OM-VST: A video action recognition model based on optimized downsampling module combined with multi-scale feature fusion.

Video classification, as an essential task in computer vision, aims to identify and label video content using computer technology automatically. However, the current mainstream video classification models face two significant challenges in practical applications: first, the classification accuracy is not high, which is mainly attributed to the complexity and diversity of video data, including factors such as subtle differences between different categories, background interference, and illumination variations; and second, the number of model training parameters is too high resulting in longer training time and increased energy consumption. To solve these problems, we propose the OM-Video Swin Transformer (OM-VST) model.

[Hematoporphyrin derivative-mediated photodynamic therapy for human colon carcinoma: a comparative study with LoVo and CoLo205 cells in vitro].

Objective: To investigate the killing effect of photodynamic therapy (PDT) mediated by hematoporphyrin derivative (HpD) on human colon carcinoma LoVo and CoLo205 cells in vitro.

Methods: LoVo and CoLo205 cells cultured in vitro were incubated in the presence of 0.5, 1.