4-(4-Chloro­phen­yl)-2,6-bis­(1H-indol-3-yl)-1,4-dihydro­pyridine-3,5-dicarbo­nitrile ethanol monosolvate

In the title compound, C29H18ClN5·C2H6O, the dihydro­pyridine ring adopts a strongly flattened envelope conformation, with a maximum deviation of 0.139 (2) Å from its best plane for the Csp 3 atom. The dihedral angles between the dihydro­pyridine ring plane and the two indole rings in positions 2 and 6 are 34.28 (5) and 40.50 (6)°, respectively. In turn, the benzene ring and the dihydro­pyridine ring are oriented at a dihedral angle of 74.69 (6)°. An intra­molecular C—H⋯Cl hydrogen bond occurs. In the crystal, mol­ecules are linked by N—H⋯N, N—H⋯O and O—H⋯N hydrogen bonds into layers parallel to (001). There are short C—H⋯Cl contacts between mol­ecules in neighboring layers

Rap2B promotes migration and invasion of human suprarenal epithelioma

The aim of our study was to elucidate the role of Rap2B in the development of human suprarenal epithelioma and to investigate the effect of Rap2B on suprarenal epithelioma cells migration and invasion. We use tissue microarray and immunohistochemistry to evaluate Rap2B staining in 75 suprarenal epithelioma tissues and 75 tumor-adjacent normal renal tissues. And the expression of Rap2B protein in human suprarenal epithelioma cells and tissues was detected by western blot simultaneously. The role of Rap2B in suprarenal epithelioma cells migration and invasion was detected by using wound healing assay, cell migration assay, and matrigel invasion assay. After that, we performed western blot analysis and gelatin zymography to detect MMP-2 protein expression and enzyme activity. Our research showed that Rap2B expression was increased in tumor tissues compared with tumor-adjacent normal renal tissues. But no correlation was found between Rap2B expression and clinicopathological parameters. In addition, we found that Rap2B promoted the cell migration and invasion abilities, and Rap2B increased MMP-2 expression and enzyme activity in suprarenal epithelioma cells. Our data indicated that Rap2B expression is significantly increased in human suprarenal epithelioma and Rap2B can promote the cell migration and invasion abilities, which may provide a new target for the treatment of suprarenal epithelioma

Effects of two Lactobacillus strains on lipid metabolism and intestinal microflora in rats fed a high-cholesterol diet

<p>Abstract</p> <p>Background</p> <p>The hypocholesterolemic effects of lactic acid bacteria (LAB) have now become an area of great interest and controversy for many scientists. In this study, we evaluated the effects of <it>Lactobacillus plantarum </it>9-41-A and <it>Lactobacillus fermentum </it>M1-16 on body weight, lipid metabolism and intestinal microflora of rats fed a high-cholesterol diet.</p> <p>Methods</p> <p>Forty rats were assigned to four groups and fed either a normal or a high-cholesterol diet. The LAB-treated groups received the high-cholesterol diet supplemented with <it>Lactobacillus plantarum </it>9-41-A or <it>Lactobacillus fermentum </it>M1-16. The rats were sacrificed after a 6-week feeding period. Body weights, visceral organ and fat pad weights, serum and liver cholesterol and lipid levels, and fecal cholesterol and bile acid concentrations were measured. Liver lipid deposition and adipocyte size were evaluated histologically.</p> <p>Results</p> <p>Compared with rats fed a high-cholesterol diet but without LAB supplementation, serum total cholesterol, low-density lipoprotein cholesterol and triglycerides levels were significantly decreased in LAB-treated rats (p < 0.05), with no significant change in high-density lipoprotein cholesterol levels. Hepatic cholesterol and triglyceride levels and liver lipid deposition were significantly decreased in the LAB-treated groups (p < 0.05). Accordingly, both fecal cholesterol and bile acids levels were significantly increased after LAB administration (p < 0.05). Intestinal <it>Lactobacillus </it>and <it>Bifidobacterium </it>colonies were increased while <it>Escherichia coli </it>colonies were decreased in the LAB-treated groups. Fecal water content was higher in the LAB-treated groups. Compared with rats fed a high-cholesterol diet, administration of <it>Lactobacillus plantarum </it>9-41-A resulted in decreases in the body weight gain, liver and fat pad weight, and adipocytes size (p < 0.05).</p> <p>Conclusions</p> <p>This study suggests that LAB supplementation has hypocholesterolemic effects in rats fed a high-cholesterol diet. The ability to lower serum cholesterol varies among LAB strains. Our strains might be able to improve the intestinal microbial balance and potentially improve intestinal transit time. Although the mechanism is largely unknown, <it>L. plantarum </it>9-41-A may play a role in fat metabolism.</p

Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm

AIM: To conduct a classification study of high myopic maculopathy (HMM) using limited datasets, including tessellated fundus, diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and macular atrophy, and minimize annotation costs, and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification. METHODS: The optimized ALFA-Mix algorithm (ALFA-Mix+) was compared with five algorithms, including ALFA-Mix. Four models, including ResNet18, were established. Each algorithm was combined with four models for experiments on the HMM dataset. Each experiment consisted of 20 active learning rounds, with 100 images selected per round. The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+ outperformed other algorithms. Finally, this study employed six models, including EfficientFormer, to classify HMM. The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+ algorithm to achieve satisfactory classification results with a small dataset. RESULTS: ALFA-Mix+ outperforms other algorithms with an average superiority of 16.6, 14.75, 16.8, and 16.7 rounds in terms of accuracy, sensitivity, specificity, and Kappa value, respectively. This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images. The EfficientFormer achieved the best results with an accuracy, sensitivity, specificity, and Kappa value of 0.8821, 0.8334, 0.9693, and 0.8339, respectively. Therefore, by combining ALFA-Mix+ with EfficientFormer, this study achieved results with an accuracy, sensitivity, specificity, and Kappa value of 0.8964, 0.8643, 0.9721, and 0.8537, respectively. CONCLUSION: The ALFA-Mix+ algorithm reduces the required samples without compromising accuracy. Compared to other algorithms, ALFA-Mix+ outperforms in more rounds of experiments. It effectively selects valuable samples compared to other algorithms. In HMM classification, combining ALFA-Mix+ with EfficientFormer enhances model performance, further demonstrating the effectiveness of ALFA-Mix+

Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2

AIM: To evaluate the application of an intelligent diagnostic model for pterygium. METHODS: For intelligent diagnosis of pterygium, the attention mechanisms—SENet, ECANet, CBAM, and Self-Attention—were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model. The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University. Conventional classification models—VGG16, ResNet50, MobileNetV2, and EfficientNetB7—were trained on the same dataset for comparison. To evaluate model performance in terms of accuracy, Kappa value, test time, sensitivity, specificity, the area under curve (AUC), and visual heat map, 470 test images of the anterior segment of the pterygium were used. RESULTS: The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%, and the Kappa value of the model was 88.92%. The testing time using the model was 9ms/image in the server and 138ms/image in the local computer. The sensitivity, specificity, and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%, 100%, and 100%, respectively; using anterior segment images in the observation period were 88.30%, 95.32%, and 96.70%, respectively; and using the anterior segment images in the surgery period were 88.18%, 94.44%, and 97.30%, respectively. CONCLUSION: The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium