Macrophage as a crucial component of innate immunity, plays an important role in inflammation and infection immunity. Notch signal pathway is a highly conserved pathway, which regulates cellular fate and participates in numerous pathological processes. At present, a lot of literature has confirmed the role of Notch signaling in regulating the differentiation, activation and metabolism of macrophage during inflammation and infection. This review focuses on how Notch signaling promotes macrophage pro-inflammatory and anti-infective immune function in different inflammatory and infectious diseases. In this regulation, Notch signaling interact with TLR signaling in macrophages or inflammatory-related cytokines including IL-6, IL-12, and TNF-α. Additionally, the potential application and challenges of Notch signaling as a therapeutic target against inflammation and infectious diseases are also discussed.
Humans ; Signal Transduction ; Macrophages ; Cytokines/metabolism* ; Inflammation/metabolism* ; Communicable Diseases ; Receptors, Notch/metabolism*

Objective: To investigate whether a deep learning-based model using unenhanced computed tomography (CT) at baseline could predict the malignancy of pulmonary nodules. Methods: A deep learning model was trained and applied for the discrimination of pulmonary nodule in Dr. Wise Lung Analyzer. This study retrospectively recruited 130 consecutive participants with pulmonary nodules detected on CT who undergoing biopsy or surgery from May 2009 to June 2017 in Jinling hospital. A total of 136 pulmonary nodules were included in this study, including 86 malignant nodules and 50 benign ones. All patients underwent CT scans 2 times at least, the first scan was defined as baseline and the last scan before the pathological results was defined as final scan. The ROC curve of deep learning model was plotted and the AUCs were calculated. Delong test was used to examine the difference of AUCs baseline and final scan. The nodules were further divided into subsolid nodule group (pure ground-glass nodule and part solid nodule) (n=87) and solid nodule group (n=49). The difference of AUCs at baseline and final scans was evaluated intra two groups. Results: The AUCs of the deep learning model at final and baseline scans were 0.876 and 0.819, respectively. There was no significant difference between them (P=0.075). The result indicated that the model could predict the consequences of pulmonary nodules well at baseline. In small nodules (longest diameter ≤10mm), the AUC at final scan (0.847) was better than it at baseline scan (0.734), but there was no significant difference between them (P=0.058). In solid nodule group, The AUC at final scan (0.932) was better than it at baseline scan (0.835), but there was no significant difference between them (P=0.066). In subsolid nodule group, the deep learning model exhibited consistent performance at final scan (AUC, 0.759) with the baseline scan (AUC, 0.728, P=0.580). Conclusions The deep learning model could predict the malignancy of pulmonary nodules including small ones at baseline, and the model exhibited consistent performance between baseline and final scans in subsolid nodules.