Background. Gastric cancer (GC), an extremely aggressive tumor with a very different prognosis, is the third leading cause of cancer-related mortality. We aimed to construct a ferroptosis-related prognostic model that can be distinguished prognostically. Methods. The gene expression and the clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). The ferroptosis-related genes were obtained from the FerrDb. Using the “limma” R package and univariate Cox analysis, ferroptosis-related genes with differential expression and prognostic value were identified in the TCGA cohort. Last absolute shrinkage and selection operator (LASSO) Cox regression was applied to shrink ferroptosis-related predictors and construct a prognostic model. Functional enrichment, ESTIMATE algorithm, and single-sample gene set enrichment analysis (ssGSEA) were applied for exploring the potential mechanism. GC patients from the GEO cohort were used for validation. Results. An 8-gene prognostic model was constructed and stratified GC patients from TCGA and meta-GEO cohort into high-risk groups or low-risk groups. GC patients in high-risk groups have significantly poorer OS compared with those in low-risk groups. The risk score was identified as an independent predictor for OS. Functional analysis revealed that the risk score was mainly associated with the biological function of extracellular matrix (ECM) organization and tumor immunity. Conclusion. In conclusion, the ferroptosis-related model can be utilized for the clinical prognostic prediction in GC.

Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to double counting and omission. In this regard, this paper uses deep learning algorithms to achieve real-time monitoring of the number of dense hemp duck flocks and to promote the development of the intelligent farming industry. We constructed a new large-scale hemp duck object detection image dataset, which contains 1500 hemp duck object detection full-body frame labeling and head-only frame labeling. In addition, this paper proposes an improved attention mechanism YOLOv7 algorithm, CBAM-YOLOv7, adding three CBAM modules to the backbone network of YOLOv7 to improve the network’s ability to extract features and introducing SE-YOLOv7 and ECA-YOLOv7 for comparison experiments. The experimental results show that CBAM-YOLOv7 had higher precision, and the recall, mAP@0.5, and mAP@0.5:0.95 were slightly improved. The evaluation index value of CBAM-YOLOv7 improved more than those of SE-YOLOv7 and ECA-YOLOv7. In addition, we also conducted a comparison test between the two labeling methods and found that the head-only labeling method led to the loss of a high volume of feature information, and the full-body frame labeling method demonstrated a better detection effect. The results of the algorithm performance evaluation show that the intelligent hemp duck counting method proposed in this paper is feasible and can promote the development of smart reliable automated duck counting.

Background and Aim:This study aims to systematically analyze the effect of long-term therapy with proton pump inhibitors (PPIs) on the risk of gastric cancer. Methods: PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), and China biomedical literature database (CBM) were searched for studies before February 2019. We evaluated the quality of the included articles through the Newcastle-Ottawa Scale and gathered relevant data to calculate the pooled odds ratio (OR) through Stata14.0. Results: Seven relevant articles conformed to the inclusion criteria; 943 070 patients were included. The pooled OR was 2.50; 95% CI (1.74, 3.85); the subgroup analysis results showed that patients who had used PPIs for more than 36 months were most likely to develop gastric cancer, and an increased risk was observed among patients after Helicobacter pylori eradication. Noncardia gastric cancer was more likely to develop. Conclusions: Long-term use of PPIs can possibly increase the risk of gastric cancer even among patients after H. pylori eradication; in particular, for noncardia gastric cancer, the risk increases with longer durations of PPI use. Due to the limited number of studies, more high-quality studies are required to be designed. 7After H. pylori eradication H2RA, histamine 2 receptor antagonist; PPIs, proton pump inhibitors.

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