Reproductive aging is a major cause of fertility decline, attributed to decreased oocyte quantity and developmental potential. A possible cause is aging of the surrounding follicular somatic cells that support oocyte growth and development by providing nutrients and regulatory factors. Here, by creating chimeric follicles, whereby an oocyte from one follicle was transplanted into and cultured within another follicle whose native oocyte was removed, we show that young oocytes cultured in aged follicles exhibited impeded meiotic maturation and developmental potential, whereas aged oocytes cultured within young follicles were significantly improved in rates of maturation, blastocyst formation and live birth after in vitro fertilization and embryo implantation. This rejuvenation of aged oocytes was associated with enhanced interaction with somatic cells, transcriptomic and metabolomic remodeling, improved mitochondrial function and higher fidelity of meiotic chromosome segregation. These findings provide the basis for a future follicular somatic cell-based therapy to treat female infertility.© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.

To determine whether baseline serum FSH and/or E2 concentrations can predict the risk for fetal chromosomal abnormalities.Case control study.Reproductive technology program at a university hospital.Patients who underwent dilation and curettage (D + C), and whose products of conception were karyotyped.Patients underwent natural conception or controlled ovarian hyperstimulation followed by intrauterine insemination, in vitro fertilization and embryo transfer, gamete intrafallopian transfer, or zygote intrafallopian transfer.Baseline serum FSH and E2 concentrations and fetal karyotype.Genetic evaluation of 78 D + C specimens revealed 34 normal and 44 abnormal fetal karyotypes. A significantly greater proportion of women with abnormal fetal karyotype had elevated baseline serum FSH (> or =15 mIU/mL [RIA] or 10 mIU/mL [Immulite]) and/or E2 > or = 50 pg/mL [Immulite]) compared with women of normal fetal karyotype. Among karyotypically abnormal abortuses, autosomal trisomy was the most common abnormality noted (79.5%), followed by mosaicism (6.8%), triploidy (6.8%), monosomy XO (4.5%), and balanced translocation (2.3%).Baseline serum FSH and/or E2 concentrations may be valuable as predictors of fetal aneuploidy.

Existing studies have investigated the relationship between the levels of serum inhibin B (INHB), anti-müllerian hormone (AMH) and precocious puberty in girls, but the results are inconsistent.The aim of this meta-analysis was to assess whether the INHB and AMH levels changed in girls with precocious puberty relative to healthy controls.PubMed, Embase, Cochrane Library and Web of Science were searched through June 2022. We included observational clinical studies reporting the serum levels INHB and AMH in girls with precocious puberty. Conference articles and observational study abstracts were included if they contained enough information regarding study design and outcome data. Case series and reports were excluded. An overall standard mean difference (SMD) between precocious puberty and healthy controls was estimated using a DerSimonian-Laird random-effects model.A total of 11 studies featuring 552 girls with precocious puberty and 405 healthy girls were selected for analysis. The meta-analysis showed that the INHB level of precocious puberty [including central precocious puberty (CPP) and premature the larche (PT)] were significantly increased. While there was no significant association between precocious puberty [including CPP, PT, premature pubarche (PP) and premature adrenarche (PA)] and the level of serum AMH.Scientific evidence suggested that the INHB level, but not the AMH level, altered in girls with precocious puberty compared with healthy controls. Through our results we think that INHB level might be a marker for the auxiliary diagnosis of precocious puberty (especially CPP and PT). Therefore, it is important to evaluate and thoroughly investigate the clinical indicators (e.g., INHB) in order to ensure early diagnosis and medical intervention, and the risk of physical, psychological and social disorders in immature girls with precocious puberty is minimized.© 2023. The Author(s).

To investigate the relationship between clinical markers of ovarian reserve and the true ovarian reserve as determined by the ovarian primordial follicle number.Prospective investigation.Academic medical center.Forty-two healthy women (aged 26-52 years) undergoing oophorectomy for benign gynecologic indications.Transvaginal ultrasound examination for the determination of the ovarian antral follicle count (AFC) and serum measurements of clinical markers of ovarian reserve. All measurements were obtained within 2 weeks of surgery, irrespective of cycle day. Ovarian primordial follicle count was then determined using a validated fractionator/optical disector method.Univariate and partial correlations between ovarian reserve markers and ovarian primordial follicle count.There were significant correlations between the ovarian primordial follicle count and AFC (r=0.78), anti-Müllerian hormone (AMH; r=0.72), FSH (r=-0.32), inhibin B (r=0.40), and chronological age (r=-0.80). After adjusting for age, significant correlations were identified between the ovarian primordial follicle count and AFC (r=0.53) and AMH (r=0.48).The ovarian AFC and serum levels of AMH correlate with the ovarian primordial follicle number even after adjustment for chronological age.Copyright © 2011 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

The value of serum AMH, INHB, and bFSH levels in assessing postoperative ovarian reserve function was analyzed by measuring serum anti-Mullerian hormone (AMH), inhibin B (INHB), and basal follicle-stimulating hormone (bFSH) levels in patients after laparoscopic cystectomy for endometrioma.

To characterize the ovarian reserve indicators for premature ovarian insufficiency (POI) at different disease stages and with various etiologies.

To determine if basal E(2) screening increases the diagnostic accuracy of basal FSH screening and to determine whether basal E(2) levels correlate with outcome in ART cycles.Retrospective.Tertiary care center.Two thousand six hundred thirty-four infertility patients.Cycle outcome was evaluated after grouping patients by basal E(2) levels beginning at <20 pg/mL and extending to >100 pg/mL at 10 pg/mL increments.Retrieved oocytes, pregnancy rate, and cancellation rate.Cancellation rates were significantly increased in patients with basal E(2) levels of <20 pg/mL or >/=80 pg/mL. Basal E(2) levels neither predicted pregnancy outcome nor correlated with ovarian response in those patients not canceled.Patients with basal E(2) levels of <20 pg/mL or >/=80 pg/mL had an increased risk for cancellation. Basal E(2) was predictive of stimulation parameters in patients 40 years or older. For those patients who proceeded to retrieval, there were no differences in pregnancy or delivery rates relative to basal E(2) levels. This suggests that irrespective of basal E(2) levels patients who produce more than three maturing follicles in response to stimulation have adequate ovarian reserve as evidenced by their pregnancy rates.

The reference range and potential value of inhibin B are still unclear and controversial. This study aimed to define the variation trend of inhibin B in healthy women with age and explore its value in the reflection of ovarian reserve.

This study aimed to explore the value of ovarian reserve tests (ORTs) for predicting poor ovary response (POR) and whether an age cutoff could improve this forecasting, so as to facilitate clinical decision-making for women undergoing in vitro fertilization (IVF).

The age-related decline of fertility is caused by a reduction of the ovarian reserve, which is represented by the number and quality of oocytes in the ovaries. Anti-Müllerian hormone (AMH) is considered one of the most useful markers of the quantity of the ovarian reserve; however, a more accurate prediction method is required. Furthermore, there is no clinically useful tool to assess the quality of the ovarian reserve and therefore a prediction tool is required. Our aim is to produce a model for prediction of the ovarian reserve that contributes to preconception care and precision medicine.This study was a retrospective analysis of 442 patients undergoing assisted reproductive technology (ART) treatment in Japan from June 2021 to January 2023. Medical records and residual serum of patients undergoing oocyte retrieval were collected. Binary classification models predicting the ovarian reserve were created using machine learning methods developed with many collected feature values. The best-performing model among 15 examined models was selected based on its area under the receiver operating characteristic curve (AUC) and accuracy. To maximize performance, feature values used for model creation were narrowed down and extracted.The best-performing model to assess the quantity of the ovarian reserve was the random forest model with an AUC of 0.9101. Five features were selected to create the model and consisted of data from only medical records. The best-performing model to assess the quality of the ovarian reserve was the random forest model, which had an AUC of 0.7983 and was created with 14 features, data from medical records and residual serum analysis.Our models are more accurate than currently popular methods for predicting the ovarian reserve. Furthermore, they can assess the ovarian reserve using only information obtained from a medical interview and single blood sampling. Enabling easy measurement of the ovarian reserve with this model would allow a greater number of women to engage in preconception care and facilitate the delivery of personalized medical treatment for patients undergoing infertility therapy.Not applicable.© 2025. The Author(s).

Individualizing follicle-stimulating hormone (FSH) dosing during controlled ovarian stimulation (COS) is critical for optimizing outcomes in assisted reproduction but remains difficult due to patient heterogeneity. Most existing models are limited to static predictions of initial doses and do not support real-time adjustments throughout stimulation.We developed a deep learning model that integrates cross-temporal and cross-feature encoding (CTFE) to predict personalized daily FSH doses in patients undergoing COS using the GnRH agonist long protocol. A total of 13,788 IVF/ICSI cycles conducted between January 2018 and December 2020 were retrospectively analyzed. Women with baseline antral follicle counts between 7 and 30 were included. Data were randomly divided into training (n = 6761), validation (n = 2898), and test (n = 4135) sets. The model encodes both static (e.g., age, BMI, basic hormone levels) and dynamic (e.g., follicle development, hormone trends during COS) variables across stimulation days. Final dose predictions were generated using a K-nearest neighbor algorithm applied to low-dimensional latent representations derived from the deep encoder layers.The CTFE model achieved a dose classification accuracy of 0.737 (± 0.004) and a weighted F1-score of 0.732 (± 0.005) on the test set. On key stimulation days 1 and 5, the CTFE model significantly outperformed traditional LASSO regression models (F1-score: 0.832 vs 0.699 on day 1; 0.817 vs 0.523 on day 5; p < 0.001). Prediction performance was maintained beyond day 13 using a sliding window mechanism, despite reduced data availability in longer stimulation cycles.This is the first study to apply a cross-temporal and cross-feature deep learning framework for daily, individualized FSH dose prediction across the full duration of COS. The model demonstrated superior performance over conventional approaches and offers a promising tool for standardizing COS management. Although currently limited by its retrospective, single-center design, the model may support future clinical decision-making and improve COS outcomes. Prospective, multicenter validation studies are warranted to confirm its utility and generalizability.© 2025. The Author(s).