Background: The aim of this study was to develop an intelligent system based on a deep learning algorithm for automatically diagnosing fungal keratitis (FK) in in vivo confocal microscopy (IVCM) images.Methods: A total of 2,088 IVCM images were included in the training dataset. The positive group consisted of 688 images with fungal hyphae, and the negative group included 1,400 images without fungal hyphae. A total of 535 images in the testing dataset were not included in the training dataset. Deep Residual Learning for Image Recognition (ResNet) was used to build the intelligent system for diagnosing FK automatically. The system was verified by external validation in the testing dataset using the area under the receiver operating characteristic curve (AUC), accuracy, specificity and sensitivity.Results: In the testing dataset, 515 images were diagnosed correctly and 20 were misdiagnosed (including 6 with fungal hyphae and 14 without). The system achieved an AUC of 0.9875 with an accuracy of 0.9626 in detecting fungal hyphae. The sensitivity of the system was 0.9186, with a specificity of 0.9834. When 349 diabetic patients were included in the training dataset, 501 images were diagnosed correctly and thirtyfour were misdiagnosed (including 4 with fungal hyphae and 30 without). The AUC of the system was 0.9769.The accuracy, specificity and sensitivity were 0.9364, 0.9889 and 0.8256, respectively.
Conclusions:The intelligent system based on a deep learning algorithm exhibited satisfactory diagnostic performance and effectively classified FK in various IVCM images. The context of this deep learning automated diagnostic system can be extended to other types of keratitis.
Recent studies have reported the anticancer activity of huaier extract in various human malignancies. However, little is known about the effect of huaier extract in non‐small cell lung cancer (NSCLC) and its underlying mechanism. The current study aimed to investigate whether huaier extract affects the progression of NSCLC. mRNA and proteins expression of pyroptotic‐related genes (NLRP3, caspase‐1, IL‐1β, and IL‐18) in NSCLC tissues and cells were, respectively, detected by qRT‐PCR and western blot. The effects of huaier extract on NSCLC cell viability and cytotoxicity were evaluated by CCK‐8 assay, colony formation assay, and LDH detection kit. Besides, we established a xenograft model to assess the antitumor effect of huaier extract on tumor growth in vivo. Our results showed that the expression of pyroptotic‐related genes was downregulated in NSCLC tissues and cell lines. Huaier extract pretreatment inhibited cell viability and the percentage of colony formation of H520 and H358 cells, and upregulated the expression of pyroptotic‐related genes. Mechanistically, huaier extract exhibited antitumor effect in NSCLC via inducing NLRP3‐dependent pyroptosis in vitro and in vivo. In conclusion, our finding confirmed that huaier extract played an antitumor role in NSCLC progression through promoting pyroptotic cell death, which provided a new potential strategy for NSCLC clinical treatment.
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