Identification of a Novel N7-Methylguanosine-Related LncRNA Signature Predicts the Prognosis of Hepatocellular Carcinoma and Experiment Verification

(1) Background: It is well-known that long non-coding RNAs (lncRNAs) and N7-methylguanosine (m7G) contribute to hepatocellular carcinoma (HCC) progression. However, it remains unclear whether lncRNAs regulating m7G modification could predict HCC prognosis. Thus, we sought to explore the prognostic implications of m7G-related lncRNAs in HCC patients. (2) Methods: Prognostic M7G-related lncRNAs obtained from The Cancer Genome Atlas (TCGA) database were screened by co-expression analysis and univariate Cox regression analysis. Next, the m7G-related lncRNA signature (m7GRLSig) was conducted by Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analysis. Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) assessed the prognostic abilities of our signature. Univariate and multivariate Cox regression, nomogram, and principal component analysis (PCA) were conducted to evaluate our signature. Subsequently, we investigated the role of m7GRLSig on the immune landscape and sensitivity to drugs in HCC patients. The potential function of lncRNAs obtained from the prognostic signature was explored by in vitro experiments. (3) Results: A novel m7GRLSig was identified using seven meaningful lncRNA (ZFPM2-AS1, AC092171.2, PIK3CD-AS2, NRAV, CASC19, HPN-AS1, AC022613.1). The m7GLPSig exhibited worse survival in the high-risk group and served as an independent prognostic factor. The m7GRLSig stratification was sensitive in assessing the immune landscape and sensitivity to drugs between the high-risk and low-risk groups. Finally, in vitro experiments confirmed that the knockdown of NRAV was accompanied by the downregulation of METTL1 during HCC progression. (4) Conclusions: The m7G-related signature is a potential predictor of HCC prognosis and contributes to individualize the effective drug treatment of HCC

Preparation and Properties of Partial-Degradable ZrO₂–Chitosan Particles–GelMA Composite Scaffolds

In the field of bone repair, the inorganic–organic composite scaffold is a promising strategy for mimicking the compositions of the natural bone. In addition, as implants for repairing load-bearing sites, an inert permanent bone substitute composites with bioactive degradable ingredients may make full use of the composite scaffold. Herein, the porous zirconia (ZrO2) matrix was prepared via the template replication method, and the partial degradable ZrO2–chitosan particles–GelMA composite scaffolds with different chitosan/GelMA volume ratios were prepared through the vacuum infiltration method. Dynamic light scattering (DLS) and the scanning electron microscope (SEM) were adopted to observe the size of the chitosan particles and the morphologies of the composites scaffold. The mechanical properties, swelling properties, and degradation properties of the composite scaffolds were also characterized by the mechanical properties testing machine and immersion tests. The CCK-8 assay was adopted to test the biocompatibility of the composite scaffold preliminarily. The results show that chitosan particles as small as 60 nm were obtained. In addition, the ratio of chitosan/GelMA can influence the mechanical properties and the swelling and degradation behaviors of the composites scaffold. Furthermore, improved cell proliferation performance was obtained for the composite scaffolds

Genetically predicted 486 blood metabolites in relation to risk of colorectal cancer: A Mendelian randomization study

Abstract Background Metabolic disorders are a hallmark feature of cancer. However, the evidence for the causality of circulating metabolites to promote or prevent colorectal cancer (CRC) is still lacking. We performed a two‐sample Mendelian randomization (MR) analysis to assess the causality from genetically proxied 486 blood metabolites to CRC. Methods Genome‐wide association study (GWAS) data for exposures were extracted from 7824 Europeans GWAS on metabolite levels. GWAS data for CRC from the GWAS catalog database GCST012879 were used for the preliminary analysis. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR‐Egger and weighted median as complementary analyses. Cochran Q test, MR‐Egger intercept test, MR‐PRESSO, Radial MR, and leave‐one‐out analysis were used for sensitivity analyses. For significant associations, additional independent CRC GWAS data GCST012880 were used for replication analysis and meta‐analysis. For the final identification of metabolites, Steiger test, linkage disequilibrium score regression, and colocalization analysis were performed for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on CRC. Results The results of this study indicated significant associations between six metabolites pyruvate (odds ratio [OR]: 0.49, 95% confidence interval [CI]: 0.32–0.77, p = 0.002), 1,6‐anhydroglucose (OR: 1.33, 95% CI: 1.11–1.59, p = 0.002), nonadecanoate (19:0) (OR: 0.40, 95% C I:0.4–0.68, p = 0.0008), 1‐linoleoylglycerophosphoethanolamine (OR: 0.47, 95% CI: 0.30–0.75, p = 0.001), 2‐hydroxystearate (OR: 0.39, 95% CI: 0.23–0.67, p = 0.0007), gamma‐glutamylthreonine (OR: 2.14, 95% CI: 1.02–4.50, p = 0.040) and CRC. MVMR analysis revealed that genetically predicted pyruvate, 1‐linoleoylglycerophosphoethanolamine and gamma‐glutamylthreonine can directly influence CRC independently of other metabolites. Conclusion The current work provides evidence to support the causality of the six circulating metabolites on CRC and a new perspective on the exploration of the biological mechanisms of CRC by combining genomics and metabolomics. These findings contribute to the screening, prevention and treatment of CRC

Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes

Structured optical fields, such as cylindrical vector (CV) and orbital angular momentum (OAM) modes, have attracted considerable attention due to their polarization singularities and helical phase wavefront structure. However, one of the most critical challenges is still the intelligent generation or precise control of these modes. Here, we demonstrate the first simulation and experimental realization of decomposing the CV and OAM modes by reconstructing the multi-view images of projected intensity distribution. Assisted by the deep learning–based stochastic parallel gradient descent (SPGD) algorithm, the modal coefficients and optical field distributions can be retrieved in 1.32 s within an average error of 0.416 % showing high efficiency and accuracy. Especially, the interference pattern and quarter-wave plate are exploited to confirm the phase and distinguish elliptical or circular polarization direction, respectively. The generated donut modes are experimentally decomposed in the CV and OAM modes, where purity of CV modes reaches 99.5 %. Finally, fast switching vortex modes is achieved by electrically driving the polarization controller to deliver diverse CV modes. Our findings may provide a convenient way to characterize and deepen the understanding of CV or OAM modes in view of modal proportions, which is expected of latent applied value on information coding and quantum computation

Recent Advances in g-C₃N₄-Based Photocatalysts for NO_x Removal

Nitrogen oxides (NOx) pollutants can cause a series of environmental issues, such as acid rain, ground-level ozone pollution, photochemical smog and global warming. Photocatalysis is supposed to be a promising technology to solve NOx pollution. Graphitic carbon nitride (g-C3N4) as a metal-free photocatalyst has attracted much attention since 2009. However, the pristine g-C3N4 suffers from poor response to visible light, rapid charge carrier recombination, small specific surface areas and few active sites, which results in deficient solar light efficiency and unsatisfactory photocatalytic performance. In this review, we summarize and highlight the recent advances in g-C3N4-based photocatalysts for photocatalytic NOx removal. Firstly, we attempt to elucidate the mechanism of the photocatalytic NOx removal process and introduce the metal-free g-C3N4 photocatalyst. Then, different kinds of modification strategies to enhance the photocatalytic NOx removal performance of g-C3N4-based photocatalysts are summarized and discussed in detail. Finally, we propose the significant challenges and future research topics on g-C3N4-based photocatalysts for photocatalytic NOx removal, which should be further investigated and resolved in this interesting research field

Glucocorticoid receptor-mediated Nr1d1 chromatin circadian misalignment in stress-induced irritable bowel syndrome

Summary: Stress-elevated glucocorticoids cause circadian disturbances and gut-brain axis (GBA) disorders, including irritable bowel syndrome (IBS). We hypothesized that the glucocorticoid receptor (GR/NR3C1) might cause chromatin circadian misalignment in the colon epithelium. We observed significantly decreased core circadian gene Nr1d1 in water avoidance stressed (WAS) BALB/c colon epithelium, like in IBS patients. WAS decreased GR binding at the Nr1d1 promoter E-box (enhancer box), and GR could suppress Nr1d1 via this site. Stress also altered GR binding at the E-box sites along the Ikzf3-Nr1d1 chromatin and remodeled circadian chromatin 3D structures, including Ikzf3-Nr1d1 super-enhancer, Dbp, and Npas2. Intestinal deletion of Nr3c1 specifically abolished these stress-induced transcriptional alternations relevant to IBS phenotypes in BALB/c mice. GR mediated Ikzf3-Nr1d1 chromatin disease related circadian misalignment in stress-induced IBS animal model. This animal model dataset suggests that regulatory SNPs of human IKZF3-NR1D1 transcription through conserved chromatin looping have translational potential based on the GR-mediated circadian-stress crosstalk