This study was intended to identify the shifts in the metabolomics profile of the hepatic tissue damaged by alcohol consumption and verify the potential restorative action of flos carthami (the flowers of Carthamus tinctorius, FC) in the protection of alcohol-induced injury by attenuating the level of identified metabolites. Rats were treated with FC and subsequently subjected to alcohol administration. The serum samples were subjected to liquid chromatography-mass spectrometry (LC-MS)-based metabolomics followed by statistical and bioinformatics analyses. The clustering of the samples showed an obvious separation in the principal component analysis (PCA) plot, and the scores plot of the orthogonal partial least squares-discriminant analysis (OPLS-DA) model allowed the distinction among the three groups. Among the 3211 total metabolites, 1088 features were significantly different between the control and alcohol-treated groups, while 367 metabolites were identified as differential metabolites between the alcohol- and FC-treated rat groups. Time series clustering approach indicated that 910 metabolites in profile 6 were upregulated by alcohol but subsequently reversed by FC treatment; among them, the top 10 metabolites based on the variable importance in projection (VIP) scores were 1-methyladenine, phenylglyoxylic acid, N-acetylvaline, mexiletine, L-fucose, propylthiouracil, dopamine 4-sulfate, isoleucylproline, (R)-salsolinol, and monomethyl phthalate. The Pearson correlation analysis and network construction revealed 96 hub metabolites that were upregulated in the alcohol liver injury model group but were downregulated by FC. This study confirmed the hepatoprotective effects of FC against alcohol-induced liver injury and the related changes in the metabolic profiles, which will contribute to the understanding and the treatment of alcohol-induced acute liver injury.

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.