The relationship of peripheral blood lncRNA-PVT1 and miR-146a levels with Th17/Treg cytokines in patients with Hashimoto’s thyroiditis and their clinical significance

Hashimoto’s thyroiditis (HT) is a prevalent autoimmune disease. We investigated the relationship of peripheral blood long noncoding RNA-plasmacytoma variant translocation 1 (lncRNA-PVT1) and microRNA (miR)-146a levels with Th17/Treg-related cytokines in HT patients and their clinical significance. Correlations of PVT1 and miR-146a with Th17/Treg-related cytokines were analyzed, and its clinical value in diagnosing HT is assessed. Results showed reduced PVT1 and IL-10 levels and increased miR-146a and IL-17 levels in HT patients. PVT1 negatively interrelated with miR-146a, IL-17, IL-23 and IL-6, and positively interrelated with IL-10; miR-146a positively correlated with IL-17, IL-23 and IL-6, but negatively correlated with IL-10 in HT patients. The area under the curve (AUC) of PVT1 and miR-146a levels for diagnosing HT were 0.822 and 0.844, respectively (sensitivity 88.73% and 86.62%, specificity 67.02% and 69.15%, cut-off values 0.76 and 2.73), with their combined detections yielding a higher AUC. Patients with poorly-expressed PVT1 and highly-expressed miR-146a had elevated HT incidence. PVT1 and miR-146a levels were also found to be an independent influencing factor for HT occurrence. Our findings suggest that HT patients have low peripheral blood PVT1 expression and high miR-146a expression. PVT1 and miR-146a level changes were correlated with Th17/Treg cytokine imbalance and could be a potential diagnostic tool and independent influencing factor for HT

Cardiovascular mortality by cancer risk stratification in patients with localized prostate cancer: a SEER-based study

PurposeThe risk of cardiovascular disease (CVD) mortality in patients with localized prostate cancer (PCa) by risk stratification remains unclear. The aim of this study was to determine the risk of CVD death in patients with localized PCa by risk stratification.Patients and methodsPopulation-based study of 340,806 cases in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with localized PCa between 2004 and 2016. The proportion of deaths identifies the primary cause of death, the competing risk model identifies the interaction between CVD and PCa, and the standardized mortality rate (SMR) quantifies the risk of CVD death in patients with PCa.ResultsCVD-related death was the leading cause of death in patients with localized PCa, and cumulative CVD-related death also surpassed PCa almost as soon as PCa was diagnosed in the low- and intermediate-risk groups. However, in the high-risk group, CVD surpassed PCa approximately 90 months later. Patients with localized PCa have a higher risk of CVD-related death compared to the general population and the risk increases steadily with survival (SMR = 4.8, 95% CI 4.6–5.1 to SMR = 13.6, 95% CI 12.8–14.5).ConclusionsCVD-related death is a major competing risk in patients with localized PCa, and cumulative CVD mortality increases steadily with survival time and exceeds PCa in all three stratifications (low, intermediate, and high risk). Patients with localized PCa have a higher CVD-related death than the general population. Management of patients with localized PCa requires attention to both the primary cancer and CVD

Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model

In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, creating an urgent need to carry out risk warning based on local government hidden debt. The paper uses the macro indicators of local government implicit debt risk at the prefecture-level city level, and introduces the micro indicators of PPP projects, financing platform bank debt, and urban investment debt to establish a BP neural network model. We not only study the contagion effect of local government hidden debt on systemic financial risks, but also predict the systemic financial risks in 2019 and construct an early warning risk system based on the prefecture-level city data from 2015 to 2018. In addition, the early warning effect of local government implicit debt on systemic financial risk under different stress scenarios is investigated. The study found that the implicit debt risk of local governments, the scale of financing platform bank debt, the scale of PPP, and the scale of urban investment bonds have a significant impact on systemic financial risks. The neural network model constructed by introducing these four variables at the same time can better predict the level of systemic financial risk. The model can also accurately predict the changes in systemic financial risks under the stress test of the increase in hidden debt of different local governments, and has a good early warning effect

Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model

In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, creating an urgent need to carry out risk warning based on local government hidden debt. The paper uses the macro indicators of local government implicit debt risk at the prefecture-level city level, and introduces the micro indicators of PPP projects, financing platform bank debt, and urban investment debt to establish a BP neural network model. We not only study the contagion effect of local government hidden debt on systemic financial risks, but also predict the systemic financial risks in 2019 and construct an early warning risk system based on the prefecture-level city data from 2015 to 2018. In addition, the early warning effect of local government implicit debt on systemic financial risk under different stress scenarios is investigated. The study found that the implicit debt risk of local governments, the scale of financing platform bank debt, the scale of PPP, and the scale of urban investment bonds have a significant impact on systemic financial risks. The neural network model constructed by introducing these four variables at the same time can better predict the level of systemic financial risk. The model can also accurately predict the changes in systemic financial risks under the stress test of the increase in hidden debt of different local governments, and has a good early warning effect

Directional tensor product complex tight framelets for compressed sensing MRI reconstruction

Compressed sensing magnetic resonance imaging (CS-MRI) is an effective way of reducing the sampling data in the k-space and shortening the scanning time. Motivated by the high performance of directional tensor product complex tight framelets (TPCTFs) for the image denoising problem, the authors proposed a novel framework that integrated TPCTF for sparse representation and projected fast iterative soft-thresholding algorithm (pFISTA) for CS-MRI reconstruction. Furthermore, to take advantage of the cross-scale relations in the wavelet tree of frame coefficients, the bivariate shrinkage (BS) function with local variance estimation is proposed to shrink thresholding. Such TPCTFs can provide sparse directional representations very well for MR image. When compared with other the state-of-the-art CS-MRI algorithms in numerical experiments, the proposed TPCTF-BS method achieves a higher reconstruction quality with respect to image edge preservation and the artefact suppression

Supplementary information files for 'Synthesis and properties of stable sub-2-nm-thick aluminum nanosheets: Oxygen passivation and two-photon luminescence'

Supplementary information files for 'Synthesis and properties of stable sub-2-nm-thick aluminum nanosheets: Oxygen passivation and two-photon luminescence'Abstract:The high reductivity of aluminum implies the utmost difficulty in achieving oxygen resistant ultrathin Al nanostructures. Herein, we demonstrate that sub-2 nm thick Al nanosheets with ambient stability can be synthesized through a facile wet chemical approach. Selective oxygen adsorption on the (111) facets of the face-centered cubic (fcc) Al has been revealed as the reason of controlling the morphology and stability of Al nanosheets, tailoring the thickness from 18 nm down to 1.5 nm. Within the (111) surface passivation, Al nanosheets have achieved satisfactory stability which ensures the possibility to study thickness-dependent localized surface plasmon resonance from visible to the Near-IR region, and significantly enhanced two-photon luminescence. This work demonstrates, for the first time, the feasibility in obtaining stable ultrathin nanostructures of Al metal, which paves the way toward optical application of Al as a sustainable plasmonic material; it also shows the great potential of the controllable synthesis for investigation of other active metal- based nanomaterials.</div

Synthesis and properties of stable sub-2-nm-thick aluminum nanosheets: Oxygen passivation and two-photon luminescence

The high reductivity of aluminum implies the utmost difficulty in achieving oxygen resistant ultrathin Al nanostructures. Herein, we demonstrate that sub-2 nm thick Al nanosheets with ambient stability can be synthesized through a facile wet chemical approach. Selective oxygen adsorption on the (111) facets of the face-centered cubic (fcc) Al has been revealed as the reason of controlling the morphology and stability of Al nanosheets, tailoring the thickness from 18 nm down to 1.5 nm. Within the (111) surface passivation, Al nanosheets have achieved satisfactory stability which ensures the possibility to study thickness-dependent localized surface plasmon resonance from visible to the Near-IR region, and significantly enhanced two-photon luminescence. This work demonstrates, for the first time, the feasibility in obtaining stable ultrathin nanostructures of Al metal, which paves the way toward optical application of Al as a sustainable plasmonic material; it also shows the great potential of the controllable synthesis for investigation of other active metal- based nanomaterials.</p

Morphine Re-arranges Chromatin Spatial Architecture of Primate Cortical Neurons

The expression of linear DNA sequence is precisely regulated by the three-dimensional (3D) architecture of chromatin. Morphine-induced aberrant gene networks of neurons have been extensively investigated; however, how morphine impacts the 3D genomic architecture of neurons is still unknown. Here, we applied digestion-ligation-only high-throughput chromosome conformation capture (DLO Hi-C) technology to investigate the effects of morphine on the 3D chromatin architecture of primate cortical neurons. After receiving continuous morphine administration for 90 days on rhesus monkeys, we discovered that morphine re-arranged chromosome territories, with a total of 391 segmented compartments being switched. Morphine altered over half of the detected topologically associated domains (TADs), most of which exhibited a variety of shifts, followed by separating and fusing types. Analysis of the looping events at kilobase-scale resolution revealed that morphine increased not only the number but also the length of differential loops. Moreover, all identified differentially expressed genes from the RNA sequencing data were mapped to the specific TAD boundaries or differential loops, and were further validated for changed expression. Collectively, an altered 3D genomic architecture of cortical neurons may regulate the gene networks associated with morphine effects. Our finding provides critical hubs connecting chromosome spatial organization and gene networks associated with the morphine effects in humans