AIM: To observe the effect of ischemic preconditioning on cyclinD₁ expression in rat liver cells during early ischemic reperfusion. METHODS: Fifty-four SD rats were randomly divided into ischemic preconditioning group (IP), ischemia/reperfusion group (IR) and sham operation group (SO). The IP and IR groups were further divided into four sub-groups (n = 6). Sham operation group (SO) served as the control group (n = 6). A model of partial liver ischemia/reperfusion was used, in which rats were subjected to liver ischemia for 60 min prior to reperfusion. The animals in the IP group underwent ischemic preconditioning twice for 5 min each time prior to the ischemia/reperfusion challenge. After 0, 1, 2, and 4 h of reperfusion, serum and liver tissue in each group were collected to detect the level of serum ALT, liver histopathology and expression of cyclinD₁ mRNA and protein. Flow cytometry was used to detect cell cycle as the quantity indicator of cell regeneration. RESULTS: Compared with IR group, IP group showed a significantly lower ALT level in 1 h to 4 h sub-groups (P < 0.05). Proliferation index(PI) indicated by the S-phase and G₂/M-phase ratio [(S+G₂/M)/(G₀/G₁+S+G₂/M)] was significantly increased in IP group at 0 and 1 h (26.44± 7.60% vs 18.56 ± 6.40%,41.87 ± 7.27% vs 20.25 ± 6.70%, P < 0.05). Meanwhile, cyclinD₁ protein expression could be detected in IP group. But in IR group, cyclinD₁ protein expression occurred 2 h after reperfusion. The expression of cyclinD₁ mRNA increased significantly in IP group at 0 and 1 h (0.568 ± 0.112 vs 0.274 ± 0.069, 0.762 ± 0.164 vs 0.348 ± 0.093, P < 0.05). CONCLUSION: Ischemic preconditioning can protect liver cells against ischemia/reperfusion injury, which may be related to cell proliferation and expression of cyclinD₁ during early ischemic reperfusion.

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The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capture multiscale features, resulting in the loss of contextual information and insufficient recovery of high-level semantic features. In this paper, we present a new multibranch hybrid attention network (MHA-Net) to capture more contextual information and high-level semantic features. The main idea of our proposed MHA-Net is to use the multibranch hybrid attention feature decoder to recover more high-level semantic features. The lightweight pyramid split attention (PSA) module is used to connect the encoder and decoder subnetwork to obtain a richer multiscale feature map. We compare the proposed MHA-Net to state-of-art approaches on the DRIVE dataset, the fluoroscopic roentgenographic stereophotogrammetric analysis X-ray dataset, and the polyp dataset. The experimental results on different modal images reveal that our proposed MHA-Net provides better segmentation results than other segmentation approaches.