Optically controllable coupling between edge and topological interface modes of graphene metasurfaces

Nonlinear topological photonics has been attracting increasing research interest, as it provides an exciting photonic platform that combines the advantages of active all-opticall control offered by nonlinear optics with the unique features of topological photonic systems, such as topologically-protected defect-immune light propagation. In this paper, we demonstrate that topological interface modes and trivial edge modes of a specially designed graphene metasurface can be coupled in a tunable and optically controllable manner, thus providing an efficient approach to transfer optical power to topologically protected states. This is achieved in a pump-signal configuration, in which an optical pump propagating in a bulk mode of the metasurface is employed to tune the band structure of the photonic system and, consequently, the coupling coefficient and wave-vector mismatch between edge and topological interface modes. This tunable coupling mechanism is particularly efficient due to the large Kerr coefficient of graphene. Importantly, we demonstrate that the required pump power can be significantly reduced if the optical device is operated in the slow-light regime. We performed our analysis using both \textit{ab initio} full-wave simulations and a coupled-mode theory that captures the main physics of this active coupler and observe a good agreement between the two approaches. This work may lead to the design of active topological photonic devices with new or improved functionality

Toxicity of kadsura coccinea (Lem.) A. C. Sm. essential oil to the bed bug, cimex lectularius L. (hemiptera: Cimicidae)

Copyright © 2019 American Society for Microbiology. All Rights Reserved. We sought to define trends in and predictors of carbapenem consumption across community, teaching, and university-affiliated hospitals in the United States and Canada. We conducted a retrospective multicenter survey of carbapenem and broad-spectrum noncarbapenem beta-lactam consumption between January 2011 and December 2013. Consumption was tabulated as defined daily doses (DDD) or as days of therapy (DOT) per 1,000 patient days (PD). Multivariate mixed-effects models were explored, and final model goodness of fit was assessed by regressions of observed versus predicted values and residual distributions. A total of 20 acute-care hospitals responded. The centers treated adult patients (n 19/20) and pediatric/neonatal patients (n 17/20). The majority of the centers were nonprofit (n 17/20) and not affiliated with medical/teaching institutions (n 11/20). The median (interquartile range [IQR]) carbapenem consumption rates were 38.8 (17.4 to 95.7) DDD/1,000 PD and 29.7 (19.2 to 40.1) DOT/1,000 PD overall. Carbapenem consumption was well described by a multivariate linear mixed-effects model (fixed effects, R2 0.792; fixed plus random effects, R2 0.974). Carbapenem consumption increased by 1.91-fold/quarter from 48.6 DDD/1,000 PD (P 0.004) and by 0.056-fold/quarter from 45.7 DOT/ 1,000 PD (P 0.93) over the study period. Noncarbapenem consumption was independently related to increasing carbapenem consumption (beta 0.31 for increasing noncarbapenem beta-lactam consumption; P 0.001). Regular antibiogram publication and promotion of conversion from intravenous (i.v.) to oral (p.o.) administration independently affected carbapenem consumption rates. In the final model, 58.5% of the observed variance in consumption was attributable to between-hospital differences. Rates of carbapenem consumption across 20 North American hospitals differed greatly, and the observed differences were correlated with hospital-specific demographics. Additional studies focusing on the drivers of hospital-specific carbapenem consumption are needed to determine whether these rates are justifiable

Hepatoprotective Effect of Polyphenol-Enriched Fraction from Folium Microcos

Folium Microcos (FM), the leaves of Microcos paniculata L., shows various biological functions including antioxidant activity and α-glucosidase inhibitory effect. However, its therapeutic potential in acute liver injury is still unknown. This study investigated the hepatoprotective effect and underlying mechanisms of the polyphenol-enriched fraction (FMF) from Folium Microcos. FMF exhibited strong free radical scavenging activities and prevented HepG2/Hepa1–6 cells from hydrogen peroxide- (H2O2-) induced ROS production and apoptosis in vitro. Antioxidant activity and cytoprotective effects were further verified by alleviating APAP-induced hepatotoxicity in mice. Western blot analysis revealed that FMF pretreatment significantly abrogated APAP-mediated phosphorylation of MAPKs, activation of proapoptotic protein caspase-3/9 and Bax, and restored expression of antiapoptotic protein Bcl2. APAP-intoxicated mice pretreated with FMF showed increased nuclear accumulation of nuclear factor erythroid 2-related factor (Nrf2) and elevated hepatic expression of its target genes, NAD(P)H:quinine oxidoreductase 1 (NQO1) and hemeoxygenase-1(HO-1). HPLC analysis revealed the four predominantly phenolic compounds present in FMF: narcissin, isorhamnetin-3-O-β-D-glucoside, isovitexin, and vitexin. Consequently, these findings indicate that FMF possesses a hepatoprotective effect against APAP-induced hepatotoxicity mainly through dual modification of ROS/MAPKs/apoptosis axis and Nrf2-mediated antioxidant response, which may be attributed to the strong antioxidant activity of phenolic components

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCCrelated genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumorinfiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area