Short-chain fatty acids reprogram metabolic profiles with the induction of reactive oxygen species production in human colorectal adenocarcinoma cells.

Short-chain fatty acids (SCFAs) exhibit anticancer activity in cellular and animal models of colon cancer. Acetate, propionate, and butyrate are the three major SCFAs produced from dietary fiber by gut microbiota fermentation and have beneficial effects on human health. Most previous studies on the antitumor mechanisms of SCFAs have focused on specific metabolites or genes involved in antitumor pathways, such as reactive oxygen species (ROS) biosynthesis. In this study, we performed a systematic and unbiased analysis of the effects of acetate, propionate, and butyrate on ROS levels and metabolic and transcriptomic signatures at physiological concentrations in human colorectal adenocarcinoma cells. We observed significantly elevated levels of ROS in the treated cells. Furthermore, significantly regulated signatures were involved in overlapping pathways at metabolic and transcriptomic levels, including ROS response and metabolism, fatty acid transport and metabolism, glucose response and metabolism, mitochondrial transport and respiratory chain complex, one-carbon metabolism, amino acid transport and metabolism, and glutaminolysis, which are directly or indirectly linked to ROS production. Additionally, metabolic and transcriptomic regulation occurred in a SCFAs types-dependent manner, with an increasing degree from acetate to propionate and then to butyrate. This study provides a comprehensive analysis of how SCFAs induce ROS production and modulate metabolic and transcriptomic levels in colon cancer cells, which is vital for understanding the mechanisms of the effects of SCFAs on antitumor activity in colon cancer.

Retrospective motion correction for preclinical/clinical magnetic resonance imaging based on a conditional generative adversarial network with entropy loss.

Multishot scan magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving the image quality in MRI. This work proposes and validates a new end-to-end motion-correction method for the multishot sequence that incorporates a conditional generative adversarial network with minimum entropy (cGANME) of MR images. The cGANME contains an encoder-decoder generator to obtain motion-corrected images and a PatchGAN discriminator to classify the image as either real (motion-free) or fake (motion-corrected). The entropy of the images is set as one loss item in the cGAN's loss as the entropy increases monotonically with the motion artifacts. An ablation experiment of the different weights of entropy loss was performed to evaluate the function of entropy loss. The preclinical dataset was acquired with a fast spin echo pulse sequence on a 7.0-T scanner. After the simulation, we had 10,080/2880/1440 slices for training, testing, and validating, respectively. The clinical dataset was downloaded from the Human Connection Project website, and 11,300/3500/2000 slices were used for training, testing, and validating after simulation, respectively. Extensive experiments for different motion patterns, motion levels, and protocol parameters demonstrate that cGANME outperforms traditional and some state-of-the-art, deep learning-based methods. In addition, we tested cGANME on in vivo awake rats and mitigated the motion artifacts, indicating that the model has some generalizability.

Robust water-fat separation based on deep learning model exploring multi-echo nature of mGRE.

PURPOSE: To design a new deep learning network for fast and accurate water-fat separation by exploring the correlations between multiple echoes in multi-echo gradient-recalled echo (mGRE) sequence and evaluate the generalization capabilities of the network for different echo times, field inhomogeneities, and imaging regions. METHODS: A new multi-echo bidirectional convolutional residual network (MEBCRN) was designed to separate water and fat images in a fast and accurate manner for the mGRE data. This new MEBCRN network contains 2 main modules, the first 1 is the feature extraction module, which learns the correlations between consecutive echoes, and the other one is the water-fat separation module that processes the feature information extracted from the feature extraction module. The multi-layer feature fusion (MLFF) mechanism and residual structure were adopted in the water-fat separation module to increase separation accuracy and robustness. Moreover, we trained the network using in vivo abdomen images and tested it on the abdomen, knee, and wrist images. RESULTS: The results showed that the proposed network could separate water and fat images accurately. The comparison of the proposed network and other deep learning methods shows the advantage in both quantitative metrics and robustness for different TEs, field inhomogeneities, and images acquired for various imaging regions. CONCLUSION: The proposed network could learn the correlations between consecutive echoes and separate water and fat images effectively. The deep learning method has certain generalization capabilities for TEs and field inhomogeneity. Although the network was trained only in vivo abdomen images, it could be applied for different imaging regions.