miR-181a increases FoxO1 acetylation and promotes granulosa cell apoptosis via SIRT1 downregulation.

Oxidative stress impairs follicular development by inducing granulosa cell (GC) apoptosis, which involves enhancement of the transcriptional activity of the pro-apoptotic factor Forkhead box O1 (FoxO1). However, the mechanism by which oxidative stress promotes FoxO1 activity is still unclear. Here, we found that miR-181a was upregulated in hydrogen peroxide (

Meta-learning based infrared ship object detection model for generalization to unknown domains

Infrared images exhibit considerable variations in probability distributions, stemming from the utilization of distinct infrared sensors and the influence of diverse environmental conditions. The variations pose great challenges for deep learning models to detect ship objects and adapt to unseen maritime environments. To address the domain shift problem, we propose an end-to-end infrared ship object detection model based on meta-learning neural network to improve domain adaptation for target domain where data is not available at training phase. Different from existing domain generalization methods, the novelty of our model lies in the effective exploitation of meta-learning and domain adaptation, ensuring that the extracted domain-independent features are meaningful and domain-invariant at the semantic level. Firstly, a double gradient-based meta-learning algorithm is designed to solve the common optimal descent direction between different domains through two gradient updates in the inner and outer loops. The algorithm enables extraction of domain-invariant features from the pseudo-source and pseudo-target domain data. Secondly, a domain discriminator with dynamic-weighted gradient reversal layer (DWGRL) is designed to accurately classify domain-invariant features and provide additional global supervision information. Finally, a multi-scale feature aggregation method is proposed to improve the extraction of multi-scale domain-invariant features. It can effectively fuse local features at different scales and global features of targets. Extensive experimental results conducted in real nighttime water surface scenes demonstrate that the proposed model achieves very high detection accuracy on target domain data, even no target domain data was used during the training phase. Compared to the existing methods, our method not only improves the detection accuracy of infrared ships by 18%, but also exhibits the smallest standard deviation with a value of 0.93, indicating its superior generalization performance

Different Angiogenic Potentials of Mesenchymal Stem Cells Derived from Umbilical Artery, Umbilical Vein, and Wharton's Jelly

Human mesenchymal stem cells derived from the umbilical cord (UC) are a favorable source for allogeneic cell therapy. Here, we successfully isolated the stem cells derived from three different compartments of the human UC, including perivascular stem cells derived from umbilical arteries (UCA-PSCs), perivascular stem cells derived from umbilical vein (UCV-PSCs), and mesenchymal stem cells derived from Wharton’s jelly (WJ-MSCs). These cells had the similar phenotype and differentiation potential toward adipocytes, osteoblasts, and neuron-like cells. However, UCA-PSCs and UCV-PSCs had more CD146+ cells than WJ-MSCs (P<0.05). Tube formation assay in vitro showed the largest number of tube-like structures and branch points in UCA-PSCs among the three stem cells. Additionally, the total tube length in UCA-PSCs and UCV-PSCs was significantly longer than in WJ-MSCs (P<0.01). Microarray, qRT-PCR, and Western blot analysis showed that UCA-PSCs had the highest expression of the Notch ligand Jagged1 (JAG1), which is crucial for blood vessel maturation. Knockdown of Jagged1 significantly impaired the angiogenesis in UCA-PSCs. In summary, UCA-PSCs are promising cell populations for clinical use in ischemic diseases

Mst1-Mediated Phosphorylation of Nur77 Improves the Endometrial Receptivity in Human and Mice

Background: Successful embryo implantation requires the attachment of a blastocyst to the receptive endometrial epithelium, which was disturbed in the women with recurrent implantation failure (RIF). Endometrial β3-integrin was the most important adhesion molecule contributing to endometrial receptivity in both humans and mice. Nur77 has been proven indispensable for fertility in mice, here we explore the role of Nur77 on embryo-epithelial adhesion and potential treatment to embryo implantation failure. Methods: The expression and location of Mst1 and Nur77 in endometrium from fertile women and RIF patients were examined by IHC, qRT-PCR and Western blotting. In vitro kinase assay following with LC-MS/MS were used to identify the phosphorylation site of Nur77 activated by Mst1. The phosphorylated Nur77 was detected by phos-tag SDS-PAGE assay and specific antibody against phospho-Nur77-Thr366. The effect of embryo-epithelium interaction was determined in the BeWo spheroid or mouse embryo adhesion assay, and delayed implantation mouse model. RNA-seq was used to explore the mechanism by which Nur77 derived peptide promotes endometrial receptivity. Findings: Endometrial Mammalian sterile 20 (STE20)-like kinase 1 (Mst1) expression level was decreased in the women with RIF than that in the fertile control group, while Mst1 activation in the epithelial cells promoted trophoblast-uterine epithelium adhesion. The effect of Nur77 mediated trophoblast-uterine epithelium adhesion was facilitated by active Mst1. Mechanistically, mst1 promotes the transcription activity of Nur77 by phosphorylating Nur77 at threonine 366 (T366), and consequently increased downstream target β3-integrin expression. Furthermore, a Nur77-derived peptide containing phosphorylated T366 markedly promoted mouse embryo attachment to Ishikawa cells ([4 (2-4)] vs [3 (2-4)]) and increased the embryo implantation rate (4 vs 1.4) in a delayed implantation mouse model by regulating integrin signalling. Finally, it is observed that the endometrial phospho-Nur77 (T366) level is decreased by 80% in the women with RIF. Interpretation: In addition to uncovering a potential regulatory mechanism of Mst1/Nur77/β3-integrin signal axis involved in the regulation of embryo-epithelium interaction, our finding provides a novel marker of endometrial receptivity and a potential therapeutic agent for embryo implantation failure. Funding: National Key Research and Development Program of China (2018YFC1004400), the National Natural Science Foundation of China (82171653, 82271698, 82030040, 81971387 and 30900727), and National Institutes of Health grants (R01HL103869)

Overlap-based undersampling method for classification of imbalanced medical datasets.

Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques

The role of autophagy in cardiovascular disease: Cross-interference of signaling pathways and underlying therapeutic targets

Autophagy is a conserved lysosomal pathway for the degradation of cytoplasmic proteins and organelles, which realizes the metabolic needs of cells and the renewal of organelles. Autophagy-related genes (ATGs) are the main molecular mechanisms controlling autophagy, and their functions can coordinate the whole autophagic process. Autophagy can also play a role in cardiovascular disease through several key signaling pathways, including PI3K/Akt/mTOR, IGF/EGF, AMPK/mTOR, MAPKs, p53, Nrf2/p62, Wnt/β-catenin and NF-κB pathways. In this paper, we reviewed the signaling pathway of cross-interference between autophagy and cardiovascular diseases, and analyzed the development status of novel cardiovascular disease treatment by targeting the core molecular mechanism of autophagy as well as the critical signaling pathway. Induction or inhibition of autophagy through molecular mechanisms and signaling pathways can provide therapeutic benefits for patients. Meanwhile, we hope to provide a unique insight into cardiovascular treatment strategies by understanding the molecular mechanism and signaling pathway of crosstalk between autophagy and cardiovascular diseases