Retinoic acid tiers mitochondrial metabolism to Sertoli Cell-Mediated efferocytosis via a non-RAR-dependent mechanism.

Efferocytosis of massive non-viable germ cells by Sertoli cells (SCs), the specialized phagocytes, is essential for maintaining testis homeostasis. What elusive is the contribution of mitochondrial metabolism to this energy-consuming process, as SC has a preference of aerobic glycolysis. All-trans retinoic acid (ATRA, hereafter referred to as RA) is a well-known morphogen that primarily acts through the nuclear RA receptor (RAR). It sustains SC blood-testisbarrier integrity, and it's SC-derived RA sets the timing of meiotic commitment. In this study, we revisited RA in SC biology, from the perspective of SC-mediated efferocytosis. We provide evidence that RA induces transcriptional programming of multiple regulators involved in efferocytosis, which thereby represses SC-mediated efferocytosis, via a RAR-independent mechanism, as blocking pan-RAR activity fails to rescue RA-induced defective efferocytosis. RA-treated SCs exhibit alternations in mitochondrial dynamics and metabolism, and the hindered efferocytosis can be rescued by stimulating mitochondrial OXPHOS via pharmacological targeting of AMPK and PDK. We thus prefer to propose a signaling axis of RA-mitochondrial metabolism-efferocytosis. Our study uncovers a hitherto unappreciated role of RA in SC biology and tiers mitochondria metabolism to SC-mediated efferocytosis, contributing a deeper understanding of SC in male reproduction.

Automated fundus ultrasound image classification based on siamese convolutional neural networks with multi-attention.

Fundus ultrasound image classification is a critical issue in the medical field. Vitreous opacity (VO) and posterior vitreous detachment (PVD) are two common eye diseases, Now, the diagnosis of these two diseases mainly relies on manual identification by doctors. This method has the disadvantages of time-consuming and manual investment, so it is very meaningful to use computer technology to assist doctors in diagnosis. This paper is the first to apply the deep learning model to VO and PVD classification tasks. Convolutional neural network (CNN) is widely used in image classification. Traditional CNN requires a large amount of training data to prevent overfitting, and it is difficult to learn the differences between two kinds of images well. In this paper, we propose an end-to-end siamese convolutional neural network with multi-attention (SVK_MA) for automatic classification of VO and PVD fundus ultrasound images. SVK_MA is a siamese-structure network in which each branch is mainly composed of pretrained VGG16 embedded with multiple attention models. Each image first is normalized, then is sent to SVK_MA to extract features from the normalized images, and finally gets the classification result. Our approach has been validated on the dataset provided by the cooperative hospital. The experimental results show that our approach achieves the accuracy of 0.940, precision of 0.941, recall of 0.940, F1 of 0.939 which are respectively increased by 2.5%, 1.9%, 3.4% and 2.5% compared with the second highest model.