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CA9 is a member of the carbonic anhydrases’ family, that is often expressed in cancer cells under hypoxic condition. However, the role of CA9 in the molecular mechanisms of tongue squamous cell carcinoma (TSCC) pathogenesis remains unclear. CA9 expression was analysed using the TCGA database, and its influence on survival was performed using Kaplan‐Meier, LASSO and COX regression analyses. The correlation between CA9 and immune infiltration was investigated by CIBERSORT and ESTIMATE. Moreover, the relationship between CA9 expression and downstream molecular regulation pathways was analysed by GSEA, GO and WGCNA. CA9 expression correlated with clinical prognosis and tumour grade in TSCC. Moreover, CA9 expression potentially contributes to the regulation of cancer cell differentiation and mediates tumour‐associated genes and signalling pathways, including apoptosis, hypoxia, G2M checkpoint, PI3K/AKR/mTOR signalling and TGF‐beta signalling pathways. However, the follicular helper T cells, regulatory T cells, immune and stromal scores showed no significance between high and low CA9 expression groups. These findings suggested that CA9 plays a critical role of TSCC prognosis and tumour grade. CA9 expression significantly correlated with the regulation of cell differentiation, various oncogenes and cancer‐associated pathways.

Defects in the optical lens directly affect the scattering properties of the optical lens and decrease the performance of the optical element. Although machine vision instead of manual detection has been widely valued, the feature fusion technique of series operation and edge detection cannot recognize low-contrast and multi-scale targets in the lens. To address these challenges, in this study, an improved YOLOv5-C3CA-SPPF network model is proposed to detect defects on the surface and inside of the lens. The hybrid module combining the coordinate attention and CSPNet (C3) is incorporated into YOLOv5-C3CA for improving the extraction of target feature information and detection accuracy. Furthermore, an SPPF features fusion module is inserted into the neck of the network model to improve the detection accuracy of the network. To enhance the performance of supervised learning algorithms, a dataset containing a total of 3800 images is created, more than 600 images for each type of defect samples. The outcome of the experiment manifests that the mean average precision (mAP) of the YOLOv5-C3CA-SPPF algorithm is 97.1%, and the detection speed FPS is 41 f/s. Contrast to the traditional lens surface defects detection algorithms, YOLOv5-C3CA-SPPF can detect the types of optical lens surface and inside defects more accurately and quickly, the experimental results show that the YOLOv5-C3CA-SPPF model for identifying optical lens defects has good generalizability and robustness, which is favorable for on-line quality automatic detection of optical lens defects and provide an important guarantee for the quality consistency of finished products.

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