Watermelon Flesh-Like Ni3 S2 @C Composite Separator with Polysulfide Shuttle Inhibition for High-Performance Lithium-Sulfur Batteries.

The shuttle effect limits the practical application of lithium-sulfur (Li-S) batteries with high specific capacity and cheap price. Herein, a three-dimensional carbon substrate containing Ni3 S2 nanoparticles is created to modify the separator. The in situ optical visualization battery proves that the material can realize the rapid conversion of Li2 S6 . Moreover, the impact of lithium-ion diffusion on the reactions in the cell is investigated, and the mechanism of Ni3 S2 @C in the cell is proposed based on the "adsorption-diffusion-conversion" mechanism. The "adsorption-diffusion-conversion" process of polysulfide is carried out on the surface of the composite separator, showing positive effects on the inhibition of polysulfide shuttle and the promotion of conversion. The separator is modified to improve sulfur utilization and reduce dead sulfur accumulation through a strategy of chemical immobilization and physical blocking. This helps to bridge the existing gaps of Li-S batteries.

Therapeutic potential and mechanisms of mesenchymal stem cell-derived exosomes as bioactive materials in tendon-bone healing.

Tendon-bone insertion (TBI) injuries, such as anterior cruciate ligament injury and rotator cuff injury, are the most common soft tissue injuries. In most situations, surgical tendon/ligament reconstruction is necessary for treating such injuries. However, a significant number of cases failed because healing of the enthesis occurs through scar tissue formation rather than the regeneration of transitional tissue. In recent years, the therapeutic potential of mesenchymal stem cells (MSCs) has been well documented in animal and clinical studies, such as chronic paraplegia, non-ischemic heart failure, and osteoarthritis of the knee. MSCs are multipotent stem cells, which have self-renewability and the ability to differentiate into a wide variety of cells such as chondrocytes, osteoblasts, and adipocytes. Numerous studies have suggested that MSCs could promote angiogenesis and cell proliferation, reduce inflammation, and produce a large number of bioactive molecules involved in the repair. These effects are likely mediated by the paracrine mechanisms of MSCs, particularly through the release of exosomes. Exosomes, nano-sized extracellular vesicles (EVs) with a lipid bilayer and a membrane structure, are naturally released by various cell types. They play an essential role in intercellular communication by transferring bioactive lipids, proteins, and nucleic acids, such as mRNAs and miRNAs, between cells to influence the physiological and pathological processes of recipient cells. Exosomes have been shown to facilitate tissue repair and regeneration. Herein, we discuss the prospective applications of MSC-derived exosomes in TBI injuries. We also review the roles of MSC-EVs and the underlying mechanisms of their effects on promoting tendon-bone healing. At last, we discuss the present challenges and future research directions.

A simulation study of 1D U-Net-based inter-crystal scatter event recovery of PET detectors.

To achieve high spatial resolution of reconstructed images in positron emission tomography (PET), the size of the scintillation crystal element is set small in current PET systems, which greatly increases the inter-crystal scattering (ICS) frequency. The ICS is a type of Compton scattering of the gamma photons from one crystal element to its neighborhood element, which obscures the determination of the first interaction position. In this study, we propose a 1D U-Net convolutional neural network to predict the first interaction position, which provides a universal way to efficiently solve the ICS recovery problem. The network is trained using the dataset collected from the GATE Monte Carlo simulation. The 1D U-Net structure is applied due to its capability of synthesizing both low-level and high-level information, which shows superiority in solving the ICS recovery problem. After being well trained, the 1D U-Net can generate a prediction accuracy of 78.1%. Compared to the coincidence events only composed from two photoelectric gamma photons, the sensitivity is improved by 149%. The contrast-to-noise ratio of the reconstructed contrast phantom increases from 6.973 to 10.795 for the 16 mm hot sphere. Compared to the take-energy-centroid method, the spatial resolution of the reconstructed resolution phantom can obtain the best improvement of 33.46%. Compared with the previous deep learning method based on the fully connected network, the proposed 1D U-Net can work more stably with considerably fewer network parameters. The 1D U-Net network model shows good universality when predicting different phantoms, and the computation speed is fast.

Hypoglycemic Effect of Nobiletin via Gut Microbiota-Metabolism Axis on Hyperglycemic Mice.

SCOPE: Prediabetes and diabetes are major public health problems worldwide without specific cure currently. Gut microbes have been recognized as one of the vital therapeutic targets for diabetes. The exploration that nobiletin (NOB) whether affects gut microbes provides a scientific basis for its application. METHODS AND RESULTS: A hyperglycemia animal model is established using high-fat-fed ApoE-/- mice. After 24 weeks of NOB intervention, the level of fasting blood glucose (FBG), glucose tolerance, insulin resistance, and glycosylated serum protein (GSP) are measured. Pancreas integrity is observed by hematoxylin-eosin (HE) staining and transmission electron microscopy. 16s RNA sequencing and untargeted metabolomics are to determine the changes of intestinal microbial composition and metabolic pathways. The levels of FBG and GSP in hyperglycemic mice are effectively reduced. The secretory function of pancreas is improved. Meanwhile, NOB treatment restored the gut microbial composition and affected metabolic function. Furthermore, NOB treatment regulates the metabolic disorder mainly through lipid metabolism, amino acid metabolism, and Secondary bile acid metabolism, etc. In addition, it is possibly existed mutual promotion between microbe and metabolites. CONCLUSION: NOB probably plays a vital role in the hypoglycemic effect and pancreatic islets protection by improving microbiota composition and gut metabolism.

Hypoglycemic Effect of Nobiletin via Regulation of Islet ß-Cell Mitophagy and Gut Microbiota Homeostasis in Streptozocin-Challenged Mice.

Nobiletin is a natural nutrient (or polymethoxyflavonoid) in orange peels exerting a preventive effect against metabolic diseases. However, there are very few reports on the hypoglycemic effect of nobiletin. In the present study, the hypoglycemic effect of nobiletin was investigated using NIT-1 cells and streptozocin (STZ)-challenged mouse models. Our results indicated that nobiletin could significantly suppress the high blood glucose in STZ-challenged mice. In addition, nobiletin could effectively activate the mitophagy and inhibit the inflammatory pathways in NIT-1 cells. The mitochondria membrane potential dysbiosis induced by glucotoxicity in NIT-1 cells was restored after treatment by nobiletin. Further investigation revealed that the hypoglycemic effect of nobiletin was mainly through regulation of gut microbiota dysbiosis, activation of mitophagy flux, inhibition of inflammasome expression, and restoration of islet morphological destruction in the pancreas of STZ-challenged mice. Our study revealed that nobiletin could be used as a functional food or drug candidate for the treatment of diabetes.

Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. MATERIALS AND METHODS: In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups. RESULTS: We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. CONCLUSION: The signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.

A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma.

AIM: Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions. METHODS: survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score. RESULTS: We identified a total of 1489 genes significantly correlated with patients' prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls. CONCLUSIONS: The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.