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Sperm DNA fragmentation index: limited effectiveness on predicting embryo quality in assisted reproduction technology treatments.
Reprod Biol Endocrinol
January 2025
Reproductive Medicine Center, Zhuhai Maternal and Child Health Care Hospital, 543 Ningxi Road, Zhuhai, 519000, China.
Purpose: Prior sperm DNA fragmentation index (DFI) thresholds for diagnosing male infertility and predicting assisted reproduction technology (ART) outcomes fluctuated between 15 and 30%, with no agreed standard. This study aimed to evaluate the impact of the sperm DFI on early embryonic development during ART treatments and establish appropriate DFI cut-off values.
Methods: Retrospectively analyzed 913 couple's ART cycles from 2021 to 2022, encompassing 1,476 IVF and 295 ICSI cycles, following strict criteria.
Establishment of critical values for AMH screening with PCOS based on restricted cubic spline plots.
Endocr Connect
January 2025
Y Liu, Department of Clinical Laboratory, Hangzhou Women's Hospital, Hangzhou, 310008, China.
Background: The aim is to develop age-specific anti-Müllerian hormone screening criteria for polycystic ovary syndrome to facilitate the early detection and diagnosis of the condition, and to subsequently evaluate the screening criteria.
Methods: A retrospective analysis was performed on patient data from Hangzhou Women's Hospital between July 2021 and August 2024. The use of restricted cubic spline analysis helped identify age-related inflection points, which were crucial for segmenting the patient population.
Exploring the Mechanism of Acupuncture in Improving Ovarian Function in Rats with Poor Ovarian Response Using High-Throughput Sequencing.
Comb Chem High Throughput Screen
January 2025
Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shierqiao Road, Jinniu District, Chengdu, Sichuan, 610072, P.R. China.
Objective: This study aimed to investigate the possible mechanism through which acupuncture protects ovaries with Poor Ovarian Response (POR) in rats based on microRNA (miRNA).
Methods: Thirty-six SPF SD female non-pregnant rats aged 8 weeks were randomly divided into the blank group, model group, and acupuncture group, with 12 rats in each group. According to the group, the rats were given gavage of Tripterygium wilfordii polyglycosides suspension for 14 days to establish the model of POR, and then the rats were treated with acupuncture for 2 weeks, once a day, for 20 minutes.
Evaluation of Predictors of Response to Ovulation Induction Using Letrozole in Women with Polycystic Ovary Syndrome: A Prospective Cohort Study.
J Hum Reprod Sci
December 2024
Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, New Delhi, India.
Background: Ovulation induction (OI) in patients with polycystic ovary syndrome (PCOS) remains challenging, and several biomarkers have been evaluated for their ability to predict ovulation. The predictive ability of candidate biomarkers, particularly with letrozole-based therapy in infertile PCOS women, remains inconclusive as it is yet to be evaluated in a prospective study.
Aim: To assess the role of anti-Müllerian hormone (AMH), follicle-stimulating hormone (FSH), luteinising hormone (LH)/FSH ratio, testosterone and free androgen index (FAI) as predictors of ovarian response to letrozole-based OI therapy during OI cycles in infertile women with PCOS from North India.
Predicting the risk of a high proportion of three/multiple pronuclei (3PN/MPN) zygotes in individual IVF cycles using comparative machine learning algorithms.
Eur J Obstet Gynecol Reprod Biol
January 2025
Center for Advanced Reproductive Medicine, Department of Obstetrics & Gynecology, University of Kansas Medical Center, Overland Park, KS 66211, USA. Electronic address:
Background: The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting the occurrence of a high proportion (>30 %) of 3PN/MPN zygotes in individual IVF cycles.
Methods: Eight machine learning algorithms were trained and compared, including the AdaBoost and Gaussian NB.
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