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Background: Evidence from observational epidemiological studies indicated that rheumatoid arthritis (RA) increased the risk of heart failure (HF). However, there is a possibility that the correlation is not explained as a causative role for RA in the pathogenesis of HF. A two-sample Mendelian randomization (MR) framework was designed to explore the potential etiological role of RA in HF to identify the target to improve the burden of HF disease. Methods: To assess the causal association between RA and HF, we analyzed summary statistics from genome-wide association studies (GWASs) for individuals of European descent. Genetic instruments for RA were identified at a genome-wide significance threshold (p < 5 × 10–8). Corresponding data were obtained from a GWAS meta-analysis (95,524 cases and 1,270,968 controls) to identify genetic variants underlying HF. MR estimates were pooled using the inverse variance weighted method. Complementary analyses were conducted to assess the robustness of the results. Results: There was no evidence of a causal association between genetically predicted RA and HF [odds ratio (OR), 1.00; 95% confidence interval (CI), 0.99–1.02; P = 0.60]. Various sensitivity analyses suggested no pleiotropy detected (all p > 0.05). Conclusion: Our findings did not support the causal role of RA in the etiology of HF. As such, therapeutics targeted at the control of RA may have a lower likelihood of effectively controlling the occurrence of HF.

Background: Faced with the global threat posed by SARS-CoV-2 (COVID-19), low-dose Computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. And this can easily interfere with the radiologist's assessment. Convolutional Neural Networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising. Objective: Modified convolutional neural network algorithm to train the denoising model. Make the model to extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising. Methods: We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising. Result: According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions. Conclusion: The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set.