M1 macrophages-derived exosomes miR-34c-5p regulates interstitial cells of Cajal through targeting SCF.

Slow transit constipation (STC) is a gastrointestinal disorder characterized by abnormal prolonged colonic transit time, which affects the life quality of many people. The decrease number of interstitial cells of Cajal (ICCs) is involved in the pathogenesis of STC. However, the molecular mechanism of loss of ICCs in STC remains unclear, making it difficult to develop new agents for the disease. In this study, we investigated the mechanism of decreasing ICCs in the pathogenesis of STC. We constructed the STC model rats by using atropine and diphenoxylate. A series of methods were used including immunofluorescence and immunochemistry staining, western blot, qRT-PCR, exosomes extraction and exosomes labeling. The results indicate that ICCs decreased in the STC rats accompanied with the macrophages activation. Further studies suggested that macrophages decreased the cell viability of ICCs by secretion exosomes containing miR-34c-5p. miR-34c5p targeted the 3Ꞌ -UTR of stem cell factor(SCF) mRNA and regulated the expression of SCF negatively. In conclusion, we demonstrated a novel regulatory mechanism of ICCs cell viability in STC. We found that exosome miR-34c-5p mediate macrophage-ICCs cross-talk. M1 macrophages derived exosomes miR-34c-5p decreased ICCs cell viability by directly targeting SCF.

Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network.

Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of 18F-labeled 2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET), magnetic resonance (MR), and computed tomography (CT) images. Thereafter, images are co-registered to derive electron density attributes required for FDG-PET attenuation correction and radiation therapy planning. Nevertheless, this traditional approach is subject to MR-CT registration defects, expands treatment expenses, and increases the patient's radiation exposure. To overcome these disadvantages, we propose a new framework for cross-modality image synthesis which we apply on MR-CT image translation for cervical cancer diagnosis and treatment. The framework is based on a conditional generative adversarial network (cGAN) and illustrates a novel tactic that addresses, simplistically but efficiently, the paradigm of vanishing gradient vs. feature extraction in deep learning. Its contributions are summarized as follows: 1) The approach -termed sU-cGAN-uses, for the first time, a shallow U-Net (sU-Net) with an encoder/decoder depth of 2 as generator; 2) sU-cGAN's input is the same MR sequence that is used for radiological diagnosis, i.e. T2-weighted, Turbo Spin Echo Single Shot (TSE-SSH) MR images; 3) Despite limited training data and a single input channel approach, sU-cGAN outperforms other state of the art deep learning methods and enables accurate synthetic CT (sCT) generation. In conclusion, the suggested framework should be studied further in the clinical settings. Moreover, the sU-Net model is worth exploring in other computer vision tasks.