Ovarian hyperstimulation syndrome is a serious complication associated with assisted reproductive technology. This systematic review aims to identify who is at high risk for developing ovarian hyperstimulation syndrome, along with evidence-based strategies to prevent it and replaces the document of the same name last published in 2016.Copyright © 2023 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With the large and complex datasets generated using precision medicine diagnostic approaches, novel techniques to process and understand these complex data were needed. At the same time, computer science has progressed rapidly to develop techniques that enable the storage, processing, and analysis of these complex datasets, a feat that traditional statistics and early computing technologies could not accomplish. Machine learning, a branch of artificial intelligence, is a computer science methodology that aims to identify complex patterns in data that can be used to make predictions or classifications on new unseen data or for advanced exploratory data analysis. Machine learning analysis of precision medicine's multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease. This review focuses on machine learning utilization for precision medicine's "big data", in the context of genetics, genomics, and beyond.

Spectacular advances have been made in personalized medicine, which has rapidly revolutionized our traditional understanding of disease diagnosis and treatment. Molecular testing of tissue and liquid samples using next generation sequencing has developed into a key technology in this scenario. It can be used for both the determination of biomarkers for diagnostic, prognostic and predictive purposes, as well as the possible improvement of treatment outcome through the use of targeted therapies and the avoidance of therapies in the event of special resistance situations. In addition to drugs that have already been approved, which among other things intervene in cellular DNA repair, many new drugs have been developed and are in clinical testing. Furthermore, new possibilities in molecular imaging have dramatically expanded our understanding of tumor spread and created new approaches for targeted therapies.© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases.

There is scanty information analysing the predictive value of a poor response, in terms of cancellation of the IVF cycle because of poor follicular development, as a predictor of ovarian response in a subsequent treatment cycle. This study, where logistic regression analysis was used, was undertaken to investigate the relative power of the woman's age, basal FSH, and previous cycle cancellation both as single and combined predictors of ovarian response in an IVF program where pituitary desensitization is routinely used.One hundred and twenty-nine consecutive patients having their first cycle of IVF/ICSI treatment cancelled because of poor follicular response and undergoing a second attempt within 6 months after the failed treatment cycle were initially selected (group 1). Group 2 comprised 129 patients undergoing the first cycle of IVF/ICSI treatment and who were randomly selected from our assisted reproductive treatment program matching by BMI and indication for IVF/ICSI to those in group 1.Cancellation rate was significantly higher but ovarian response significantly lower in group 1 as compared with group 2. As indicated by the AUC(ROC) determined with ROC analysis, such a poor outcome in patients having a previous IVF/ICSI cycle cancelled due to poor response was observed whatever the level of basal FSH. In a logistic regression analysis and according to the odds ratio values, the predictive capacity of a previous poor response was 9 and 7.6 times higher than the predictive capacity of age and basal FSH, respectively. Any two or all three variables studied did not improve the predictive value of previous cycle cancellation alone.The history of an IVF/ICSI cancelled cycle due to poor follicular response in a standard stimulation protocol is a better predictor of cancellation in subsequent treatment cycles than age or FSH. The poor ovarian response associated with previous cycle cancellation occurs whatever the level of basal FSH.

After an IVF cycle cancellation, does changing the stimulation protocol affect the odds of live birth and recurrent cancellation in the subsequent cycle?

To examine whether sequence variants within the FSHR and CYP19A1 genes are related to the ovarian response to controlled ovarian stimulation (COS).Genetic association study using both single-gene and combined analyses of women with sequence variants undergoing in vitro fertilization treatment.Academic research institute hospital.Seven hundred and five women undergoing ovarian stimulation with recombinant follicle-stimulating hormone (FSH).Peripheral blood extraction, DNA purification, and FSHR c.919G>A (rs6165, p.Thr307Ala) and CYP19A1 c.*19C>T (rs10046) sequence variants analyses.Single-gene statistical analysis and combined statistical analysis with the SPSS17.0 software; FSHR c.919G>A and CYP19A1 c.*19C>T sequence variant genotypes and clinical parameters related to the COS response as oocyte retrieval and hormone levels, doses of exogenous FSH.Women with genotype Ala/Ala at FSHR position 307 had higher basal levels of FSH and were more likely to have a low ovarian response compared with other genotypes. Women with genotype TT at CYP19A1 yielded fewer oocytes after ovarian stimulation. The combined analysis of these two sequence variants revealed that these two single-nucleotide variants have a synergistic effect in conferring the risk of a low ovarian response.Our results support an association of sequence variants in the genes that participate in estrogen synthesis, notably the FSHR and CYP19A1 genes, with the outcome of COS.Copyright © 2019 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Are there any associations between the variants of FSHR c.2039 G>A (p. Ser680Asn, rs6166) and LHCGR c.935A>G (p. Asn312Ser, rs2293275) and ovarian reserve, ovarian response, clinical pregnancy rate and POSEIDON group?A total of 210 infertile women were enrolled in this prospective study. The gene variants were analysed by the Sanger method. The clinical parameters were analysed based on genotypes.The frequency of heterozygous and homozygous G allele for FSHR c.2039 G>A in the low prognosis group was significantly higher than that in other response groups (P = 0.034); there was no significant association between LHCGR c.935 A>G and ovarian response. Moreover, the serum anti-Müllerian hormone (AMH) concentration, antral follicle count (AFC), oocytes retrieved, metaphase II (MII) oocytes and two-pronuclear (2PN) oocytes in patients with AG genotype for FSHR c.2039 G>A were significantly lower than those with AA genotype. The serum LH concentrations and clinical pregnancy rate of fresh embryo transfer in patients with GG genotype for LHCGR c.935 A>G were significantly higher than that of the AG genotype. In POSEIDON analysis, the low prognosis women with AA genotype for FSHR c.2039 G>A were more likely to appear in subgroup 1 (P = 0.038).The FSHR c.2039 G>A variant has a significant beneficial influence on ovarian reserve and ovarian response. The LHCGR c.935 A>G variant is associated with increased clinical pregnancy rate of fresh embryo transfer in infertile women. In addition, the low prognosis women with AA genotype for FSHR c.2039 G>A tend to show better ovarian reserve and prognosis.Copyright © 2021 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Cytochrome P450 2D6, 3A4 and 3A5 are involved in the metabolism of many drugs. These enzymes have a genetic polymorphism responsible for different metabolic phenotypes. They play a role in the metabolism of clomiphene citrate (CC), which is used to induce ovulation. Response to CC treatment is variable, and no predictive factors have thus far been identified.

To elucidate the impact of ovarian stimulation on the intrauterine milieu represented by the cytokine, chemokine, and growth factor profile in endometrial secretions aspirated before embryo transfer.Prospective cohort study.Fertility center in tertiary referral university hospital.Forty-two patients undergoing ovarian stimulation with GnRH analogues were recruited. They participated in both a natural and an ovarian-stimulated cycle for within patient comparisons.Endometrial secretion aspiration was performed immediately before embryo transfer.The concentrations of 17 mediators known to be involved in human embryo implantation were assessed by multiplex immunoassay.After correction for multiple testing, significantly higher concentrations of interleukin (IL)-1β, IL-5, IL-10, IL-12, IL-17, tumor necrosis factor (TNF)-α, heparin-binding epidermal growth factor (HbEGF), eotaxin, and dickkopf homologue-1 were present in endometrial secretions obtained in stimulated compared with natural cycles.Endometrial secretion analysis provides a novel means of investigating the effect of ovarian stimulation on the intrauterine milieu. The in vivo milieu encountered by the embryo after transfer is significantly altered by ovarian stimulation.Copyright © 2010 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Controlled ovarian stimulation (COS) is used to augment the number of retrieved oocytes in in vitro fertilization (IVF). Follicular fluid (FF) contributes significantly to oocyte quality. Since the FF is composed of follicular secretions and plasma exudation, it reflects alterations in granulosa and thecal cells secretion as well as changes in the level of plasma constituents. Phospholipids (PL) and free fatty acids (FFA) are important constituents of both, FF and serum. Our hypothesis is that COS affects the level of PL and FFA in serum. Furthermore, since the level of PL and FFA in FF partially depends on their levels in serum, as a collaterally of our hypothesis is that the existing level of PL and FFA in serum correlates with the levels of PL and FFA in FF, and that the dose of applied gonadotropins during COS will correlate with the levels of PL and FFA in serum and FF. In addition, we assume that the level of PL and FFA in serum and in FF after COS will correlate with the retrieved number of GQ oocytes, one of the most important outcomes of COS..Copyright © 2018 Elsevier Ltd. All rights reserved.

Serum estradiol (E2) level is routinely used to monitor the ovarian response during controlled ovarian hyperstimulation (COH) and the concentration of serum E2 may influence the oocyte quality and pregnancy outcome. However, the knowledge on the association between COH induced serum E2 level, oocyte quality and embryo development is limited. Therefore we investigated the association between serum E2 level, oxidative stress in the follicular fluid and granulosa cells (GCs) response to elucidate the association between E2 level and embryological outcome. In this study, patients (n = 30) undergoing ART were categorized as 'normal responders' (NR, n = 10), 'poor responders' (PR, n = 10) and hyper responders (HR, n = 10). The follicular fluid malondialdehyde (MDA) level was determined. The total RNA extracted from GCs was subjected to analyse the relative abundance of transcripts of stress response genes (P53, caspase 3,8-oxoguanine DNA glycosylase, OGG1 and heat shock protein 70; HSP70) and embryological outcome was noted. Follicular fluid MDA level was significantly higher in PR (p < 0.01) compared NR and HR whereas number of top-quality embryos were significantly lower in PR and HR compared to NR (p < 0.01). The relative expression of P53, HSP70, and OGG1 in GCs was significantly elevated in PR (p < 0.05-0.01). An inverse relationship was established between serum E2 level vs follicular MDA level (r = -0.45; p < 0.01) and follicular MDA level vs. number of top-quality embryos (r = -0.45; p < 0.01). Hence, patients with low serum E2 had elevated oxidative stress in their follicular environment and poor quality embryos implicating the risk of oxidative stress in patients with poor ovarian response.Copyright © 2020 Society for Biology of Reproduction & the Institute of Animal Reproduction and Food Research of Polish Academy of Sciences in Olsztyn. Published by Elsevier B.V. All rights reserved.

The mechanism by which Meishan (MS) sows are superior to white crossbred sows in ovarian follicle development remains unclear. Given gut microbiota could regulate female ovarian function and reproductive capacity, this study aimed to determine the role of gut microbiota-ovary axis on follicular development in sows.

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).

In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient’s response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation.