emergency complications were observed. Results A total of 5 013 patients were included in the study. The common emergency complications after oocyte retrieval included vaginal bleeding in 137 cases (2.73%) , ovarian hyperstimulation syndrome (OHSS) in 35 (0.69%), hematuria caused by bladder injury in 11 cases (0.21%), pelvic infection in 3 (0.06%), and vagal reflex in 2 (0.04%). OHSS was related to age, the number of basal follicles, the number of oviposaccharides and the estradiol level on the day of human chorionic gonadotropin injection, but not related to the body mass index and the number of days of gonadotropin use; which might be misdiagnosed most likely. Conclusions OHSS is one of the common and severe emergency complications after oocyte retrieval with ART, which should be concerned. Active treatment of complications is helpful to reduce the incidence of emergency complications after oocyte retrieval with ART.

    Release date:2017-06-22 02:01 Export PDF Favorites Scan
  • Brain magnetic resonance image registration based on parallel lightweight convolution and multi-scale fusion

    Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.

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