HIV interactions with monocytes and dendritic cells: viral latency and reservoirs

HIV is a devastating human pathogen that causes serious immunological diseases in humans around the world. The virus is able to remain latent in an infected host for many years, allowing for the long-term survival of the virus and inevitably prolonging the infection process. The location and mechanisms of HIV latency are under investigation and remain important topics in the study of viral pathogenesis. Given that HIV is a blood-borne pathogen, a number of cell types have been proposed to be the sites of latency, including resting memory CD4+ T cells, peripheral blood monocytes, dendritic cells and macrophages in the lymph nodes, and haematopoietic stem cells in the bone marrow. This review updates the latest advances in the study of HIV interactions with monocytes and dendritic cells, and highlights the potential role of these cells as viral reservoirs and the effects of the HIV-host-cell interactions on viral pathogenesis

Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement

The non-uniform illumination or imbalanced intensity in medical images brings challenges for automated screening, examination and diagnosis of diseases. Previously, CycleGAN was proposed to transform input images into enhanced ones without paired images. However, it did not consider many local details of the structures, which are essential for medical images. In this paper, we propose a Cycle Structure and Illumination constrained GAN (CSI-GAN), for medical image enhancement. Inspired by CycleGAN based on the global constraints of the adversarial loss and cycle consistency, the proposed CSI-GAN treats low and high quality images as those in two domains and computes local structure and illumination constraints for learning both overall characteristics and local details. To evaluate the effectiveness of CSI-GAN, we have conducted experiments over two medical image datasets: corneal confocal microscopy (CCM) and endoscopic images. The experimental results show that our method yields better performance than both conventional methods and other deep learning based methods. As a complementary output, we will release the CCM dataset to the public in the future