Atherosclerosis(As)is a chronic inflammatory disease associated with lipid deposition.Copper is con-sidered to be an important trace element and is closely related to the occurrence and development of As.Excessive accu-mulation of copper ions in cells can induce cell death,a new type of cell death named"cuproptosis".Under normal con-ditions,the body's copper metabolism can control the copper level in a stable range.When the disease occurs,copper ho-meostasis is destroyed,intracellular copper overload produces cytotoxicity,induces oxidative stress,inflammation,cell py-roptosis and cuproptosis,and promotes the occurrence and development of As.This article summarizes the relationship between copper levels and As,and discusses the mechanism of cuproptosis and the pathological mechanism of copper over-load promoting As from the perspective of the body's copper regulation,and reviews the relevant drug intervention,expec-ting to provide a new therapeutic target for As.

Objective The fully automatic segmentation of glioma and its subregions is fundamental for computer-aided clinical diagnosis of tumors.In the segmentation process of brain magnetic resonance imaging(MRI),convolutional neural networks with small convolutional kernels can only capture local features and are ineffective at integrating global features,which narrows the receptive field and leads to insufficient segmentation accuracy.This study aims to use dilated convolution to address the problem of inadequate global feature extraction in 3D-UNet.Methods 1)Algorithm construction:A 3D-UNet model with three pathways for more global contextual feature extraction,or 3DGE-UNet,was proposed in the paper.By using publicly available datasets from the Brain Tumor Segmentation Challenge(BraTS)of 2019(335 patient cases),a global contextual feature extraction(GE)module was designed.This module was integrated at the first,second,and third skip connections of the 3D UNet network.The module was utilized to fully extract global features at different scales from the images.The global features thus extracted were then overlaid with the upsampled feature maps to expand the model's receptive field and achieve deep fusion of features at different scales,thereby facilitating end-to-end automatic segmentation of brain tumors.2)Algorithm validation:The image data were sourced from the BraTs 2019 dataset,which included the preoperative MRI images of 335 patients across four modalities(T1,T1ce,T2,and FLAIR)and a tumor image with annotations made by physicians.The dataset was divided into the training,the validation,and the testing sets at an 8∶1∶1 ratio.Physician-labelled tumor images were used as the gold standard.Then,the algorithm's segmentation performance on the whole tumor(WT),tumor core(TC),and enhancing tumor(ET)was evaluated in the test set using the Dice coefficient(for overall effectiveness evaluation),sensitivity(detection rate of lesion areas),and 95%Hausdorff distance(segmentation accuracy of tumor boundaries).The performance was tested using both the 3D-UNet model without the GE module and the 3DGE-UNet model with the GE module to internally validate the effectiveness of the GE module setup.Additionally,the performance indicators were evaluated using the 3DGE-UNet model,ResUNet,UNet++,nnUNet,and UNETR,and the convergence of these five algorithm models was compared to externally validate the effectiveness of the 3DGE-UNet model.Results 1)In internal validation,the enhanced 3DGE-UNet model achieved Dice mean values of 91.47%,87.14%,and 83.35%for segmenting the WT,TC,and ET regions in the test set,respectively,producing the optimal values for comprehensive evaluation.These scores were superior to the corresponding scores of the traditional 3D-UNet model,which were 89.79%,85.13%,and 80.90%,indicating a significant improvement in segmentation accuracy across all three regions(P＜0.05).Compared with the 3D-UNet model,the 3DGE-UNet model demonstrated higher sensitivity for ET(86.46%vs.80.77%)(P＜0.05),demonstrating better performance in the detection of all the lesion areas.When dealing with lesion areas,the 3DGE-UNet model tended to correctly identify and capture the positive areas in a more comprehensive way,thereby effectively reducing the likelihood of missed diagnoses.The 3DGE-UNet model also exhibited exceptional performance in segmenting the edges of WT,producing a mean 95%Hausdorff distance superior to that of the 3D-UNet model(8.17 mm vs.13.61 mm,P＜0.05).However,its performance for TC(8.73 mm vs.7.47 mm)and ET(6.21 mm vs.5.45 mm)was similar to that of the 3D-UNet model.2)In the external validation,the other four algorithms outperformed the 3DGE-UNet model only in the mean Dice for TC(87.25%),the mean sensitivity for WT(94.59%),the mean sensitivity for TC(86.98%),and the mean 95%Hausdorff distance for ET(5.37 mm).Nonetheless,these differences were not statistically significant(P＞0.05).The 3DGE-UNet model demonstrated rapid convergence during the training phase,outpacing the other external models.Conclusion The 3DGE-UNet model can effectively extract and fuse feature information on different scales,improving the accuracy of brain tumor segmentation.

Gut microbiota dysbiosis is an avenue for the promotion of atherosclerosis (AS) and this effect is mediated partly via the circulating microbial metabolites. More microbial metabolites related to AS vascular inflammation, and the mechanisms involved need to be clarified urgently. Paeonol (Pae) is an active compound isolated from Paeonia suffruticoas Andr. with anti-AS inflammation effect. However, considering the low oral bioavailability of Pae, it is worth exploring the mechanism by which Pae reduces the harmful metabolites of the gut microbiota to alleviate AS. In this study, ApoE^-/- mice were fed a high-fat diet (HFD) to establish an AS model. AS mice were administrated with Pae (200 or 400 mg·kg^-1) by oral gavage and fecal microbiota transplantation (FMT) was conducted. 16S rDNA sequencing was performed to investigate the composition of the gut microbiota, while metabolomics analysis was used to identify the metabolites in serum and cecal contents. The results indicated that Pae significantly improved AS by regulating gut microbiota composition and microbiota metabolic profile in AS mice. We also identified α-hydroxyisobutyric acid (HIBA) as a harmful microbial metabolite reduced by Pae. HIBA supplementation in drinking water promoted AS inflammation in AS mice. Furthermore, vascular endothelial cells (VECs) were cultured and stimulated by HIBA. We verified that HIBA stimulation increased intracellular ROS levels, thereby inducing VEC inflammation via the TXNIP/NLRP3 pathway. In sum, Pae reduces the production of the microbial metabolite HIBA, thus alleviating the ROS/TXNIP/NLRP3 pathway-mediated endothelial inflammation in AS. Our study innovatively confirms the mechanism by which Pae reduces the harmful metabolites of gut microbiota to alleviate AS and proposes HIBA as a potential biomarker for AS clinical judgment.
Animals ; Mice ; Atherosclerosis/drug therapy* ; Diet, High-Fat ; Endothelial Cells ; Inflammation/drug therapy* ; Mice, Inbred C57BL ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Reactive Oxygen Species

Basic research in life science and medicine has dug into single cell level in recent years. Single-cell analysis offers to understand life from diverse perspectives and is used to profile cell heterogeneity to investigate mechanism of diseases. Single cell technologies have also found applications in forensic medicine and clinical reproductive medicine, while the techniques are rapidly evolving and have become more and more sophisticated. In this article, we reviewed various single cell isolation techniques and their pros and cons, including manual cell picking, laser capture microdissection and microfluidics, as well as analysis methods for DNA, RNA and protein in single cell. In addition, we summarized major up-to-date single cell research achievements and their potential applications.
Animals ; Cell Separation ; DNA ; Laser Capture Microdissection ; RNA ; Single-Cell Analysis