Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Objective:To develop a deep learning-based system for real-time recognition and classification of portal hypertensive gastropathy (PHG) and evaluate its ability to assist junior endoscopists.Methods:A total of 2 848 gastroscopy images from 832 patients with liver cirrhosis were selected from Digestive Endoscopy Center databases of Renmin Hospital of Wuhan University, Wuhan Hospital of Traditional Chinese and Western Medicine, and the Second Hospital of Jingzhou from January 2015 to October 2023. This system referred to 3 endoscopic features of Baveno Ⅱ scoring system. Three models were developed respectively for gastric antral vascular ectasia (GAVE), mosaic-like pattern (MLP), and red marks (RM). The specific classification references were as follows: (1) GAVE model: 0 no, 1 yes; (2) MLP model: 0 no, 1 mild, 2 severe; (3) RM model: 0 no, 1 isolated, 2 fused. The classification results for endoscopic characteristics of PHG of 3 endoscopy experts were taken as the gold standard. The yolov8-m model was used for training. The training dataset, validation dataset, and test dataset were allocated at a ratio of 8∶1∶1. The test dataset was used to evaluate the performance of models and their auxiliary effects on endoscopists. The accuracy, recall, precision, specificity and Kappa coefficient were calculated. Results:The accuracy, recall, specificity of GAVE model were 96.0% (48/50), 87.5% (7/8) and 97.6% (41/42). There was no significant difference between its accuracy and the gold standard ( χ2=316.226, P=1.000). The precision of GAVE1 and GAVE0 were 87.5% (7/8) and 97.6% (41/42) respectively. The accuracy of MLP model was 84.1% (132/157), and there was no significant difference compared with the gold standard ( χ2=3.286, P=0.193). The precision and recall of MLP2 were 88.2% (15/17) and 75.0% (15/20). The precision and recall of MLP1 were 77.9% (60/77) and 88.2% (60/68). The precision and recall of MLP0 were 90.5% (57/63) and 82.6% (57/69). The accuracy of RM model was 87.9% (123/140), and there was no significant difference compared with the gold standard ( χ2=2.891, P=0.409). The precision and recall of RM2 were 94.7% (18/19) and 78.3% (18/23). The precision and recall of RM1 were 72.2% (26/36) and 81.3% (26/32). The precision and recall of RM0 were 92.9% (79/85) and 92.9% (79/85). The mean accuracy of the three junior endoscopists, with and without the assistance of the GAVE model, MLP model, and RM model, respectively increased from 95.3% to 99.3%, from 83.9% to 91.9%, and from 81.9% to 83.1%. The overall consistency analysis of the 3 junior endoscopists with the gold standard indicated that the consistency of the GAVE model before and after assistance was extremely strong (both an overall Kappa of 1.000); the consistency before assistance of the MLP model was moderate (with an overall Kappa of 0.601), which increased to extremely strong after assistance (with an overall Kappa of 0.964); and the consistency of the RM model before and after assistance was also relatively strong (with an overall Kappa of 0.792 before and 0.798 after). Conclusion:The deep learning system accurately identifies and classifies PHG features and significantly enhances diagnostic performance of junior endoscopists.

Gastric cancer(GC)is one of the most common gastrointestinal tumors.As a newly discovered type of non-coding RNAs,transfer RNA(tRNA)-derived small RNAs(tsRNAs)play a dual biological role in cancer.Our previous studies have demonstrated the potential of tRF-23-Q99P9P9NDD as a diagnostic and prognostic biomarker for GC.In this work,we confirmed for the first time that tRF-23-Q99P9P9NDD can promote the proliferation,migration,and invasion of GC cells in vitro.The dual luciferase reporter gene assay confirmed that tRF-23-Q99P9P9NDD could bind to the 3'untranslated region(UTR)site of acyl-coenzyme A dehydrogenase short/branched chain(ACADSB).In addition,ACADSB could rescue the effect of tRF-23-Q99P9P9NDD on GC cells.Next,we used Gene Ontology(GO),the Kyoto Encyclopedia of Genes and Genomes(KEGG),and Gene Set Enrichment Analysis(GSEA)to find that downregulated ACADSB in GC may promote lipid accumulation by inhibiting fatty acid catabolism and ferroptosis.Finally,we verified the correlation between ACADSB and 12 ferroptosis genes at the transcriptional level,as well as the changes in reactive oxygen species(ROS)levels by flow cytometry.In summary,this study proposes that tRF-23-Q99P9P9NDD may affect GC lipid metabolism and ferroptosis by targeting ACADSB,thereby promoting GC progression.It provides a theoretical basis for the diagnostic and prognostic monitoring value of GC and opens up new possibilities for treatment.

With continually improving reperfusion strategies and patient care, the overall mortality of acute myocardial infarction (AMI) has been significantly reduced during the past two decades. However, this success is a double-edged sword, as many patients surviving an AMI will progress towards ischemic heart failure (HF) over time. The pathologic causes of ischemic HF are undoubtedly multifactorial. However, the inflammatory response is considered one of the most important causes of pathological remodeling because it spans the whole process of HF development. The macrophage-mediated inflammatory response was once considered a purely harmful factor leading to pathological remodeling and HF. However, growing evidence demonstrates that multiple subgroups of macrophage exist and contribute differently to ischemic HF development. Understanding macrophage populations and how they contribute to post-MI remodeling and consequent ischemic HF is, therefore, critical to understanding and treating the disease. This review focuses on different macrophage populations that regulate post-MI cardiac injury and how immunoregulation therapy may benefit patients with ischemic HF.

With continually improving reperfusion strategies and patient care, the overall mortality of acute myocardial infarction (AMI) has been significantly reduced during the past two decades. However, this success is a double-edged sword, as many patients surviving an AMI will progress towards ischemic heart failure (HF) over time. The pathologic causes of ischemic HF are undoubtedly multifactorial. However, the inflammatory response is considered one of the most important causes of pathological remodeling because it spans the whole process of HF development. The macrophage-mediated inflammatory response was once considered a purely harmful factor leading to pathological remodeling and HF. However, growing evidence demonstrates that multiple subgroups of macrophage exist and contribute differently to ischemic HF development. Understanding macrophage populations and how they contribute to post-MI remodeling and consequent ischemic HF is, therefore, critical to understanding and treating the disease. This review focuses on different macrophage populations that regulate post-MI cardiac injury and how immunoregulation therapy may benefit patients with ischemic HF.