Reproductive Biology and Endocrinology最新文献

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.

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.

Background: Reproduction in women is at risk due to exposure to chemicals that can disrupt the endocrine system during different windows of sensitivity throughout life. Steroid hormone levels are fundamental for the normal development and function of the human reproductive system, including the ovary. This study aims to elucidate steroidogenesis at different life-stages in human ovaries.

Methods: We have developed a sensitive and specific LC-MS/MS method for 21 important steroid hormones and measured them at different life stages: in media from cultures of human fetal ovaries collected from elective terminations of normally progressing pregnancy and in media from adult ovaries from Caesarean section patients, and follicular fluid from women undergoing infertility treatment. Statistically significant differences in steroid hormone levels and their ratios were calculated with parametric tests. Principal component analysis (PCA) was applied to explore clustering of the ovarian-derived steroidogenic profiles.

Results: Comparison of the 21 steroid hormones revealed clear differences between the various ovarian-derived steroid profiles. Interestingly, we found biosynthesis of both canonical and "backdoor" pathway steroid hormones and corticosteroids in first and second trimester fetal and adult ovarian tissue cultures. 17α-estradiol, a less potent naturally occurring isomer of 17β-estradiol, was detected only in follicular fluid. PCA of the ovarian-derived profiles revealed clusters from: adult ovarian tissue cultures with relatively high levels of androgens; first trimester and second trimester fetal ovarian tissue cultures with relatively low estrogen levels; follicular fluid with the lowest androgens, but highest corticosteroid, progestogen and estradiol levels. Furthermore, ratios of specific steroid hormones showed higher estradiol/ testosterone and estrone/androstenedione (indicating higher CYP19A1 activity, p < 0.01) and higher 17-hydroxyprogesterone/progesterone and dehydroepiandrosterone /androstenedione (indicating higher CYP17A1 activity, p < 0.01) in fetal compared to adult ovarian tissue cultures.

Conclusions: Human ovaries demonstrate de novo synthesis of non-canonical and "backdoor" pathway steroid hormones and corticosteroids. Elucidating the steroid profiles in human ovaries improves our understanding of physiological, life-stage dependent, steroidogenic capacity of ovaries and will inform mechanistic studies to identify endocrine disrupting chemicals that affect female reproduction.

Background: Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the generalizability of object detection models, using sperm analysis as a pilot example.

Methods: Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model generalizability. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations.

Results: Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications.

Conclusions: The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model generalizability. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced generalizability across clinics.