Development of pericardial fat count images using a combination of three different deep-learning models

Rationale and Objectives: Pericardial fat (PF), the thoracic visceral fat surrounding the heart, promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. For evaluating PF, this study aimed to generate pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model. Materials and Methods: The data of 269 consecutive patients who underwent coronary computed tomography (CT) were reviewed. Patients with metal implants, pleural effusion, history of thoracic surgery, or that of malignancy were excluded. Thus, the data of 191 patients were used. PFCIs were generated from the projection of three-dimensional CT images, where fat accumulation was represented by a high pixel value. Three different deep-learning models, including CycleGAN, were combined in the proposed method to generate PFCIs from CXRs. A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method. To evaluate the image quality of the generated PFCIs, structural similarity index measure (SSIM), mean squared error (MSE), and mean absolute error (MAE) of (i) the PFCI generated using the proposed method and (ii) the PFCI generated using the single model were compared. Results: The mean SSIM, MSE, and MAE were as follows: 0.856, 0.0128, and 0.0357, respectively, for the proposed model; and 0.762, 0.0198, and 0.0504, respectively, for the single CycleGAN-based model. Conclusion: PFCIs generated from CXRs with the proposed model showed better performance than those with the single model. PFCI evaluation without CT may be possible with the proposed method

Additional file 2 of Role of DNA dioxygenase Ten-Eleven translocation 3 (TET3) in rheumatoid arthritis progression

Additional file 2: Supplementary Table S1. TET3-mediated upregulated genes. Supplementary Table S2. TET3-mediated downregulated genes. Supplementary Table S3. Results of functional enrichment analysis. Supplementary Table S4. KEGG pathway analysis. Supplementary Table S5. Demographic, clinical, and biochemical features of RA and OA patients whose synovial tissues were used in the experiments

Additional file 1 of Role of DNA dioxygenase Ten-Eleven translocation 3 (TET3) in rheumatoid arthritis progression

Additional file 1: Supplementary Figure S1. Degeneration of TET3 mRNA induced by TNFα stimulation. Relative mRNA expression levels of TET3 in RA FLS (n = 3) treated with actinomycin D (Wako, 10 μg/mL), followed by stimulation with TNFα for 0, 0.5, 1, 2, and 6 hrs. Supplementary Figure S2. Relative mRNA expression levels of TET1/2/3 with or without TET3-knockdown. (A) RA FLS (n = 2) samples were used to study TET3 mRNA levels by qPCR. Data are mean ± SEM. (B) RA FLS (n = 3) samples were used to study TET3 protein levels by Western blotting. Supplementary Figure S3. Heat map of differentially expressed genes in all RA FLS with or without TNFα stimulation and TET3-knockdown. 2013 of all 21,448 genes were differentially expressed genes in 4 RA FLS groups (ANOVA F-test, P < 0.05) and analyzed. Red corresponds to gene upregulation and blue to gene downregulation. Supplementary Figure S4. Cell proliferation of FLS by TET3 expression. RA FLS (n=3) were transfected with control or TET3 siRNAs. Cell numbers of FLS were counted with Hemocytometer at day 1, 3, and 7. P value by the t-test

Additional file 3 of Role of DNA dioxygenase Ten-Eleven translocation 3 (TET3) in rheumatoid arthritis progression

Additional file 3