The cGAS-STING pathway in COPD: targeting its role and therapeutic potential.

Chronic obstructive pulmonary disease(COPD) is a gradually worsening and fatal heterogeneous lung disease characterized by airflow limitation and increasingly decline in lung function. Currently, it is one of the leading causes of death worldwide. The consistent feature of COPD is airway inflammation. Several inflammatory factors are known to be involved in COPD pathogenesis; however, anti-inflammatory therapy is not the first-line treatment for COPD. Although bronchodilators, corticosteroids and roflumilast could improve airflow and control symptoms, they could not reverse the disease. The cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) signaling pathway plays an important novel role in the immune system and has been confirmed to be a key mediator of inflammation during infection, cellular stress, and tissue damage. Recent studies have emphasized that abnormal activation of cGAS-STING contributes to COPD, providing a direction for new treatments that we urgently need to develop. Here, we focused on the cGAS-STING pathway, providing insight into its molecular mechanism and summarizing the current knowledge on the role of the cGAS-STING pathway in COPD. Moreover, we explored antagonists of cGAS and STING to identify potential therapeutic strategies for COPD that target the cGAS-STING pathway.

Range-Intensity-Profile-Guided Gated Light Ranging and Imaging Based on a Convolutional Neural Network.

Three-dimensional (3D) range-gated imaging can obtain high spatial resolution intensity images as well as pixel-wise depth information. Several algorithms have been developed to recover depth from gated images such as the range-intensity correlation algorithm and deep-learning-based algorithm. The traditional range-intensity correlation algorithm requires specific range-intensity profiles, which are hard to generate, while the existing deep-learning-based algorithm requires large number of real-scene training data. In this work, we propose a method of range-intensity-profile-guided gated light ranging and imaging to recover depth from gated images based on a convolutional neural network. In this method, the range-intensity profile (RIP) of a given gated light ranging and imaging system is obtained to generate synthetic training data from Grand Theft Auto V for our range-intensity ratio and semantic network (RIRS-net). The RIRS-net is mainly trained on synthetic data and fine-tuned with RIP data. The network learns both semantic depth cues and range-intensity depth cues in the synthetic data, and learns accurate range-intensity depth cues in the RIP data. In the evaluation experiments on both a real-scene and synthetic test dataset, our method shows a better result compared to other algorithms.