Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System.

OBJECTIVES: Gastroesophageal reflux disease (GERD) is a complex disease with a high worldwide prevalence. The Los Angeles classification (LA-grade) system is meaningful for assessing the endoscopic severity of GERD. Deep learning (DL) methods have been widely used in the field of endoscopy. However, few DL-assisted researches have concentrated on the diagnosis of GERD. This study is the first to develop a five-category classification DL model based on the LA-grade using explainable artificial intelligence (XAI). MATERIALS AND METHODS: A total of 2081 endoscopic images were used for the development of a DL model, and the classification accuracy of the models and endoscopists with different levels of experience was compared. RESULTS: Some mainstream DL models were utilized, of which DenseNet-121 outperformed. The area under the curve (AUC) of the DenseNet-121 was 0.968, and its classification accuracy (86.7%) was significantly higher than that of junior (71.5%) and experienced (77.4%) endoscopists. An XAI evaluation was also performed to explore the perception consistency between the DL model and endoscopists, which showed meaningful results for real-world applications. CONCLUSIONS: The DL model showed a potential in improving the accuracy of endoscopists in LA-grading of GERD, and it has noticeable clinical application prospects and is worthy of further promotion.

Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system.

BACKGROUND: The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed in the area of digestive endoscopy. To improve the efficiency and accuracy of the endoscopist's Hill classification and assist in incorporating it into routine endoscopy reports and GERD assessment examinations, this study first employed DL to establish a four-category model based on the Hill classification. MATERIALS AND METHODS: A dataset consisting of 3256 GEFV endoscopic images has been constructed for training and evaluation. Furthermore, a new attention mechanism module has been provided to improve the performance of the DL model. Combined with the attention mechanism module, numerous experiments were conducted on the GEFV endoscopic image dataset, and 12 mainstream DL models were tested and evaluated. The classification accuracy of the DL model and endoscopists with different experience levels was compared. RESULTS: 12 mainstream backbone networks were trained and tested, and four outstanding feature extraction backbone networks (ResNet-50, VGG-16, VGG-19, and Xception) were selected for further DL model development. The ResNet-50 showed the best Hill classification performance; its area under the curve (AUC) reached 0.989, and the classification accuracy (93.39%) was significantly higher than that of junior (74.83%) and senior (78.00%) endoscopists. CONCLUSIONS: The DL model combined with the attention mechanism module in this paper demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.

A new attention mechanism module has been proposed and integrated into the DL model.According to our knowledge, this is the first study to establish a four-category DL model based on the Hill grading.The DL model demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists.

The yellowhorn AGL transcription factor gene XsAGL22 contributes to ABA biosynthesis and drought tolerance in poplar.

Regulation of abscisic acid (ABA) biosynthesis helps plants adapt to drought stress, but the underlying molecular mechanisms are largely unclear. Here, a drought-induced transcription factor XsAGL22 was isolated from yellowhorn (Xanthoceras sorbifolium Bunge). Yeast one-hybrid and electrophoretic mobility shift assays indicated that XsAGL22 can physically bind to the promoters of the ABA biosynthesis-related genes XsNCED6 and XsBG1, and a dual-luciferase assay showed that XsAGL22 activates the promoters of the later two genes. Transient overexpression of XsAGL22 in yellowhorn leaves also increased the expression of XsNCED6 and XsBG1 and increased cellular ABA levels. Finally, heterologous overexpression of XsAGL22 in poplar increased ABA content, reduced stomatal aperture and increased drought resistance. Our results suggest that XsAGL22 is a powerful regulator of ABA biosynthesis and plays a critical role in drought resistance in plants.

[Effect of afforestation modes on soil microbial community and nitrogen functional genes in Hippophae rhamnoides plantation].

The study aimed to assess the effect of different afforestation modes on microbial composition and nitrogen functional genes in soil. Soil samples from a pure Hippophae rhamnoides stand (SS) and three mixed stands, namely, H. rhamnoides and Pinus tabuliformis (SY), H. rhamnoides and Platycladus orientalis (SB), H. rhamnoides and Robinia pseucdoacacia (SC) were selected. The results showed that the total PLFA (TPLFA), bacterial PLFA, gram positive bacterial PLFA (G⁺PLFA) were significantly higher in soil samples from other three stands than those of the pure one. However, no significant difference was found for fungal PLFA among them. The abundance of nifH, amoA, nirK and narG genes were higher in SY and SC than in SS. The TPLFA, G⁺PLFA, gram negative bacterial PLFA (G⁻PLFA), and all of the detected gene abundance were significantly and positively correlated with soil pH, total organic carbon, total nitrogen, ammonium nitrogen and available potassium. Afforestation modes affected indirectly soil microbial composition and functional genes through soil properties. Mixing P. tabuliformis or P. orientalis with H. rhamnoides might be suitable afforestation modes, which might improve soil quality.