DHAFormer: Dual-channel hybrid attention network with transformer for polyp segmentation.

The accurate early diagnosis of colorectal cancer significantly relies on the precise segmentation of polyps in medical images. Current convolution-based and transformer-based segmentation methods show promise but still struggle with the varied sizes and shapes of polyps and the often low contrast between polyps and their background. This research introduces an innovative approach to confronting the aforementioned challenges by proposing a Dual-Channel Hybrid Attention Network with Transformer (DHAFormer).

Detecting the location and significance of Ezrin protein expression in hepatocellular carcinoma.

To explore the characteristics of localization and prognostic implication of Ezrin expression in HCC, 92 cases of HCC meeting strict follow-up criteria were selected for immunohistochemical staining of Ezrin protein. Correlations between Ezrin expression and clinicopathological features of HCC were evaluated using Chi-square tests, survival rates were calculated using the Kaplan-Meier method, and the relationship between prognostic factors and patient overallsurvival was analyzed using Cox proportional hazard analysis. In results, Ezrin protein was mainly expressed in the inner side of the cell membrane of the adjacent non tumor tissues, and diffusely expressed in cytoplasm of HCC tissues.