Medium- and High-Entropy Rare Earth Hexaborides with Enhanced Solar Energy Absorption and Infrared Emissivity.

The development of a new generation of solid particle solar receivers (SPSRs) with high solar absorptivity (0.28-2.5 µm) and high infrared emissivity (1-22 µm) is crucial and has attracted much attention for the attainment of the goals of "peak carbon" and "carbon neutrality". To achieve the modulation of infrared emission and solar absorptivity, two types of medium- and high-entropy rare-earth hexaboride (ME/HEREB6) ceramics, (La0.25Sm0.25Ce0.25Eu0.25)B6 (MEREB6) and (La0.2Sm0.2Ce0.2Eu0.2Ba0.2)B6 (HEREB6), with severe lattice distortions were synthesized using a high-temperature solid-phase method. Compared to single-phase lanthanum hexaboride (LaB6), HEREB6 ceramics show an increase in solar absorptivity from 54.06% to 87.75% in the range of 0.28-2.5 µm and an increase in infrared emissivity from 76.19% to 89.96% in the 1-22 µm wavelength range. On the one hand, decreasing the free electron concentration and the plasma frequency reduces the reflection and ultimately increases the solar absorptivity. On the other hand, the lattice distortion induces changes in the B-B bond length, leading to significant changes in the Raman scattering spectrum, which affects the damping constant and ultimately increases the infrared emissivity. In conclusion, the multicomponent design can effectively improve the solar energy absorption and heat transfer capacity of ME/HEREB6, thus providing a new avenue for the development of solid particles.

An XGBoost predictive model of ongoing pregnancy in patients following hysteroscopic adhesiolysis.

RESEARCH QUESTION: What are the factors influencing the fertility of patients with intrauterine adhesions (IUA) after hysteroscopic adhesiolysis and which assessment system is more efficient in predicting post-operative ongoing pregnancy? DESIGN: The clinical information of 369 individuals diagnosed with and treated for IUA were obtained from the Multicentre Prospective Clinical Database for the Construction of Predictive Models on Risk of Intrauterine Adhesion (NCT05381376) and randomly divided into the training and validation cohorts. A univariate analysis was performed to identify relevant clinical indicators, followed by a least absolute shrinkage and selection operator (LASSO) regression for regularization and SHapley Additive exPlanation (SHAP) for extreme gradient boosting (XGBoost) predictive model visualization. Finally, receiver operating characteristic (ROC) curves were constructed to assess the model's efficiency. RESULTS: Univariate analysis and LASSO regression demonstrated that 12 clinical indicators were significantly associated with post-operative ongoing pregnancy in IUA patients. SHAP visualization indicated that post-operative Fallopian tube ostia, blood supply, uterine cavity shape and age had the highest significance. The area under the ROC curve (AUC) of the XGBoost model in the training and validation cohorts was 0.987 (95% CI 0.979-0.996) and 0.985 (95% CI 0.967-1), respectively. These values were significantly higher than those of the American Fertility Society (AFS) classification, the Chinese Society for Gynecological Endoscopy (CSGE) classification and endometrial thickness (all P < 0.001). CONCLUSIONS: The XGBoost model had higher accuracy in predicting post-operative reproductive outcomes in IUA patients. Clinically, the model may be useful for managing and categorizing IUA and determining optimal action to aid in pregnancy.

RNA-seq reveals co-dysregulated circular RNAs in the adenomyosis eutopic endometrium and endometrial-myometrial interface.

BACKGROUND: Uterine adenomyosis is associated with chronic pelvic pain, abnormal uterine bleeding, and infertility. The pathogenesis of adenomyosis is still unclear. Circular RNAs (circRNAs) have been implicated in several benign diseases and malignant tumors. We aimed to explore the co-dysregulated circular RNA profile in the eutopic endometrium and endometrial-myometrial interface (EMI) of adenomyosis. METHODS: Total RNA was extracted from the eutopic endometrium and EMI of 5 patients with adenomyosis and 3 patients without adenomyosis. Next-generation sequencing was performed to identify the circRNA expression profile of the two tissue types. Bioinformatics analysis was performed to predict circRNA-binding miRNAs and miRNA-binding mRNAs and construct ceRNA networks, and functional enrichment analysis was performed to predict the biological functions of circRNAs. RESULTS: Among the adenomyosis patients, 760 circRNAs were significantly upregulated and 119 circRNAs were significantly downregulated in the EMI of adenomyosis, while 47 circRNAs were significantly upregulated and 17 circRNAs were significantly downregulated in the eutopic endometrium of adenomyosis. We identified hsa_circ_0002144 and hsa_circ_0005806 as co-upregulated and hsa_circ_0079536 and hsa_circ_0024766 as co-downregulated in the eutopic endometrium and EMI. Bioinformatics analysis was performed to construct a ceRNA network of codifferentially expressed circRNAs. The MAPK signaling pathway is the most important signaling pathway involved in the function of the ceRNA network. CONCLUSIONS: Co-dysregulated circRNAs were present in the eutopic endometrium and EMI of adenomyosis. MiRNA binding sites were observed for all of these circRNAs and found to regulate gene expression. Co-dysregulated circRNAs may induce the eutopic endometrial invagination process through the MAPK signaling pathway and promote the progression of adenomyosis.

Gradient Boosting Machine Learning Model for Defective Endometrial Receptivity Prediction by Macrophage-Endometrium Interaction Modules.

Background: A receptive endometrium is a prerequisite for successful embryo implantation. Mounting evidence shows that nearly one-third of infertility and implantation failures are caused by defective endometrial receptivity. This study pooled 218 subjects from multiple datasets to investigate the association of the immune infiltration level with reproductive outcome. Additionally, macrophage-endometrium interaction modules were constructed to explore an accurate and cost-effective approach to endometrial receptivity assessment. Methods: Immune-infiltration levels in 4 GEO datasets (n=218) were analyzed and validated through meta-analysis. Macrophage-endometrium interaction modules were selected based on the weighted gene co-expression network in GSE58144 and differentially expressed genes dominated by GSE19834 dataset. Xgboost, random forests, and regression algorithms were applied to predictive models. Subsequently, the efficacy of the models was compared and validated in the GSE165004 dataset. Forty clinical samples (RT-PCR and western blot) were performed for expression and model validation, and the results were compared to those of endometrial thickness in clinical pregnancy assessment. Results: Altered levels of Mϕs infiltration were shown to critically influence embryo implantation. The three selected modules, manifested as macrophage-endometrium interactions, were enrichment in the immunoreactivity, decidualization, and signaling functions and pathways. Moreover, hub genes within the modules exerted significant reproductive prognostic effects. The xgboost algorithm showed the best performance among the machine learning models, with AUCs of 0.998 (95% CI 0.994-1) and 0.993 (95% CI 0.979-1) in GSE58144 and GSE165004 datasets, respectively. These results were significantly superior to those of the other two models (random forest and regression). Similarly, the model was significantly superior to ultrasonography (endometrial thickness) with a better cost-benefit ratio in the population. Conclusion: Successful embryo implantation is associated with infiltration levels of Mϕs, manifested in genetic modules involved in macrophage-endometrium interactions. Therefore, utilizing the hub genes in these modules can provide a platform for establishing excellent machine learning models to predict reproductive outcomes in patients with defective endometrial receptivity.

Establishment of an Artificial Neural Network Model Using Immune-Infiltration Related Factors for Endometrial Receptivity Assessment.

BACKGROUND: A comprehensive clinical strategy for infertility involves treatment and, more importantly, post-treatment evaluation. As a component of assessment, endometrial receptivity does not have a validated tool. This study was anchored on immune factors, which are critical factors affecting embryonic implantation. We aimed at establishing novel approaches for assessing endometrial receptivity to guide clinical practice. METHODS: Immune-infiltration levels in the GSE58144 dataset (n = 115) from GEO were analysed by digital deconvolution and validated by immunofluorescence (n = 23). Then, modules that were most associated with M1/M2 macrophages and their hub genes were selected by weighted gene co-expression network as well as univariate analyses and validated using the GSE5099 macrophage dataset and qPCR analysis (n = 19). Finally, the artificial neural network model was established from hub genes and its predictive efficacy validated using the GSE165004 dataset (n = 72). RESULTS: Dysregulation of M1 to M2 macrophage ratio is an important factor contributing to defective endometrial receptivity. M1/M2 related gene modules were enriched in three biological processes in macrophages: antigen presentation, interleukin-1-mediated signalling pathway, and phagosome acidification. Their hub genes were significantly altered in patients and associated with ribosomal, lysosomal, and proteasomal pathways. The established model exhibited an excellent predictive value in both datasets, with an accuracy of 98.3% and an AUC of 0.975 (95% CI 0.945-1). CONCLUSIONS: M1/M2 polarization influences endometrial receptivity by regulating three gene modules, while the established ANN model can be used to effectively assess endometrial receptivity to inform pregnancy and individualized clinical management strategies.

Hierarchical cluster analysis in the study of the effect of cytokine expression patterns on endometrial repair and receptivity after hysteroscopic adhesiolysis.

BACKGROUND: In a previous study, we reported that amnion promotes endometrial cell growth by regulating cytokines. In this study, hierarchical cluster analysis enabled the evaluation of cytokine expression changes after amnion treatment to be explored by cluster patterns. The role of IL1B on endometrial repair and receptivity was revealed. METHODS: A total of 30 patients were recruited in this clinical trial (NCT02496052) of hysteroscopic adhesiolysis. They were randomly allocated into an amnion grafts group (amnion group) and a control group. After hysteroscopic adhesiolysis, a Foley catheter covered with a sterilized freeze-dried amnion graft was inserted into the uterine cavity of the participants in the amnion group, whereas for the control group, a Foley catheter without amnion graft was inserted. After surgery, patient follow-up was done for a year. Uterine exudates were collected every day for seven days after surgery, and analyzed by enzyme-linked immunosorbent assays. Hierarchical cluster analysis was performed to compare expression patterns of each cytokine. Single-gene gene set enrichment analysis and differentially expressed genes enrichment analysis of IL1B were performed using NCBI GEO (N=151) to evaluate its potential mechanisms and impact on endometrial receptivity. RESULTS: Compared to the control group, cytokine expression patterns of the amnion group revealed significant stratifications, which were highly correlated with the expression levels of IL1B on the sixth to seventh day after surgery, improving the pregnant rate. Wilcoxon test revealed significantly low expression levels of IL1B in the reduced endometrial receptivity group compared to the normal group. Moreover, gene set enrichment analysis showed that lysosomes, cell cycle, and calcium signaling pathways were associated with the biological processes in which IL1B plays a role. Screening and enrichment analyses of differentially expressed genes further verified the mechanisms of action of IL1B on endometrial repair and receptivity recovery. CONCLUSIONS: Amnion promotes endometrial repair and receptivity by altering the expression levels and patterns of IL1B. Furthermore, by affecting lysosomal, cell cycle, and calcium signaling pathways, IL1B may be one of the factors involved in endometrial repair and receptivity recovery.

Ferroptosis resistance mechanisms in endometriosis for diagnostic model establishment.

RESEARCH QUESTION: Does ferroptosis resistance occur in patients with endometriosis, and can it be used as an indicator for diagnosis? DESIGN: Six datasets including 164 expression arrays from endometriosis studies were selected from the National Center for Biotechnology Information Gene Expression Omnibus. Surrogate variable analysis was used for data integration after RobustRankAggreg to determine ferroptosis-associated gene expression trends. Differential genes between eutopic and normal endometrium, as well as between ectopic and eutopic endometrium, were determined. Ferroptosis resistance mechanisms during the development of endometriosis were determined by intergenic co-expression and the Kyoto Encyclopedia of Genes and Genomes pathway. Independent factors were then screened by least absolute shrinkage and selection operator regression to build a nomogram diagnostic model. The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve were used to verify model consistency and diagnostic efficacy, respectively. RESULTS: Differential analysis revealed a progressive increase in changes of ferroptosis-related genes in endometriosis patients during the process of endometrium development from eutopic to ectopic. In the subsequent regression selection, nine ferroptosis-associated genes in the eutopic endometrium could, in most cases, distinguish endometriosis patients from healthy patients. Therefore, these genes can be used as independent factors for endometriosis diagnosis. The nomogram model established by these molecular markers had an area under the ROC curve of 0.979. CONCLUSIONS: The eutopic and ectopic endometrium of patients with endometriosis is associated with ferroptosis resistance genetic alterations. Ferroptosis-associated genes have excellent clinical value in endometriosis diagnosis, achieved by the nomogram model.