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Additional file 1: Figure S1. NLRP3 is upregulated in HBE while remains unchanged in A549 and H446. (A) Western blotting showing the protein level of NLRP3, CASP1 cleavage and GSDMD cleavage were increased after radiation in HBE (n = 3). (B) Western blotting showing the protein level of NLRP3 is not upregulated in A549 and H446 (n = 3). Figure S2. ELISA results of IL-18 in supernatant of BEAS-2B, H446, A549 and H460 at different time point after radiation (n = 3). Figure S3. qRT-PCR showing the increase of mRNA level of NLRP3, CASP1, IL-1β and GSDMD expression after radiation were inhibited in BEAS-2B transfected with NLRP3 siRNA (n = 3). Bar graphs show the mean ± SEM; ***P < 0.001, ****P < 0.0001. SEM: standard error of mean; ns: not significant. Figure S4. MCC950 inhibits the proliferation of lung cancer cells. (A) CCK-8 assay was performed to measure the half-maximal inhibitory concentration (IC50) of MCC950 (n = 3). (B) MCC950 significantly decreased the colony formation ability of H446 and A549 (n = 3). (C) MCC950 inhibited the proliferation ability of H446 and A549 (n = 3). Bar graphs show the mean ± SEM; **P < 0.01. SEM: standard error of mean. Figure S5. Radiation induced intracellular ROS accumulation triggers NLRP3 inflammasome activation in HBE. (A) Flow cytometry of the intracellular ROS level at 6h post radiation pretreated with or without NAC in HBE (n = 3). (B) Western blotting showing the protein level of NLRP3, ASC, CASP1 cleavage and GSDMD cleavage were inhibited pretreated with NAC after radiation in HBE (n = 3). Bar graphs show the mean ± SEM. **P < 0.01; ***P < 0.001; ****P < 0.0001. ROS: reactive oxygen species; NAC: N-acetyl-l-cysteine; SEM: standard error of mean. Figure S6. The ROS and DPYSL4 levels are increased in irradiated lung tissue of mice. (A) The ROS levels were increased in RILI (n = 5). (B) Representative images of IHC staining for DPYSL4 in lung tissue after radiation (n = 5). Bar graphs show the mean ± SEM. ****P < 0.0001. ROS: reactive oxygen species; SEM: standard error of mean. Figure S7. IL-1β promotes the proliferation, migration, and activation of MEF. (A) Microscopy images of HLF and MEF. Scale bar, 100 μm. (B) Western blotting showing the protein level of COL1A1, α-SMA, TIMP-1 and MMP-3 in MEF after IL-1β treatment for 24h (n = 3). (C) Representative microscopy images of transwell assay showing IL-1β promoted the migration of MEF (n = 3). (D) EdU assay showing IL-1β promoted the proliferation of MEF (n = 3). HLF: primary human fibroblast; MEF: primary mouse embryonic fibroblast. Figure S8. Gel electrophoresis was performed to confirm the knockout of IL1R of mice (n = 3). H2O, no template control group. B6, C57BL/6N wild type mice. 12&13, IL1R−/− mice.

Additional file 2: Table S1. The primer sequences used in qRT-PCR. Table S2. Primer sequence of IL1R mutant and wild type.

Background. Artificial Intelligence (AI) is an advanced technology for the latest 20 years. Machine learning (ML) and deep learning (DL) are the major innovations for AI, which has been applied for multiple fields. Ophthalmology has become to be one of the most significant disciplines for human healthcare. Methodology. This study utilizes methods of text mining and bibliometric analysis to explore applications of AI to ophthalmology. 179 related articles from Web of Science (WOS) and 96 papers from China National Knowledge Infrastructure (CNKI) are explored during the period of 2000 to 2021. A descriptive analysis of major trends, journal releasing, topic mapping and quotation relationships is implemented in this paper. Leading authors, journals, institutions, nations and references in the related research are identified. Results. Findings show that the application of AI technologies in ophthalmologic diagnosis with optical coherence tomography (OCT) fundus images is the core topic for this area’s studies, especially for diabetic retinopathy (DR), aged macular degeneration (AMD) and glaucoma. It is also be predicted as the core direction over the recent years. Besides, The USA, England and China is the most competitive countries in this scientific filed. Journals of Ophthalmology, Investigative Ophthalmology and Visual Science, Eye, Acta Ophthalmologica and Scientific Reports are the top five journal related to the research area. There is a significant difference between WOS and CNKI databases pertaining to the application of Artificial Intelligence (AI) to ophthalmology, especially for the historic development, topic mapping and discipline category. Finally, the potential academic value of interdisciplinary subject of “AI in Ophthalmology” and tradition Chinese medicine (TRM) is discussed. Limitations and suggestions for the future research is indicated at the end of this paper.

Macula fovea detection is a crucial prerequisite towards screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.