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Objective: To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms. Methods: A retrospective analysis was conducted on the clinical data of 4,216 POR cycles who underwent in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) at Sichuan Jinxin Xinan Women and Children's Hospital from January 2015 to December 2021. Based on the presence of high-quality cleavage embryos 72 h post-fertilization, the samples were divided into the high-quality cleavage embryo group (N = 1950) and the non-high-quality cleavage embryo group (N = 2266). Additionally, based on whether high-quality blastocysts were observed following full blastocyst culture, the samples were categorized into the high-quality blastocyst group (N = 124) and the non-high-quality blastocyst group (N = 1800). The factors influencing the formation of high-quality embryos were analyzed using logistic regression. The predictive models based on machine learning methods were constructed and evaluated accordingly. Results: Differential analysis revealed that there are statistically significant differences in 14 factors between high-quality and non-high-quality cleavage embryos. Logistic regression analysis identified 14 factors as influential in forming high-quality cleavage embryos. In models excluding three variables (retrieved oocytes, MII oocytes, and 2PN fertilized oocytes), the XGBoost model performed slightly better (AUC = 0.672, 95% CI = 0.636–0.708). Conversely, in models including these three variables, the Random Forest model exhibited the best performance (AUC = 0.788, 95% CI = 0.759–0.818). In the analysis of high-quality blastocysts, significant differences were found in 17 factors. Logistic regression analysis indicated that 13 factors influence the formation of high-quality blastocysts. Including these variables in the predictive model, the XGBoost model showed the highest performance (AUC = 0.813, 95% CI = 0.741–0.884). Conclusion: We developed a predictive model for the formation of high-quality embryos using machine learning methods for patients with POR undergoing treatment with the PPOS protocol. This model can help infertility patients better understand the likelihood of forming high-quality embryos following treatment and help clinicians better understand and predict treatment outcomes, thus facilitating more targeted and effective interventions. [ABSTRACT FROM AUTHOR]

Ischemic heart disease, especially myocardial infarction (MI), is one of the leading causes of death worldwide, and desperately needs effective treatments, such as cell therapy. Cardiopulmonary progenitors (CPPs) are stem cells for both heart and lung, but their repairing role in damaged heart is still unknown. Here, we obtained CPPs from E9.5 mouse embryos, maintained their stemness while expanding, and identified their characteristics by scRNA‐seq, flow cytometry, quantitative reverse transcription‐polymerase chain reaction, and differentiation assays. Moreover, we employed mouse MI model to investigate whether CPPs could repair the injured heart. Our data identified that CPPs exhibit hybrid fibroblastic, endothelial, and mesenchymal state, and they could differentiate into cell lineages within the cardiopulmonary system. Moreover, intramyocardial injection of CPPs improves cardiac function through CPPs exosomes (CPPs‐Exo) by promotion of cardiomyocytic proliferation and vascularization. To uncover the underlying mechanism, we used miRNA‐seq, bulk RNA‐seq, and bioinformatic approaches, and found the highly expressed miR‐27b‐3p in CPPs‐Exo and its target gene Sik1, which can influence the transcriptional activity of CREB1. Therefore, we postulate that CPPs facilitate cardiac repair partially through the SIK1‐CREB1 axis via exosomal miR‐27b‐3p. Our study offers a novel insight into the role of CPPs‐Exo in heart repair and highlights the potential of CPPs‐Exo as a promising therapeutic strategy for MI. [ABSTRACT FROM AUTHOR]

Gold exists in the form of micro-super micro dispersed states in Carlin-type gold ores, which is in the form of rich organic matter. The gold occurrence state cannot be analyzed accurately or at best, can be roughly estimated by using the Mineral Liberation Analyzer (MLA) and the traditional chemistry method. In this study, carlin-type gold ore in Guizhou Huilong was analyzed systematically using MLA, and the results show that micro-super micro gold was mainly embedded in pyrite and arsenopyrite, and less was embedded in gangue minerals and other minerals. The separation degree of pyrite and arsenopyrite were 66% and 30% in minerals, respectively. As gold-bearing minerals are attached or embedded in other minerals, the separation of gold-bearing sulfides (GBS) is more difficult. The leaching orders regarding free gold (FG), gold-bearing carbonates (GBC), gold-bearing sulfides (GBS) and silicates (GBSi) were sequenced on the basis of the tradition method, gold occurrence state and high content of organic matter characteristics of Guizhou Huilong gold ore. Furthermore, according to the high content of organic matter in this deposit, the activated carbon was added in the process of leaching FG and GBC to restrain the adsorption to gold by organic carbon. The comparative studies between traditional methods (TM) and modified methods (MM) show that there is only a small effect on the detected value after adding activated carbon into the gold ores which contain about 1% of organic carbon. In MM, gold-bearing carbonates were leached prior to gold-bearing sulfides; results of every phase data were more accurate than those of TM. Along with the addition of activated carbon in MM, the contents of FG, GBS, GBC, GBSi in Guizhou Huilong gold ore were 1.25%, 84.17%, 11.46% and 3.13%, respectively, which was consistent with the results of processing experiments. This indicates that MM can be applied to analyze precisely the occurrence states of gold in carlin-type gold ores.