Reproductive Biology and Endocrinology最新文献

Background: Among the POSEIDON criteria, group 3 and group 4 have an expected low prognosis. For those patients with inadequate ovary reserve, embryo accumulated from consecutive oocyte retrieval cycles for multiple frozen-thawed embryo transfers (FET) has become more common. It is necessary to inform them of the pregnancy outcomes after single or multiple FET cycles before the treatment. However few studies about cumulative live birth rate (CLBR) for those with low prognosis have been reported.

Methods: This retrospective study included 4712 patients undergoing frozen embryo transfer cycles from July 2015 to August 2020. Patients were stratified as POSEIDON group 3, group 4, control 1 group (< 35 years) and control 2 group (≥ 35 years). The primary outcome is CLBRs up to six FET cycles and the secondary outcomes were LBRs per transfer cycle. Optimistic approach was used for the analysis of CLBRs and the depiction of cumulative incidence curves.

Results: Under optimistic model analyses, control 1 group exhibited the highest CLBR (93.98%, 95%CI 91.63-95.67%) within 6 FET cycles, followed by the CLBR from women in POSEIDON group 3(92.51%, 95%CI 77.1-97.55)was slightly lower than that in control 1 group. The CLBR of POSEIDON group 4(55% ,95%CI 39.34-70.66%)was the lowest and significantly lower than that of control 2 group(88.7%, 95%CI 80.68-96.72%). Further, patients in POSEIDON group 4 reached a CLBR plateau after 5 FET cycles.

Conclusions: The patients of POSEIDON group 3 may not be considered as traditional "low prognosis" in clinical practice as extending the number of FET cycles up to 6 can archive considerably CLBR as control women. While for the POSEIDON group 4, a simple repeat of the FET cycle is not recommended after four failed FET cycles, some strategies such as PGT-A may be beneficial.

This comprehensive review explores the evolving landscape of sperm selection techniques within the realm of Assisted Reproductive Technology (ART). Our analysis delves into a range of methods from traditional approaches like density gradient centrifugation to advanced techniques such as Magnetic-Activated Cell Sorting (MACS) and Intracytoplasmic Morphologically Selected Sperm Injection (IMSI). We critically assess the efficacy of these methods in terms of sperm motility, morphology, DNA integrity, and other functional attributes, providing a detailed comparison of their clinical outcomes. We highlight the transition from conventional sperm selection methods, which primarily focus on physical characteristics, to more sophisticated techniques that offer a comprehensive evaluation of sperm molecular properties. This shift not only promises enhanced prediction of fertilization success but also has significant implications for improving embryo quality and increasing the chances of live birth. By synthesizing various studies and research papers, we present an in-depth analysis of the predictability of different sperm selection procedures in ART. The review also discusses the clinical applicability of these methods, emphasizing their potential in shaping the future of assisted reproduction. Our findings suggest that the integration of advanced sperm selection strategies in ART could lead to more cost-effective treatments with reduced duration and higher success rates. This review aims to provide clinicians and researchers in reproductive medicine with comprehensive insights into the current state and future prospects of sperm selection technologies in ART.

Fatigue, an increasingly acknowledged symptom in various chronic diseases, has garnered heightened attention, during the medical era of bio-psycho-social model. Its persistence not only significantly compromises an individual's quality of life but also correlates with chronic organ damage. Surprisingly, the intricate relationship between fatigue and female reproductive health, specifically infertility, remains largely unexplored. Our exploration into the existing body of evidence establishes a compelling link between fatigue with uterine and ovarian diseases, as well as conditions associated with infertility, such as rheumatism. This observation suggests a potentially pivotal role of fatigue in influencing overall female fertility. Furthermore, we propose a hypothetical mechanism elucidating the impact of fatigue on infertility from multiple perspectives, postulating that neuroendocrine, neurotransmitter, inflammatory immune, and mitochondrial dysfunction resulting from fatigue and its co-factors may further contribute to endocrine disorders, menstrual irregularities, and sexual dysfunction, ultimately leading to infertility. In addition to providing this comprehensive theoretical framework, we summarize anti-fatigue strategies and accentuate current knowledge gaps. By doing so, our aim is to offer novel insights, stimulate further research, and advance our understanding of the crucial interplay between fatigue and female reproductive health.

Background: The cumulative live birth rate (CLBR) has been regarded as a key measure of in vitro fertilization (IVF) success after a complete treatment cycle. Women undergoing IVF face great psychological pressure and financial burden. A predictive model to estimate CLBR is needed in clinical practice for patient counselling and shaping expectations.

Methods: This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation.

Results: Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05.

Conclusions: This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated ameliora

Objective: Ovarian stimulation (OS) with high daily gonadotropin doses are commonly offered to patients attempting social/elective egg freezing. However, the optimal daily gonadotropin dose that would allow a higher oocyte yield in the successive IVF cycle attempt was not settled and should be determined.

Patients and methods: Data from all women admitted to our IVF unit for social/EEF, who underwent two consecutive IVF cycle attempts, with only those who used in the first attempt a starting daily gonadotropin dose of 300IU were analyzed. Patients characteristics and OS variables were used in an attempt to build a logistic model, helping in determining the daily gonadotropin dose that should be offered to patient during their second EEF attempt, aiming to further increase their oocyte yield.

Results: Three hundred and thirteen consecutive women undergoing two successive IVF cycle attempts were evaluated. Using logistic regression model, two equations were developed using individual patient-level data that determine the daily gonadotropin dose needed aiming to increase the oocyte yield in the successive cycle. (a): X=-0.514 + 2.87*A1 + 1.733*A2-0.194* (E2/1000) and (b): P = EXP(X) / [1 + EXP(X)].

Conclusions: Using the aforementioned equations succeeded in determining the daily gonadotropin dose that might result in increasing oocyte yield, with an AUC of 0.85. Any additional oocyte retrieved to these EEF patients might get them closer to fulfil their desire to parenthood.

Background: The epidemiologic evidence on the association between acid load potential of diet and the risk of diminished ovarian reserve (DOR) is scarce. We aim to explore the possible relationship between dietary acid load (DAL), markers of ovarian reserve and DOR risk in a case-control study.

Methods: 370 women (120 women with DOR and 250 women with normal ovarian reserve as controls), matched by age and BMI, were recruited. Dietary intake was obtained using a validated 80-item semi-quantitative food frequency questionnaire (FFQ). The DAL scores including the potential renal acid load (PRAL) and net endogenous acid production (NEAP) were calculated based on nutrients intake. NEAP and PRAL scores were categorized by quartiles based on the distribution of controls. Antral follicle count (AFC), serum antimullerian hormone (AMH) and anthropometric indices were measured. Logistic regression models were used to estimate multivariable odds ratio (OR) of DOR across quartiles of NEAP and PRAL scores.

Results: Following increase in PRAL and NEAP scores, serum AMH significantly decreased in women with DOR. Also, AFC count had a significant decrease following increase in PRAL score (P = 0.045). After adjustment for multiple confounding variables, participants in the top quartile of PRAL had increased OR for DOR (OR: 1.26; 95%CI: 1.08-1.42, P = 0.254).

Conclusion: Diets with high acid-forming potential may negatively affect ovarian reserve in women with DOR. Also, high DAL may increase the risk of DOR. The association between DAL and markers of ovarian reserve should be explored in prospective studies and clinical trials.

Background: Prospective observational studies have demonstrated that the machine learning (ML) -guided noninvasive chromosome screening (NICS) grading system, which we called the noninvasive chromosome screening-artificial intelligence (NICS-AI) grading system, can be used embryo selection. The current prospective interventional clinical study was conducted to investigate whether this NICS-AI grading system can be used as a powerful tool for embryo selection.

Methods: Patients who visited our centre between October 2018 and December 2021 were recruited. Grade A and B embryos with a high probability of euploidy were transferred in the NICS group. The patients in the control group selected the embryos according to the traditional morphological grading. Finally, 90 patients in the NICS group and 161 patients in the control group were compared statistically for their clinical outcomes.

Results: In the NICS group, the clinical pregnancy rate (70.0% vs. 54.0%, p < 0.001), the ongoing pregnancy rate (58.9% vs. 44.7%, p = 0.001), and the live birth rate (56.7% vs. 42.9%, p = 0.001) were significantly higher than those of the control group. When the female was ≥ 35 years old, the clinical pregnancy rate (67.7% vs. 32.1%, p < 0.001), ongoing pregnancy rate (56.5% vs. 25.0%, p = 0.001), and live birth rate (54.8% vs. 25.0%, p = 0.001) in the NICS group were significantly higher than those of the control group. Regardless of whether the patients had a previous record of early spontaneous abortion or not, the live birth rate of the NICS group was higher than that of the control group (61.0% vs. 46.9%; 57.9% vs. 34.8%; 33.3% vs. 0%) but the differences were not statistically significant.

Conclusions: NICS-AI was able to improve embryo utilisation rate, and the live birth rate, especially for those ≥ 35 years old, with transfer of Grade A embryos being preferred, followed by Grade B embryos. NICS-AI can be used as an effective tool for embryo selection in the future.

Background: The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analyses driven by artificial intelligence have been presented recently to automate and objectify picture assessments.

Methods: In present retrospective study, a total of 3448 biopsied blastocysts from 979 Time-lapse (TL)-PGT cycles were retrospectively analyzed. The "intelligent data analysis (iDA) Score" as a deep learning algorithm was used in TL incubators and assigned each blastocyst with a score between 1.0 and 9.9.

Results: Significant differences were observed in iDAScore among blastocysts with different ploidy. Additionally, multivariate logistic regression analysis showed that higher scores were significantly correlated with euploidy (p < 0.001). The Area Under the Curve (AUC) of iDAScore alone for predicting euploidy embryo is 0.612, but rose to 0.688 by adding clinical and embryonic characteristics.

Conclusions: This study provided additional information to strengthen the clinical applicability of iDAScore. This may provide a non-invasive and inexpensive alternative for patients who have no available blastocyst for biopsy or who are economically disadvantaged. However, the accuracy of embryo ploidy is still dependent on the results of next-generation sequencing technology (NGS) analysis.