Background and Aims. Linked color imaging (LCI) helps screen and diagnose for early gastric cancer by color contrast in different mucosa. RGB (red, green, and blue) pixel brightness quantifies colors, which is relatively objective. Limited studies have combined LCI images with RGB to help screen for early gastric cancer (EGC). We aimed to evaluate the RGB pixel brightness characteristics of EGC and noncancer areas in LCI images. Methods. We retrospectively reviewed early gastric cancer (EGC) patients and LCI images. All pictures were evaluated by at least two endoscopic physicians. RGB pixel brightness analysis of LCI images was performed in MATLAB software to compare the cancer with noncancer areas. Receiver operating characteristic (ROC) curve was analyzed for sensitivity, specificity, cut-off, and area under the curve (AUC). Results. Overall, 38 early gastric cancer patients were enrolled with 38 LCI images. Pixel brightness of red, green, and blue in cancer was remarkably higher than those in noncancer areas (190.24±37.10 vs. 160.00±40.35, p<0.001; 117.96±33.91 vs. 105.33±30.01, p=0.039; 114.36±34.88 vs. 90.93±30.14, p<0.001, respectively). Helicobacter plyori (Hp) infection was not relevant to RGB distribution of EGC. Whether the score of Kyoto Classification of Gastritis (KCG) is ≥4 or p>0.05). Receiver operating characteristic (ROC) curve for differentiating cancer from noncancer was calculated. The maximum area under the curve (AUC) was 0.767 in B/G, with a sensitivity of 0.605, a specificity of 0.921, and a cut-off of 0.97. Conclusions. RGB pixel brightness was useful and more objective in distinguishing early gastric cancer for LCI images.

Background/Aims. The aim of this study is to examine the protective effect of the cholinergic anti-inflammatory pathway (CAP) in experimental esophagitis in rats. Methods. A total of 40 male Sprague-Dawley (SD) rats were randomly divided into five groups as follows: control group, sham + saline group, sham + acid group, operation + saline group, and operation + acid group. Two weeks after the dorsal motor nucleus of the vagus (DMV) destruction, hydrochloric acid with pepsin was perfused into the lower part of the esophagus for 90 min. The rats were sacrificed 60 min after perfusion. The esophagus was prepared for hematoxylin and eosin (HE) staining, and the degree of inflammation and NF-κB activation in the esophagus was measured. Inflammatory cytokines (TNF-α, IL-6, IL-1β, and PGE2) in the esophagus were measured by ELISA. The brain was removed and processed for c-fos immunohistochemistry staining. The c-fos-positive neurons were counted and analyzed. Results. The TNF-α, IL-1β, IL-6, and PGE2 concentrations in the esophageal tissue increased after acid perfusion. The microscopic esophagitis scores and the activation of NF-κB p65 in the esophagus were significantly higher in the operation + acid group than in the operation + saline group. c-fos-positive neurons significantly increased in rats receiving acid perfusion in the amygdala (AM), the paraventricular nucleus of the hypothalamus (PVN), the parabrachial nucleus (PBN), the nucleus of the solitary tract (NTS)/DMV, the nucleus ambiguous (NA), the reticular nucleus of the medulla (RNM), and the area postrema (AP). After DMV destruction, c-fos expression was reduced in the AM, PVN, PBN, NTS/DMV, NA, RNM, and AP, especially in the AM, PVN, NTS/DMV, RNM, and AP. Conclusions. The DMV is an important nucleus of the CAP. The DMV lesion can aggravate esophageal inflammation and injury from acid-induced acute esophagitis in a rat model. The CAP has a protective effect on the acute esophagitis rat model and could be a new therapy for reflux esophagitis (RE).

The early detection accuracy of early gastric cancer (EGC) determines the choice of the optimal treatment strategy and the related medical expenses. We aimed to develop a simple, affordable, and time-saving diagnostic model using six machine learning (ML) algorithms for the diagnosis of EGC. It is based on the endoscopy-based Kyoto classification score obtained after the completion of endoscopy and other clinical features obtained after medical consultation. We retrospectively evaluated 1999 patients who underwent gastrointestinal endoscopy at the China Beijing Hospital. Of these, 203 subjects were diagnosed with EGC. The data were randomly divided into training and test sets (ratio 4:1). We constructed six ML models, and the developed models were evaluated on the testing set. This procedure was repeated five times. The Kolmogorov–Arnold Networks (KANs) model achieved the best performance (mean AUC value: 0.76; mean balanced accuracy: 70.96%; mean precision: 58.91%; mean recall: 70.96%; mean false positive rate: 26.11%; mean false negative rate: 31.96%; and mean F1 score value: 58.46). The endoscopy-based Kyoto classification score was the most important feature with the highest feature importance score. The results suggest that the KAN model, the optimal ML model in this study, has the potential to identify EGC patients, which may result in a reduction in both the time cost and medical expenses in clinical practice.