Gastric Mucosal Protective Effects of Cinnamomum cassia in a Rat Model of Ethanol-Induced Gastric Injury.

Cinnamomum cassia (cassia) is a tropical aromatic evergreen tree of the Lauraceae family well known for its fragrance and spicy flavor and widely used in Asian traditional medicine. It has recently garnered attention for its diverse potential health benefits, including anti-inflammatory, anti-cancer, and anti-diabetic properties. However, the gastroprotective effect of C. cassia, particularly against ethanol-induced gastric damage, remains unclear. We investigated the potential gastroprotective property of C. cassia and the underlying mechanisms of action in a rat model of ethanol-induced gastric injury. To assess its effectiveness, rats were fed C. cassia for a 14-day period prior to inducing gastric damage by oral administration of ethanol. Our results indicated that pre-treatment with C. cassia mitigated ethanol-induced gastric mucosal lesions and bleeding. Reduced gastric acid secretion and expression of acid secretion-linked receptors were also observed. Additionally, pretreatment with C. cassia led to decreased levels of inflammatory factors, including TNF-α, p-p65, and IκBα. Notably, C. cassia upregulated the expressions of HO1 and HSP90, with particular emphasis on the enhanced expression of PAS and MUC, the crucial gastric mucosa defense molecules. These findings suggest that C. cassia has protective effects on the gastric mucosa and can effectively reduce oxidative stress and inflammation.

SAR ATR for Limited Training Data Using DS-AE Network.

Although automatic target recognition (ATR) with synthetic aperture radar (SAR) images has been one of the most important research topics, there is an inherent problem of performance degradation when the number of labeled SAR target images for training a classifier is limited. To address this problem, this article proposes a double squeeze-adaptive excitation (DS-AE) network where new channel attention modules are inserted into the convolutional neural network (CNN) with a modified ResNet18 architecture. Based on the squeeze-excitation (SE) network that employs a representative channel attention mechanism, the squeeze operation of the DS-AE network is carried out by additional fully connected layers to prevent drastic loss in the original channel information. Then, the subsequent excitation operation is performed by a new activation function, called the parametric sigmoid, to improve the adaptivity of selective emphasis of the useful channel information. Using the public SAR target dataset, the recognition rates from different network structures are compared by reducing the number of training images. The analysis results and performance comparison demonstrate that the DS-AE network showed much more improved SAR target recognition performances for small training datasets in relation to the CNN without channel attention modules and with the conventional SE channel attention modules.