UNASSIGNED: Recurrent pregnancy loss (RPL) frequently links to a prolonged endometrial receptivity (ER) window, leading to the implantation of non-viable embryos. Existing ER assessment methods face challenges in reliability and invasiveness. Radiomics in medical imaging offers a non-invasive solution for ER analysis, but complex, non-linear radiomic-ER relationships in RPL require advanced analysis. Machine learning (ML) provides precision for interpreting these datasets, although research in integrating radiomics with ML for ER evaluation in RPL is limited.
UNASSIGNED: To develop and validate an ML model that employs radiomic features derived from multimodal transvaginal ultrasound images, focusing on improving ER evaluation in RPL.
UNASSIGNED: This retrospective, controlled study analyzed data from 346 unexplained RPL patients and 369 controls. The participants were divided into training and testing cohorts for model development and accuracy validation, respectively. Radiomic features derived from grayscale (GS) and shear wave elastography (SWE) images, obtained during the window of implantation, underwent a comprehensive five-step selection process. Five ML classifiers, each trained on either radiomic, clinical, or combined datasets, were trained for RPL risk stratification. The model demonstrating the highest performance in identifying RPL patients was selected for further validation using the testing cohort. The interpretability of this optimal model was augmented by applying Shapley additive explanations (SHAP) analysis.
UNASSIGNED: Analysis of the training cohort (242 RPL, 258 controls) identified nine key radiomic features associated with RPL risk. The extreme gradient boosting (XGBoost) model, combining radiomic and clinical data, demonstrated superior discriminatory ability. This was evidenced by its area under the curve (AUC) score of 0.871, outperforming other ML classifiers. Validation in the testing cohort of 215 subjects (104 RPL, 111 controls) confirmed its accuracy (AUC: 0.844) and consistency. SHAP analysis identified four endometrial SWE features and two GS features, along with clinical variables like age, SAPI, and VI, as key determinants in RPL risk stratification.
UNASSIGNED: Integrating ML with radiomics from multimodal endometrial ultrasound during the WOI effectively identifies RPL patients. The XGBoost model, merging radiomic and clinical data, offers a non-invasive, accurate method for RPL management, significantly enhancing diagnosis and treatment.

It has been recognized that oxidative stress (OS) is implicated in the etiology of recurrent pregnancy loss (RPL), yet the biomarkers reflecting oxidative stress in association with RPL remain scarce. The dataset GSE165004 was retrieved from the Gene Expression Omnibus (GEO) database. From the GeneCards database, a compendium of 789 genes related to oxidative stress-related genes (OSRGs) was compiled. By intersecting differentially expressed genes (DEGs) in normal and RPL samples with OSRGs, differentially expressed OSRGs (DE-OSRGs) were identified. In addition, four machine learning algorithms were employed for the selection of diagnostic markers for RPL. The Receiver Operating Characteristic (ROC) curves for these genes were generated and a predictive nomogram for the diagnostic markers was established. The functions and pathways associated with the diagnostic markers were elucidated, and the correlations between immune cells and diagnostic markers were examined. Potential therapeutics targeting the diagnostic markers were proposed based on data from the Comparative Toxicogenomics Database and ClinicalTrials.gov. The candidate biomarker genes from the four models were further validated in RPL tissue samples using RT-PCR and immunohistochemistry. A set of 20 DE-OSRGs was identified, with 4 genes (KRAS, C2orf69, CYP17A1, and UCP3) being recognized by machine learning algorithms as diagnostic markers exhibiting robust diagnostic capabilities. The nomogram constructed demonstrated favorable predictive accuracy. Pathways including ribosome, peroxisome, Parkinson\'s disease, oxidative phosphorylation, Huntington\'s disease, and Alzheimer\'s disease were co-enriched by KRAS, C2orf69, and CYP17A1. Cell chemotaxis terms were commonly enriched by all four diagnostic markers. Significant differences in the abundance of five cell types, namely eosinophils, monocytes, natural killer cells, regulatory T cells, and T follicular helper cells, were observed between normal and RPL samples. A total of 180 drugs were predicted to target the diagnostic markers, including C544151, D014635, and CYP17A1. In the validation cohort of RPL patients, the LASSO model demonstrated superiority over other models. The expression levels of KRAS, C2orf69, and CYP17A1 were significantly reduced in RPL, while UCP3 levels were elevated, indicating their suitability as molecular markers for RPL. Four oxidative stress-related diagnostic markers (KRAS, C2orf69, CYP17A1, and UCP3) have been proposed to diagnose and potentially treat RPL.

OBJECTIVE: Could the risk of subsequent pregnancy loss be predicted based on the risk factors of recurrent pregnancy loss (RPL) patients?
CONCLUSIONS: A nomogram, constructed from independent risk factors identified through multivariate logistic regression, serves as a reliable tool for predicting the likelihood of subsequent pregnancy loss in RPL patients.
BACKGROUND: Approximately 1-3% of fertile couples experience RPL, with over half lacking a clear etiological factor. Assessing the subsequent pregnancy loss rate in RPL patients and identifying high-risk groups for early intervention is essential for pregnancy counseling. Previous prediction models have mainly focused on unexplained RPL, incorporating baseline characteristics such as age and the number of previous pregnancy losses, with limited inclusion of laboratory and ultrasound indicators.
METHODS: The retrospective study involved 3387 RPL patients who initially sought treatment at the Reproductive Immunology Clinic of Renji Hospital, Shanghai Jiao Tong University School of Medicine, between 1 January 2020 and 31 December 2022. Of these, 1153 RPL patients met the inclusion criteria and were included in the analysis.
METHODS: RPL was defined as two or more pregnancy losses (including biochemical pregnancy loss) with the same partner before 28 weeks of gestation. Data encompassing basic demographics, laboratory indicators (autoantibodies, peripheral immunity coagulation, and endocrine factors), uterine and endometrial ultrasound results, and subsequent pregnancy outcomes were collected from enrolled patients through initial questionnaires, post-pregnancy visits fortnightly, medical data retrieval, and telephone follow-up for lost patients. R software was utilized for data cleaning, dividing the data into a training cohort (n = 808) and a validation cohort (n = 345) in a 7:3 ratio according to pregnancy success and pregnancy loss. Independent predictors were identified through multivariate logistic regression. A nomogram was developed, evaluated by 10-fold cross-validation, and compared with the model incorporating solely age and the number of previous pregnancy losses. The constructed nomogram was evaluated using the AUC, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA). Patients were then categorized into low- and high-risk subgroups.
RESULTS: We included age, number of previous pregnancy losses, lupus anticoagulant, anticardiolipin IgM, anti-phosphatidylserine/prothrombin complex IgM, anti-double-stranded DNA antibody, arachidonic acid-induced platelet aggregation, thrombin time and the sum of bilateral uterine artery systolic/diastolic ratios in the nomogram. The AUCs of the nomogram were 0.808 (95% CI: 0.770-0.846) in the training cohort and 0.731 (95% CI: 0.660-0.802) in the validation cohort, respectively. The 10-fold cross-validated AUC ranged from 0.714 to 0.925, with a mean AUC of 0.795 (95% CI: 0.750-0.839). The AUC of the nomogram was superior compared to the model incorporating solely age and the number of previous pregnancy losses. Calibration curves, DCAs, and CICAs showed good concordance and clinical applicability. Significant differences in pregnancy loss rates were observed between the low- and high-risk groups (P < 0.001).
CONCLUSIONS: This study was retrospective and focused on patients from a single reproductive immunology clinic, lacking external validation data. The potential impact of embryonic chromosomal abnormalities on pregnancy loss could not be excluded, and the administration of medication to all cases impacted the investigation of risk factors for pregnancy loss and the model\'s predictive efficacy.
CONCLUSIONS: This study signifies a pioneering effort in developing and validating a risk prediction nomogram for subsequent pregnancy loss in RPL patients to effectively stratify their risk. We have integrated the nomogram into an online web tool for clinical applications.
BACKGROUND: This study was supported by the National Natural Science Foundation of China (82071725). All authors have no competing interests to declare.
BACKGROUND: N/A.

UNASSIGNED: Unexplained recurrent pregnancy loss (URPL) is a clinical dilemma in reproductive fields. Its diagnosis is mainly exclusionary after extensive clinical examination, and some of the patients may still face the risk of miscarriage.
UNASSIGNED: We analyzed follicular fluid (FF) from in vitro fertilization (IVF) in eight patients with URPL without endocrine abnormalities or verifiable causes of abortion and eight secondary infertility controls with no history of pregnancy loss who had experienced at least one normal pregnancy and delivery by direct data-independent acquisition (dDIA) quantitative proteomics to identify differentially expressed proteins (DEPs). In this study, bioinformatics analysis was performed using online software including g:profiler, String, and ToppGene. Cytoscape was used to construct the protein-protein interaction (PPI) network, and ELISA was used for validation.
UNASSIGNED: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the DEPs are involved in the biological processes (BP) of complement and coagulation cascades. Apolipoproteins (APOs) are key proteins in the PPI network. ELISA confirmed that APOB was low-expressed in both the FF and peripheral blood of URPL patients.
UNASSIGNED: Dysregulation of the immune network intersecting coagulation and inflammatory response is an essential feature of URPL, and this disequilibrium exists as early as the oogenesis stage. Therefore, earlier intervention is necessary to prevent the development of URPL. Moreover, aberrant lipoprotein regulation appears to be a key factor contributing to URPL. The mechanism by which these factors are involved in the complement and coagulation cascade pathways remains to be further investigated, which also provides new candidate targets for URPL treatment.

UNASSIGNED: It has been proven that immune disorders are one of the vital risk factors of recurrent pregnancy loss (RPL), and the presence of food intolerance seems to play an essential role in this. However, the impact of immune status induced by food intolerance on RPL has not been reported. This study utilized a targeted diet avoiding food intolerance as much as possible for each participant to investigate their effects on pregnancy outcomes in RPL patients with positive autoimmune markers.
UNASSIGNED: From January 2020 to May 2021, fifty-eight patients with RPL were enrolled. They were divided into two groups based on the presence of autoantibodies: the autoantibody-positive group (AP, n = 29) and the autoantibody-negative group (AN, n = 29). Their food-specific immunoglobulin (Ig) G antibodies for 90 foods were tested using enzyme-linked immunosorbent assay (ELISA). The levels of immune parameters and the presence of gastrointestinal discomforts (diarrhea or constipation, eczema, and mouth ulcers) were recorded before and after dietary conditioning, followed by the analysis of pregnancy outcomes.
UNASSIGNED: Compared to the AN group, the patients in the AP group showed immune disorders at baseline, such as reduced levels of IL-4 and complement C3, and increased levels of IL-2 and total B cells. These parameters within the AP group were significantly improved after dietary conditioning that avoided food intolerance, while no significant changes were observed in the AN group. Patients in the AP group had significantly higher food-specific IgG antibodies for cow\'s milk (89.66% vs. 48.28%, p < .001), yolk (86.21% vs. 27.59%, p < .001), bamboo shoots (86.21% vs. 44.83%, p < .001) compared to those in the AN group. Additionally, gastrointestinal discomforts including diarrhea or constipation, eczema, and mouth ulcers were more common in the AP group than in the AN group. After 3-month dietary conditioning, these significantly improved characteristics were only observed in the AP group (p < .001). Finally, the baby-holding rate was higher in the AP group compared to the AN group (p < .05).
UNASSIGNED: The RPL patients in the AN group did not exhibit immune disorders, whereas those in the AP group experienced immune disorders and gastrointestinal discomforts. For patient with positive autoantibodies, dietary intervention may mitigate immune disorders and gastrointestinal discomforts, presenting a promising approach to enhance pregnancy outcomes.

暂无摘要。

Establishing prediction models of pregnancy outcomes for recurrent pregnancy loss women at specific gestational weeks will provide patients and physicians with more precise information, ultimately leading to time and cost savings associated with unnecessary revisits. Therefore, our aim was to develop a prediction model for first trimester pregnancy loss in RPL patients. We used ultrasound indices during the first trimester of pregnancy in combination with demographic characteristics and commonly used serum markers. The independent risk factors for each week were as follows: age and P in the fifth week; age, mGSD and CRL in the sixth week; age, hCG and CRL in the seventh week; CRL in the eighth week; mGSD and CRL in ninth week. The corresponding AUC was 0.671, 0.796, 0.872, 0.871, 0.813, respectively. There is a linear relationship between age and first trimester pregnancy loss. hCG < 69,636.6 mIU/ml was associated with a higher risk of pregnancy loss in the seventh gestation week. An mGSD < 18.3 mm, adjusted for age, BMI, and previous pregnancy loss in the sixth week, was linked to an increased risk of first trimester pregnancy loss. A small CRL measurement (less than 2.4 mm, 9.9 mm, 16.9 mm, and 18.6 mm) in the sixth, seventh, eighth and ninth week was closely correlated with higher risk of first trimester pregnancy loss. Furthermore, an mGSD < 33.3 mm and > 48.3 mm in ninth gestational week was associated with a higher risk of pregnancy loss. These models and thresholds may help physicians and patients make more informed decisions together. Further studies are needed to confirm the results.

[This corrects the article DOI: 10.3389/fendo.2024.1415786.].

暂无摘要。

Decidual natural killer (dNK) cells are the most abundant immune cells at the maternal-fetal interface during early pregnancy in both mice and humans, and emerging single-cell transcriptomic studies have uncovered various human dNK subsets that are disrupted in patients experiencing recurrent early pregnancy loss (RPL) at early gestational stage, suggesting a connection between abnormal proportions or characteristics of dNK subsets and RPL pathogenesis. However, the functional mechanisms underlying this association remain unclear. Here, we established a mouse model by adoptively transferring human dNK cells into pregnant NOG (NOD/Shi-scid/IL-2Rγnull) mice, where human dNK cells predominantly homed into the uteri of recipients. Using this model, we observed a strong correlation between the properties of human dNK cells and pregnancy outcome. The transfer of dNK cells from RPL patients (dNK-RPL) remarkably worsened early pregnancy loss and impaired placental trophoblast cell differentiation in the recipients. These adverse effects were effectively reversed by transferring CD56+CD39+ dNK cells. Mechanistic studies revealed that CD56+CD39+ dNK subset facilitates early differentiation of mouse trophoblast stem cells (mTSCs) towards both invasive and syncytial pathways through secreting macrophage colony-stimulating factor (M-CSF). Administration of recombinant M-CSF to NOG mice transferred with dNK-RPL efficiently rescued the exacerbated pregnancy outcomes and fetal/placental development. Collectively, this study established a novel humanized mouse model featuring functional human dNK cells homing into the uteri of recipients and uncovered the pivotal role of M-CSF in fetal-supporting function of CD56+CD39+ dNK cells during early pregnancy, highlighting that M-CSF may be a previously unappreciated therapeutic target for intervening RPL.