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High-fat diet (HFD) decreases insulin sensitivity. How high-fat diet causes insulin resistance is largely unknown. Here, we show that lean mice become insulin resistant after being administered exosomes isolated from the feces of obese mice fed a HFD or from patients with type II diabetes. HFD altered the lipid composition of exosomes from predominantly phosphatidylethanolamine (PE) in exosomes from lean animals (L-Exo) to phosphatidylcholine (PC) in exosomes from obese animals (H-Exo). Mechanistically, we show that intestinal H-Exo is taken up by macrophages and hepatocytes, leading to inhibition of the insulin signaling pathway. Moreover, exosome-derived PC binds to and activates AhR, leading to inhibition of the expression of genes essential for activation of the insulin signaling pathway, including IRS-2, and its downstream genes PI3K and Akt. Together, our results reveal HFD-induced exosomes as potential contributors to the development of insulin resistance. Intestinal exosomes thus have potential as broad therapeutic targets. High-fat diet plays a role in development of insulin resistance. Here, the authors report a mechanism that underlies the development of diet induced insulin resistance through the activation of an aryl hydrocarbon receptor mediated signalling pathway in the liver by faecal exosomes derived from intestinal cells.

Background Extracellular communication within the tumor microenvironment plays a critical role in tumor progression. Although exosomes can package into long non-coding RNAs (lncRNAs) to mediate extracellular communication, the role of exosomal lncRNA PTENP1 in bladder cancer (BC) remains unclear. Method We detected PTENP1 expression between patients with BC and healthy controls; the expression occurred in tissues and exosomes from plasma. We assessed the diagnostic accuracy by the receiver operating characteristic curve (ROC) and the area under curve (AUC). Cell phenotypes and animal experiments were performed to determine the effect of exosomal PTENP1 . Results PTENP1 was significantly reduced in BC tissues and in exosomes from plasma of patients with BC ( P  < 0.05). We found that PTENP1 was mainly wrapped by exosomes. Exosomal PTENP1 could distinguish patients with BC from healthy controls (AUC = 0.743; 95% confidence interval (CI) = 0.645–0.840). Normal cells secreted exosomal PTENP1 and transmitted it to BC cells, thus inhibiting the biological malignant behavior of BC cells by increasing cell apoptosis and reducing the ability to invade and migrate ( P  < 0.05). Exosomal PTENP1 could suppress tumor growth in vivo. Furthermore, exosomal PTENP1 mediated the expression of PTEN by competitively binding to microRNA-17. Conclusion Exosomal PTENP1 is a promising novel biomarker that can be used for the clinical detection of BC. Exosomes derived from normal cells transfer PTENP1 to BC cells, which reduce the progression of BC both in vitro and in vivo and suggest that exosomal PTENP1 participates in normal-cell-to-bladder-cell communication during the carcinogenesis of BC.

Understanding two‐phase flow in laminated sandstones is important for fluid migration control and operational strategy determination in underground energy and hydrology engineering projects. Digital core analysis provides unparalleled understanding of two‐phase flow in complex porous media, but the integration into field analytical workflow is obstructed by the huge computational burden and imaging limitations on a standard rock core. To address this challenge, we propose a novel pore‐scale rock‐typing and upscaling approach for fast computation of two‐phase flow properties on large three‐dimensional digital rock images that contain billions of voxels. Firstly, a heterogeneous rock sample is divided into several homogeneous rock types through data clustering of regional 3D morphological parameters, and their two‐phase flow properties are calculated from selected subsamples using in‐house pore‐network model. The capillary pressure and relative permeability curves of the full digital image are then estimated through quasi‐static modeling on the rock type distribution. The excellent agreement between the upscaling results and pore‐scale simulations on the full image has verified the effectiveness of this two‐phase flow upscaling strategy. With largely reduced computational demands and clearly defined lamination heterogeneity, this approach has demonstrated good potential in bridging the gap between pore‐scale and core‐scale fluid flow mechanisms. In addition, due to the laminated structural characteristics, we also find a significant reduction in phase mobility over a range of saturations in the relative permeability curves of this highly permeable rock sample.

Extensive research indicates that tumor stemness promotes tumor progression. Nonetheless, the underlying roles of stemness-related genes in renal clear cell carcinoma (ccRCC) are unclear. Data used in bioinformatics analysis were downloaded from The Cancer Genome Atlas (TCGA) database. Moreover, the R software, SPSS, and GraphPad Prism 8 were used for mapping and statistical analysis. First, the stemness index of each patient was quantified using a machine learning algorithm. Subsequently, the differentially expressed genes between high and low stemness index were identified as stemness-related genes. Based on these genes, a stable and effective prognostic model was identified to predict the overall survival of patients using a random forest algorithm (Training cohort; 1-year AUC: 0.67; 3-year AUC: 0.79; 5-year AUC: 0.73; Validation cohort; 1-year AUC: 0.66; 3-year AUC: 0.71; 5-year AUC: 0.7). The model genes comprised AC010973.2 , RNU6-125P , AP001209.2 , Z98885.1 , KDM5C-IT1, and AL021368.3 . Due to its highest importance evaluated by randomforst analysis, the AC010973.2 gene was selected for further research. In vitro experiments demonstrated that AC010973.2 is highly expressed in ccRCC tissue and cell lines. Meanwhile, its knockdown could significantly inhibit the proliferation of ccRCC cells based on colony formation and CCK8 assays. In summary, our findings reveal that the stemness-related gene AC01097.3 is closely associated with the survival of patients. Besides, it remarkably promotes cell proliferation in ccRCC, hence a novel potential therapeutic target.

Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC). First, we introduce the common centrality measures; second, we propose the densities Den1(v) and Den2(v) of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC), BC, subgraph centrality (SC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC). LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC.

Rational construction of broadband and strong visible-light-absorbing (BSVLA) earth-abundant complexes is of great importance for efficient and sustainable solar energy utilization. Herein, we explore a universal Cu(I) center to couple with multiple strong visible-light-absorbing antennas to break the energy level limitations of the current noble-metal complexes, resulting in the BSVLA nonprecious complex (Cu-3). Systematic investigations demonstrate that double “ping-pong” energy-transfer processes in Cu-3 involving resonance energy transfer and Dexter mechanism enable a BSVLA between 430 and 620 nm and an antenna-localized long-lived triplet state for efficient intermolecular electron/energy transfer. Impressively, Cu-3 exhibited an outstanding performance for both energy- and electron-transfer reactions. Pseudo-first-order rate constant of photooxidation of 1,5-dihydroxynaphthalene with Cu-3 can achieve a record value of 190.8 × 10−3 min−1 among the molecular catalytic systems, over 30 times higher than that with a noble-metal photosensitizer (PS) [Ru(bpy)₃]2+. These findings pave the way to develop BSVLA earth-abundant PSs for boosting photosynthesis.

Background & aims N 6 -methyladenosine (m 6 A) modification plays a critical role in progression of hepatocellular carcinoma (HCC), and aerobic glycolysis is a hallmark of cancer including HCC. However, the role of YTHDF3, one member of the core readers of the m 6 A pathway, in aerobic glycolysis and progression of HCC is still unclear. Methods Expression levels of YTHDF3 in carcinoma and surrounding tissues of HCC patients were evaluated by immunohistochemistry. Loss and gain-of-function experiments in vitro and in vivo were used to assess the effects of YTHDF3 on HCC cell proliferation, migration and invasion. The role of YTHDF3 in hepatocarcinogenesis was observed in a chemically induced HCC model with Ythdf3 −/− mice. Untargeted metabolomics and glucose metabolism phenotype assays were performed to evaluate relationship between YTHDF3 and glucose metabolism. The effect of YTHDF3 on PFKL was assessed by methylated RNA immunoprecipitation assays (MeRIP). Co-immunoprecipitation and immunofluorescence assays were performed to investigate the connection between YTHDF3 and PFKL. Results We found YTHDF3 expression was greatly upregulated in carcinoma tissues and it was correlated with poor prognosis of HCC patients. Gain-of-function and loss-of-function assays demonstrated YTHDF3 promoted proliferation, migration and invasion of HCC cells in vitro, and YTHDF3 knockdown inhibited xenograft tumor growth and lung metastasis of HCC cells in vivo. YTHDF3 knockout significantly suppressed hepatocarcinogenesis in chemically induced mice model. Mechanistically, YTHDF3 promoted aerobic glycolysis by promoting phosphofructokinase PFKL expression at both mRNA and protein levels. MeRIP assays showed YTHDF3 suppressed PFKL mRNA degradation via m 6 A modification. Surprisingly, PFKL positively regulated YTHDF3 protein expression, not as a glycolysis rate-limited enzyme, and PFKL knockdown effectively rescued the effects of YTHDF3 overexpression on proliferation, migration and invasion ability of Sk-Hep-1 and HepG2 cells. Notably, co-immunoprecipitation assays demonstrated PFKL interacted with YTHDF3 via EFTUD2, a core subunit of spliceosome involved in pre-mRNA splicing process, and ubiquitination assays showed PFKL could positively regulate YTHDF3 protein expression via inhibiting ubiquitination of YTHDF3 protein by EFTUD2. Conclusions our study uncovers the key role of YTHDF3 in HCC, characterizes a positive functional loop between YTHDF3 and phosphofructokinase PFKL in glucose metabolism of HCC, and suggests the connection between pre-mRNA splicing process and m 6 A modification.

Personal credit scoring is a challenging issue. In recent years, research has shown that machine learning has satisfactory performance in credit scoring. Because of the advantages of feature combination and feature selection, decision trees can match credit data which have high dimension and a complex correlation. Decision trees tend to overfitting yet. eXtreme Gradient Boosting is an advanced gradient enhanced tree that overcomes its shortcomings by integrating tree models. The structure of the model is determined by hyperparameters, which is aimed at the time-consuming and laborious problem of manual tuning, and the optimization method is employed for tuning. As particle swarm optimization describes the particle state and its motion law as continuous real numbers, the hyperparameter applicable to eXtreme Gradient Boosting can find its optimal value in the continuous search space. However, classical particle swarm optimization tends to fall into local optima. To solve this problem, this paper proposes an eXtreme Gradient Boosting credit scoring model that is based on adaptive particle swarm optimization. The swarm split, which is based on the clustering idea and two kinds of learning strategies, is employed to guide the particles to improve the diversity of the subswarms, in order to prevent the algorithm from falling into a local optimum. In the experiment, several traditional machine learning algorithms and popular ensemble learning classifiers, as well as four hyperparameter optimization methods (grid search, random search, tree-structured Parzen estimator, and particle swarm optimization), are considered for comparison. Experiments were performed with four credit datasets and seven KEEL benchmark datasets over five popular evaluation measures: accuracy, error rate (type I error and type II error), Brier score, and F1 score. Results demonstrate that the proposed model outperforms other models on average. Moreover, adaptive particle swarm optimization performs better than the other hyperparameter optimization strategies.

Ischemic stroke is an important public health problem. However, comprehensive data on its burden in aging populations is limited. The aim of this study is to provide an up-to-date assessment of the prevalence, incidence, mortality, disability-adjusted life years, and risk factors for ischemic stroke globally in adults aged 60 years and older from 1990 to 2021 based on population changes. The Global Burden of Diseases, Injuries, and Risk Factors Study 2021 served as the data source for this study. Average annual percentage changes were estimated over the study period to quantify temporal patterns and assess trends in age-standardized rates of the prevalence, incidence, mortality, and disability-adjusted life-years of ischemic stroke. The significant increase in the prevalence and incidence of ischemic stroke is mainly related to population ageing and the significant increase in the number of people over 60 years of age, with the significant increase in the population over 60 years of age being the main driving force, while epidemiological changes have had the opposite effect. Critically, using the entire age population for calculations will prompt us to underestimate the burden of ischemic stroke. The burden of ischemic stroke disease is highest in older men than in older women, and the age-standardized prevalence rates, incidence rates, mortality rates, and disability-adjusted life-years rates are 26-35% higher in men than in women. High-middle sociodemographic index and Sub-Saharan Africa regions suffer the heaviest burden. Ischemic stroke health inequities widen, with less developed regions bearing a heavier ischemic stroke burden and the disparity in that burden becoming more pronounced over time. Population aging is the primary driver of the growing burden of ischemic stroke. Our findings indicate that prevention and control of this disease remain critical public health challenges. Targeted interventions addressing modifiable risk factors could significantly reduce the global burden of ischemic stroke.

Over previous decades, many nature-inspired optimization algorithms (NIOAs) have been proposed and applied due to their importance and significance. Some survey studies have also been made to investigate NIOAs and their variants and applications. However, these comparative studies mainly focus on one single NIOA, and there lacks a comprehensive comparative and contrastive study of the existing NIOAs. To fill this gap, we spent a great effort to conduct this comprehensive survey. In this survey, more than 120 meta-heuristic algorithms have been collected and, among them, the most popular and common 11 NIOAs are selected. Their accuracy, stability, efficiency and parameter sensitivity are evaluated based on the 30 black-box optimization benchmarking (BBOB) functions. Furthermore, we apply the Friedman test and Nemenyi test to analyze the performance of the compared NIOAs. In this survey, we provide a unified formal description of the 11 NIOAs in order to compare their similarities and differences in depth and a systematic summarization of the challenging problems and research directions for the whole NIOAs field. This comparative study attempts to provide a broader perspective and meaningful enlightenment to understand NIOAs.