N-acetylcysteine decreases airway inflammation and responsiveness in asthma by modulatingclaudin 18 expression

Background/Aims@#N-acetylcysteine (NAC) affects signaling pathways involved in apoptosis, angiogenesis, cell growth and arrest, redox-regulated gene expression, and the inflammatory response. However, it is not known how the signal mechanism for tight junctional protein claudin (CLDN) 18 is regulated in asthma patients. @*Methods@#To investigate the effects of NAC on CLDN18 expression in a mouse model of asthma, and to assess plasma levels of CLDN18 in asthma patients. A murine model of asthma induced by ovalbumin (OVA) was established using wild-type BALB/c female mice, and the levels of CLDNs, phosphorylated-pyruvate dehydrogenase kinase 1 (p-PDK1), and protein kinase B (Akt) pathway proteins following NAC treatment were examined by Western blotting and immunohistochemistry. In addition, the plasma levels of CLDN18 were evaluated in asthmatic patients and control subjects. @*Results@#NAC diminished OVA-induced airway hyper-responsiveness and inflammation.Levels of CLDN18 protein were higher in lung tissue from OVA mice than tissue from control mice, and were increased by treatment with NAC or dexamethasone. Treatment with NAC or dexamethasone decreased the OVA-induced increase in interleukin-1α protein levels. Although treatment with NAC increased OVA-induced p-PDK1 protein levels, it decreased phosphorylated Akt (pAkt)/Akt levels. Soluble CLDN18 levels were lower in patients with asthma than in controls and were correlated with the percentage of neutrophils, forced expiratory volume in 1 second (FEV1)/forced vital capacity % (FVC%) and FEV1%. @*Conclusions@#CLDN18 plays a role in the pathogenesis of asthma and NAC diminishes airway inflammation and responsiveness by modulating CLDN18 expression.

Convolutional neural network classifier for distinguishing Barrett's esophagus and neoplasia endomicroscopy images.

Barrett's esophagus is a diseased condition with abnormal changes of the cells in the esophagus. Intestinal metaplasia (IM) and gastric metaplasia (GM) are two sub-classes of Barrett's esophagus. As IM can progress to the esophageal cancer, the neoplasia (NPL), developing methods for classifying between IM and GM are important issues in clinical practice. We adopted a deep learning (DL) algorithm to classify three conditions of IM, GM, and NPL based on endimicroscopy images. We constructed a convolutional neural network (CNN) architecture to distinguish among three classes. A total of 262 endomicroscopy imaging data of Barrett's esophagus were obtained from the international symposium on biomedical imaging (ISBI) 2016 challenge. 155 IM, 26 GM and 55 NPL cases were used to train the architecture. We implemented image distortion to augment the sample size of the training data. We tested our proposed architecture using the 26 test images that include 17 IM, 4 GM and 5 NPL cases. The classification accuracy was 80.77%. Our results suggest that CNN architecture could be used as a good classifier for distinguishing endomicroscopy imaging data of Barrett's esophagus.