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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]

Cardiopulmonary progenitor cells (CPPs) constitute a minor subpopulation of cells that are commonly associated with heart and lung morphogenesis during embryonic development but completely subside after birth. This fact offers the possibility for the treatment of pulmonary heart disease (PHD), in which the lung and heart are both damaged. A reliable source of CPPs is urgently needed. In this study, we reprogrammed human cardiac fibroblasts (HCFs) into CPP-like cells (or induced CPPs, iCPPs) and evaluated the therapeutic potential of iCPP-derived exosomes for acute lung injury (ALI). iCPPs were created in passage 3 primary HCFs by overexpressing GLI1, WNT2, ISL1 and TBX5 (GWIT). Exosomes were isolated from the culture medium of passage 6-8 GWIT-iCPPs. A mouse ALI model was established by intratracheal instillation of LPS. Four hours after LPS instillation, ALI mice were treated with GWIT-iCPP-derived exosomes (5 × 10 9 , 5 × 10 10 particles/mL) via intratracheal instillation. We showed that GWIT-iCPPs could differentiate into cell lineages, such as cardiomyocyte-like cells, endothelial cells, smooth muscle cells and alveolar epithelial cells, in vitro. Transcription analysis revealed that GWIT-iCPPs have potential for heart and lung development. Intratracheal instillation of iCPP-derived exosomes dose-dependently alleviated LPS-induced ALI in mice by attenuating lung inflammation, promoting endothelial function and restoring capillary endothelial cells and the epithelial cells barrier. This study provides a potential new method for the prevention and treatment of cardiopulmonary injury, especially lung injury, and provides a new cell model for drug screening., (© 2024. The Author(s), under exclusive licence to Shanghai Institute of Materia Medica, Chinese Academy of Sciences and Chinese Pharmacological Society.)