Effect of rotor spinning transfer channel modification on fiber orientation and yarn properties

The vortices are generated at the conventional transfer channel, having adverse effects on fiber configuration. In a former research, a conventional transfer channel was modified via rounding the transfer channel inlet corner and adding a bypass channel. The simulation results obtained with the modified transfer channel showed that the vortices were eliminated and the inlet air velocity increased. We present the impact of this modification on the yarn properties and fiber straightness. Four groups of yarn samples were spun using the conventional and modified spinning system. Yarn properties and fiber straightness along the rotor groove were evaluated. Results revealed that tenacities of the yarns spun on the modified system, increased in comparison to that of the conventionally spun yarns. The number of nearly straight fibers is increased by 25.55% by using the modified system, which was mainly attributed to the decrease in the number of fibers with trailing and leading hooks.Peer ReviewedPostprint (author's final draft

An Attention-based Graph Neural Network for Heterogeneous Structural Learning

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

Objective: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen for training compared to the end-to-end deep learning method. Results: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. Conclusion: This study provides an intelligent, reliable and robust MRS quantification. Significance: QNet is the first LLS quantification aided by deep learning

Maternal Diet Intervention Before Pregnancy Primes Offspring Lipid Metabolism in Liver

Nonalcoholic fatty liver disease (NAFLD) has a developmental origin and is influenced in utero. We aimed to evaluate if maternal diet intervention before pregnancy would be beneficial to reduce the risk of offspring NAFLD. In our study, female mice were either on a normal-fat diet (NF group), or a high-fat diet for 12 weeks and continued on this diet throughout pregnancy and lactation (HF group), or switched from HF-to-NF diet 1 week (H1N group), or 9 weeks (H9N group) before pregnancy. Compared with the NF offspring, the H1N and HF, but not the H9N offspring, displayed more severe hepatic steatosis and glucose intolerance. More specifically, an abnormal blood lipid panel was seen in the H1N offspring and abnormal hepatic free fatty acid composition was present in both the HF and H1N offspring, while the H9N offspring displayed both at normal levels. These physiological changes were associated with desensitized hepatic insulin/AKT signaling, increased expression of genes and proteins for de novo lipogenesis and cholesterol synthesis, decreased expression of genes and proteins for fatty acid oxidation, increased Pcsk9 expression, and hypoactivation of 5' AMP-activated protein kinase (AMPK) signaling in the HF and H1N offspring. However, these effects were completely or partially rescued in the H9N offspring. In summary, we found that early maternal diet intervention is effective in reducing the risk of offspring NAFLD caused by maternal HF diet. These findings provide significant support to develop effective diet intervention strategies and policies for prevention of obesity and NAFLD to promote optimal health outcomes for mothers and children

Celastrol alleviates diabetic vascular injury via Keap1/Nrf2-mediated anti-inflammation

Introduction: Celastrol (Cel) is a widely used main component of Chinese herbal medicine with strong anti-inflammatory, antiviral and antitumor activities. In the present study, we aimed to elucidate the cellular molecular protective mechanism of Cel against diabetes-induced inflammation and endothelial dysfunction.Methods: Type 2 diabetes (T2DM) was induced by db/db mice, and osmotic pumps containing Cel (100 μg/kg/day) were implanted intraperitoneally and were calibrated to release the drug for 28 days. In addition, human umbilical vein endothelial cells (HUVECs) were cultured in normal or high glucose and palmitic acid-containing (HG + PA) media in the presence or absence of Cel for 48 h.Results: Cel significantly ameliorated the hyperglycemia-induced abnormalities in nuclear factor (erythroid-derived 2)-like protein 2 (Nrf2) pathway activity and alleviated HG + PA-induced oxidative damage. However, the protective effect of Cel was almost completely abolished in HUVECs transfected with short hairpin (sh)RNA targeting Nrf2, but not by nonsense shRNA. Furthermore, HG + PA reduced the phosphorylation of AMP-activated protein kinase (AMPK), the autophagic degradation of p62/Kelch-like ECH-associated protein 1 (Keap1), and the nuclear localization of Nrf2. However, these catabolic pathways were inhibited by Cel treatment in HUVECs. In addition, compound C (AMPK inhibitors) and AAV9-sh-Nrf2 reduced Cel-induced Nrf2 activation and angiogenesis in db/db mice.Discussion: Taking these findings together, the endothelial protective effect of Cel in the presence of HG + PA may be at least in part attributed to its effects to reduce reactive oxygen species (ROS) and inflammation through p62/Keap1-mediated Nrf2 activation

TOB1 modulates neutrophil phenotypes to influence gastric cancer progression and immunotherapy efficacy

IntroductionThe ErbB-2.1(TOB1) signaling transducer protein is a tumor-suppressive protein that actively suppresses the malignant phenotype of gastric cancer cells. Yet, TOB1 negatively regulates the activation and growth of different immune cells. Understanding the expression and role of TOB1 in the gastric cancer immune environment is crucial to maximize its potential in targeted immunotherapy.MethodsThis study employed multiplex immunofluorescence analysis to precisely delineate and quantify the expression of TOB1 in immune cells within gastric cancer tissue microarrays. Univariate and multivariate Cox analyses were performed to assess the influence of clinical-pathological parameters, immune cells, TOB1, and double-positive cells on the prognosis of gastric cancer patients. Subsequent experiments included co-culture assays of si-TOB1-transfected neutrophils with AGS or HGC-27 cells, along with EdU, invasion, migration assays, and bioinformatics analyses, aimed at elucidating the mechanisms through which TOB1 in neutrophils impacts the prognosis of gastric cancer patients.ResultsWe remarkably revealed that TOB1 exhibits varying expression levels in both the nucleus (nTOB1) and cytoplasm (cTOB1) of diverse immune cell populations, including CD8+ T cells, CD66b+ neutrophils, FOXP3+ Tregs, CD20+ B cells, CD4+ T cells, and CD68+ macrophages within gastric cancer and paracancerous tissues. Significantly, TOB1 was notably concentrated in CD66b+ neutrophils. Survival analysis showed that a higher density of cTOB1/nTOB1+CD66b+ neutrophils was linked to a better prognosis. Subsequent experiments revealed that, following stimulation with the supernatant of tumor tissue culture, the levels of TOB1 protein and mRNA in neutrophils decreased, accompanied by enhanced apoptosis. HL-60 cells were successfully induced to neutrophil-like cells by DMSO. Neutrophils-like cells with attenuated TOB1 gene expression by si-TOB1 demonstrated heightened apoptosis, consequently fostering a malignant phenotype in AGS and HCG-27 cells upon co-cultivation. The subsequent analysis of the datasets from TCGA and TIMER2 revealed that patients with high levels of TOB1 combined neutrophils showed better immunotherapy response.DiscussionThis study significantly advances our comprehension of TOB1’s role within the immune microenvironment of gastric cancer, offering promising therapeutic targets for immunotherapy in this context

Screening and validation of atherosclerosis PAN-apoptotic immune-related genes based on single-cell sequencing

BackgroundCarotid atherosclerosis (CAS) is a complication of atherosclerosis (AS). PAN-optosome is an inflammatory programmed cell death pathway event regulated by the PAN-optosome complex. CAS’s PAN-optosome-related genes (PORGs) have yet to be studied. Hence, screening the PAN-optosome-related diagnostic genes for treating CAS was vital.MethodsWe introduced transcriptome data to screen out differentially expressed genes (DEGs) in CAS. Subsequently, WGCNA analysis was utilized to mine module genes about PANoptosis score. We performed differential expression analysis (CAS samples vs. standard samples) to obtain CAS-related differentially expressed genes at the single-cell level. Venn diagram was executed to identify PAN-optosome-related differential genes (POR-DEGs) associated with CAS. Further, LASSO regression and RF algorithm were implemented to were executed to build a diagnostic model. We additionally performed immune infiltration and gene set enrichment analysis (GSEA) based on diagnostic genes. We verified the accuracy of the model genes by single-cell nuclear sequencing and RT-qPCR validation of clinical samples, as well as in vitro cellular experiments.ResultsWe identified 785 DEGs associated with CAS. Then, 4296 module genes about PANoptosis score were obtained. We obtained the 7365 and 1631 CAS-related DEGs at the single-cell level, respectively. 67 POR-DEGs were retained Venn diagram. Subsequently, 4 PAN-optosome-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) were identified via machine learning. Cellular function tests on four genes showed that these genes have essential roles in maintaining arterial cell viability and resisting cellular senescence.ConclusionWe obtained four PANoptosis-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) associated with CAS, laying a theoretical foundation for treating CAS

Polydopamine nanoparticles for treatment of acute inflammation-induced injury

Nanotechnology-mediated anti-inflammatory therapy is emerging as a novel strategy for treatment of inflammation-induced injury. However, one of the main hurdles for these anti-inflammatory nano-drugs is their potential toxic side effects in vivo. Herein, we uncovered that polydopamine (PDA) nanoparticles with structure and chemical properties similar to melanin, a natural bio-polymer, displayed significant anti-inflammation therapeutic effect on acute inflammation-induced injury. PDA with enriched phenol groups functioned as a radical scavenger to eliminate reactive oxygen species (ROS) generated during inflammatory responses. As revealed by in vivo photoacoustic imaging with a H2O2-specific nanoprobe, PDA nanoparticles remarkably reduced intracellular ROS levels in murine macrophages challenged with either H2O2 or lipopolysaccharide (LPS). The anti-inflammatory capacity of PDA nanoparticles was further demonstrated in murine models of both acute peritonitis and acute lung injury (ALI), where diminished ROS generation, reduced proinflammatory cytokines, attenuated neutrophil infiltration, and alleviated lung tissue damage were observed in PDA-treated mice after a single dose of PDA treatment. Our work therefore presents the great promise of PDA nanoparticles as a biocompatible nano-drug for anti-inflammation therapy to treat acute inflammation-induced injury