Appropriate ovarian responses to the controlled ovarian stimulation strategy is the premise for a good outcome of the in vitro fertilization cycle. With the booming of artificial intelligence, machine learning is becoming a popular and promising approach for tailoring a controlled ovarian stimulation strategy. Nowadays, most machine learning-based tailoring strategies aim to generally classify the controlled ovarian stimulation outcome, lacking the capacity to precisely predict the outcome and evaluate the impact features. Based on a clinical cohort composed of 1365 women and two machine learning methods of artificial neural network and supporting vector regression, a regression prediction model of the number of oocytes retrieved is trained, validated, and selected. Given the proposed model, an index called the normalized mean impact value is defined and calculated to reflect the importance of each impact feature. The proposed models can estimate the number of oocytes retrieved with high precision, with the regression coefficient being 0.882% and 89.84% of the instances having the prediction number ≤ 5. Among the impact features, the antral follicle count has the highest importance, followed by the E₂ level on the human chorionic gonadotropin day, the age, and the Anti-Müllerian hormone, with their normalized mean impact value > 0.3. Based on the proposed model, the prognostic results for ovarian response can be predicted, which enables scientific clinical decision support for the customized controlled ovarian stimulation strategies for women, and eventually helps yield better in vitro fertilization outcomes.

MITA (also called STING), a master regulator of DNA-mediated innate immune activation, is a potential therapeutic target for viral infection and virus-related diseases. The circRNA-mediated ceRNA network plays important roles in gene regulation and may contribute to many human diseases. However, the relationship between MITA and recurrent miscarriage (RM) and its circRNA-related regulatory mechanisms remain unclear. In this study, we validated that the decidual M1/M2 ratio was upregulated in RM patients, suggesting the vital roles of decidual macrophages in the pathogenesis of RM. We found that MITA was highly expressed in decidual macrophages of RM patients and validated that MITA could promote apoptosis and macrophage proinflammatory polarization in THP-1-derived macrophage (TDM) cells. Using circRNA sequencing and bioinformatic analysis, we screened out a novel circRNA (circKIAA0391) that is overexpressed in decidual macrophages of RM patients. Mechanistically, we found that circKIAA0391 could promote the apoptosis and proinflammatory polarization of TDM cells by sponging the miR-512-5p/MITA axis. This study provides a theoretical basis for further understanding the impact of MITA on macrophages and its circRNA-related regulatory mechanisms, which may have a crucial immunomodulatory function in the pathophysiology of RM.

The maternal-fetal interface shares similarities with tumor tissues in terms of the immune microenvironment. Normal pregnancy is maintained due to the immunosuppressed state, but pyroptosis induced by MITA can trigger the body’s immune response and disrupt the immunosuppressed state of the maternal-fetal interface, leading to abortion. In this study, we explored the role of MITA and TRIM38 in regulating pyroptosis and maintaining the immune tolerance of the maternal-fetal interface during pregnancy. Our findings show that the interaction between MITA and TRIM38 plays a crucial role in maintaining the immunosuppressed state of the maternal-fetal interface. Specifically, we observed that TRIM38-mediated K48 ubiquitination of MITA was higher in M2 macrophages, leading to low expression levels of MITA and thus inhibiting pyroptosis. Conversely, in M1 macrophages, the ubiquitination of K48 was lower, resulting in higher expression levels of MITA and promoting pyroptosis. Our results also indicated that pyroptosis played an important role in hindering the transformation of M1 to M2 and maintaining the immunosuppressed state of the maternal-fetal interface. These discoveries help elucidate the mechanisms that support the preservation of the immune tolerance microenvironment at the maternal-fetal interface, playing a vital role in ensuring successful pregnancy.

Background: This study aimed to examine the differential variations in the metabolic composition of follicular fluid (FF) among normal-weight patients with polycystic ovary syndrome (PCOS) and controls and to identify potential biomarkers that may offer insights into the early identification and management of these patients. Methods: We collected FF samples from 45 normal-weight women with PCOS and 36 normal-weight controls without PCOS who were undergoing in vitro fertilization–embryo transfer. An untargeted metabolomic study of collected FF from infertile women was performed using high-performance liquid chromatography–tandem spectrometry (LC-MS). The tendency of the two groups to separate was demonstrated through multivariate analysis. Univariate analysis and variable importance in projection were used to screen out differential metabolites. Metabolic pathway analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG), and a diagnostic model was established using the random forest algorithm. Results: The metabolomics analysis revealed an increase in the expression of 23 metabolites and a decrease in that of 10 metabolites in the FF of normal-weight women with PCOS. According to the KEGG pathway analysis, these differential metabolites primarily participated in the metabolism of glycerophospholipids and the biosynthesis of steroid hormones. Based on the biomarker combination of the top 10 metabolites, the area under the curve value was 0.805. The concentrations of prostaglandin E2 in the FF of individuals with PCOS exhibited an inverse association with the proportion of high-quality embryos (p < 0.05). Conclusions: Our research identified a distinct metabolic profile of the FF from normal-weight women with PCOS. The results offer a broader comprehension of the pathogenesis and advancement of PCOS, and the detected differential metabolites could be potential biomarkers and targets for the treatment of PCOS.