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Immunoglobulin γ-1 heavy chain constant region (IGHG1) is a functional isoform of immunoglobulins and plays an important role in the cytolytic activity of immune effector cells. Dysregulated IGHG1 was implicated in the occurrence and development of various tumors. Protoporphyrin IX (PpIX) is an endogenous fluorophore and is used in photodynamic therapy, which induces the generation of reactive oxygen species to initiate the death of tumor cells. However, the roles of IGHG1 in the colorectal cancer cell proliferation and PpIX accumulation have not been reported yet. Data from qRT-PCR and western blot analysis showed that IGHG1 was up-regulated in the colorectal cancer cells. Colorectal cancer cells were then transfected with shRNA targeting IGHG1 to down-regulate IGHG1 and conducted with Cell Counting Kit 8 (CCK8) and colony formation assays. Results demonstrated that shRNA-mediated down-regulation of IGHG1 decreased cell viability of colorectal cancer and suppressed cell proliferation. Moreover, PpIX accumulation was promoted and the hemin content was decreased by the silence of IGHG1. Interference of IGHG1 reduced the phosphorylated extracellular signal-regulated kinase (ERK) and ferrochelatase (FECH) expression, resulting in retarded cell proliferation in an MEK-FECH axis-dependent pathway.

The method aims to recognize and extract the characteristic parameters of bridge cracks based on images of the cracks obtained through the application of preprocessing technologies, such as graying, graphical enhancements, spatial filtering, gray-level threshold segmentation, etc.. The approach has been tested for accuracy to avoid the incorrect identification of chaff as a method error. The proposed method has proved to be rather accurate and effective in extracting information on cracks from the bridge image tests.

The method aims to recognize and extract the characteristic parameters of bridge cracks based on images of the cracks obtained through the application of preprocessing technologies, such as graying, graphical enhancements, spatial filtering, gray-level threshold segmentation, etc.. The approach has been tested for accuracy to avoid the incorrect identification of chaff as a method error. The proposed method has proved to be rather accurate and effective in extracting information on cracks from the bridge image tests.