Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis.

BACKGROUND: Artificial intelligence represents an emerging area with promising potential for improving colonoscopy quality. AIMS: To develop a colon polyp detection model using STFT and evaluate its performance through a randomized sample experiment. METHODS: Colonoscopy videos from the Digestive Endoscopy Center of the First Affiliated Hospital of Anhui Medical University, recorded between January 2018 and November 2022, were selected and divided into two datasets. To verify the model's practical application in clinical settings, 1500 colonoscopy images and 1200 polyp images of various sizes were randomly selected from the test set and compared with the STFT model's and endoscopists' recognition results with different years of experience. RESULTS: In the randomized sample trial involving 1500 colonoscopy images, the STFT model demonstrated significantly higher accuracy and specificity compared to endoscopists with low years of experience (0.902 vs. 0.809, 0.898 vs. 0.826, respectively). Moreover, the model's sensitivity was 0.904, which was higher than that of endoscopists with low, medium, or high years of experience (0.80, 0.896, 0.895, respectively), with statistical significance (P < 0.05). In the randomized sample experiment of 1200 polyp images of different sizes, the accuracy of the STFT model was significantly higher than that of endoscopists with low years of experience when the polyp size was ≤ 0.5 cm and 0.6-1.0 cm (0.902 vs. 0.70, 0.953 vs. 0.865, respectively). CONCLUSIONS: The STFT-based colon polyp detection model exhibits high accuracy in detecting polyps in colonoscopy videos, with a particular efficiency in detecting small polyps (≤ 0.5 cm)(0.902 vs. 0.70, P < 0.001).

The degree of population aging and living carbon emissions: Evidence from China.

Population aging and global warming have become everyday concerns of all countries. Based on the panel data of 30 provinces in China from 2003 to 2019, this paper uses the panel fixed effect model and two-stage least square method to analyze the effect of population aging on domestic energy carbon emissions of urban and rural residents. On this basis, the threshold regression model is introduced to explore the heterogeneity of the effect under different aging levels. The results show that (1) the progress of population aging at the overall level will significantly increase the level of carbon emissions from household energy consumption. At the regional level, the effect of population aging on carbon emissions from household energy consumption in rural areas is higher than in urban areas. (2) Population aging has a nonlinear effect on the carbon emissions of residential energy consumption. For urban areas, when the level of population aging crosses the threshold, its marginal impact on living carbon emissions in urban areas is further enhanced. In contrast, the opposite is true in rural areas. (3) Heterogeneity analysis results show that the impact of population aging on residential energy carbon emissions differs in different regions at the national and rural levels but does not show regional heterogeneity at the urban level.

HNF4A-Bridging the Gap Between Intestinal Metaplasia and Gastric Cancer.

Background: Intestinal metaplasia (IM) of gastric epithelium has traditionally been regarded as an irreversible stage in the process of the Correa cascade. Exploring the potential molecular mechanism of IM is significant for effective gastric cancer prevention. Methods: The GSE78523 dataset, obtained from the Gene Expression Omnibus (GEO) database, was analyzed using RStudio software to identify the differently expressed genes (DEGs) between IM tissues and normal gastric epithelial tissues. Subsequently, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GESA), and protein-protein interaction (PPI) analysis were used to find potential genes. Additionally, the screened genes were analyzed for clinical, immunological, and genetic correlation aspects using single gene clinical correlation analysis (UALCAN), Tumor-Immune System Interactions Database (TISIDB), and validated through western blot experiments. Results: Enrichment analysis showed that the lipid metabolic pathway was significantly associated with IM tissues and the apolipoprotein B (APOB) gene was identified in the subsequent analysis. Experiment results and correlation analysis showed that the expression of APOB was higher in IM tissues than in normal tissues. This elevated expression of APOB was also found to be associated with the expression levels of hepatocyte nuclear factor 4A (HNF4A) gene. HNF4A was also found to be associated with immune cell infiltration to gastric cancer and was linked to the prognosis of gastric cancer patients. Moreover, HNF4A was also highly expressed in both IM tissues and gastric cancer cells. Conclusion: Our findings indicate that HNF4A regulates the microenvironment of lipid metabolism in IM tissues by targeting APOB. Higher expression of HNF4A tends to lead to a worse prognosis in gastric cancer patients implying it may serve as a predictive indicator for the progression from IM to gastric cancer.

Integrating network pharmacology, bioinformatics, and experimental validation to unveil the molecular targets and mechanisms of galangin for treating hepatocellular carcinoma.

BACKGROUND: Galangin, a flavonoid compound, is derived from Alpinia officinarum Hance. Previous studies have shown that galangin can inhibit the proliferation of hepatocellular carcinoma (HCC), but its mechanism is still unclear. This study aims to investigate the potential targets and molecular mechanisms of galangin on HCC through network pharmacology, bioinformatics, molecular docking, and experimental in vitro validation. METHODS: In this study, network pharmacology was used to investigate the targets and mechanisms of galangin in the treatment of HCC. AutoDockTools software was used to simulate and calculate the binding of galangin to its core targets. GO and KEGG enrichment analyses were conducted in the DAVID database to explore the main biological functions and signaling pathways impacted by galangin intervention. In addition, bioinformatics was applied to examine the correlation between the differential expressions of the anti-HCC core targets of galangin and the survival of patients with HCC. Finally, the findings obtained from network pharmacology and bioinformatics were verified in cell experiments. RESULTS: A total of 67 overlapping target genes of galangin and HCC were identified. Through the analysis of the protein-protein interaction (PPI) network, 10 hub genes with the highest degree of freedom were identified, including SRC, ESR1, MMP9, CDK4, CCNB1, MMP2, CDK2, CDK1, CHK1, and PLK1. These genes were found to be closely related to the degradation of the extracellular matrix, signal transduction, and the cell cycle. GO and KEGG enrichment analyses revealed that galangin exerts an anti-HCC role by affecting various signaling pathways, including the cell cycle, pathways in cancer, and the PI3K-Akt signaling pathway. The results of molecular docking indicated a significant interaction between galangin and CCNB1, CDK4, CDK1, and PLK1. Bioinformatics analysis revealed that CCNB1, CDK4, CDK1, and PLK1 were upregulated in the liver of patients with HCC at both the mRNA and protein levels. Flow cytometry analysis showed that galangin induced G0/G1 phase arrest and cell apoptosis in HepG2 and Huh7 cells. Additionally, galangin suppressed the expression of key proteins and mRNAs involved in the cell cycle pathway. CONCLUSIONS: These results suggest that galangin inhibits the growth of HCC cells by arresting the cell cycle at the G0/G1 phase.

Identification of the function of FOSB in cholangiocarcinoma using bioinformatics analysis.

Background: Exploring the potential mechanism of cholangiocarcinoma (CCA) metabolic reprogramming is significant for guiding clinical treatment. However, related research and exploration are still lacking. Therefore, we aimed to identify a reliable metabolism-related gene or biomarker of CCA using bioinformatics analysis. Methods: The GSE26566, GSE45001, and GSE132305 datasets were obtained from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) between CCA tissues and adjacent tissues were screened out. The key gene was identified through enrichment and functional analysis, and its immune and clinical correlation was investigated utilizing the Tumor Immune Evaluation Resource (TIMER2.0), the Tumor-Immune System Interactions Database (TISIDB), the Gene Expression Profiling Interactive Analysis (GEPIA2), and the Kaplan-Meier Plotter. Finally, immunohistochemistry (IHC) was performed to validate the results. Results: By analysis, the expression of FBJ murine osteosarcoma viral oncogene homolog B (FOSB) was significantly downregulated in CCA tissues when compared with adjacent tissues. Moreover, the expression levels of FOSB positively correlated with tumor-infiltrating immune cells in most tumors, and patients with high FOSB expression tended to have a better prognosis. The FOSB and SIRT3/HIF1A axes have similar expression trends and metabolic functions in CCA cells, and the correlation between of them was preliminarily explored by IHC experiments. Conclusions: The expression levels of FOSB are closely related to the prognosis of CCA patients, which may be a predictive indicator for prognosis and immunotherapy.