Topological protection of dual-polarization biphoton states in photonic crystals.

Polarization control is a major issue in topological quantum optics that limits reliable generation and transmission of quantum states. This study presents what we believe to be a novel topological photonic crystal design that provides topological protection for biphoton pairs for both TE and TM polarization. By well-designed cell configurations within the lattice, two topological boundaries emerge that can accommodate TM and TE modes at the same time. By adjusting the dispersion curves, we can further design nonlinear four-wave mixing processes within the topological photonic crystals and provide theoretical explanations for the entanglement of the dual-polarization biphoton states. Numerical results confirm the robust transport of entangled photon pairs, even when subjected to sharp bending. Moreover, combining the dual-polarization topological photonic crystal with a polarization beam splitter enables the preparation of polarization-encoded maximally entangled states. Our work exhibits significant potential for applications in robust optical quantum information processing and quantum secure communication.

Construction of anoikis-related lncRNAs risk model: Predicts prognosis and immunotherapy response for gastric adenocarcinoma patients.

Background: Anoikis acts as a programmed cell death that is activated during carcinogenesis to remove undetected cells isolated from ECM. Further anoikis based risk stratification is expected to provide a deeper understanding of stomach adenocarcinoma (STAD) carcinogenesis. Methods: The information of STAD patients were acquired from TCGA dataset. Anoikis-related genes were obtained from the Molecular Signatures Database and Pearson correlation analysis was performed to identify the anoikis-related lncRNAs (ARLs). We performed machine learning algorithms, including Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ARLs to build the OS-score and OS-signature. Clinical subgroup analysis, tumor mutation burden (TMB) detection, drug susceptibility analysis, immune infiltration and pathway enrichment analysis were further performed to comprehensive explore the clinical significance. Results: We established a STAD prognostic model based on five ARLs and its prognostic value was verified. Survival analysis showed that the overall survival of high-risk score patients was significantly shorter than that of low-risk score patients. The column diagrams show satisfactory discrimination and calibration. The calibration curve verifies the good agreement between the prediction of the line graph and the actual observation. TIDE analysis and drug sensitivity analysis showed significant differences between different risk groups. Conclusion: The novel prognostic model based on anoikis-related lncRNAs we identified could be used for prognosis prediction and precise therapy in gastric adenocarcinoma.

Machine learning-based metabolism-related genes signature and immune infiltration landscape in diabetic nephropathy.

Background: To identify the diagnostic biomarkers of metabolism-related genes (MRGs), and investigate the association of the MRGs and immune infiltration landscape in diabetic nephropathy (DN). Methods: The transcriptome matrix was downloaded from the GEO database. R package "limma" was utilized to identify the differential expressed MRGs (DE-MRGs) of HC and DN samples. Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DE-MRGs were performed using "clusterProfiler" R package. WGCNA, LASSO, SVM-RFE, and RFE algorithms were employed to select the diagnostic feature biomarkers for DN. The ROC curve was used to evaluate discriminatory ability for diagnostic feature biomarkers. CIBERSORT algorithm was performed to investigate the fraction of the 22-types immune cells in HC and DN group. The correlation of diagnostic feature biomarkers and immune cells were performed via Spearman-rank correlation algorithm. Results: A total of 449 DE-MRGs were identified in this study. GO and KEGG pathway enrichment analysis indicated that the DE-MRGs were mainly enriched in small molecules catabolic process, purine metabolism, and carbon metabolism. ADI1, PTGS2, DGKH, and POLR2B were identified as diagnostic feature biomarkers for DN via WGCNA, LASSO, SVM-RFE, and RFE algorithms. The result of CIBERSORT algorithm illustrated a remarkable difference of immune cells in HC and DN group, and the diagnostic feature biomarkers were closely associated with immune cells. Conclusion: ADI1, PTGS2, DGKH, and POLR2B were identified as diagnostic feature biomarkers for DN, and associated with the immune infiltration landscape, providing a novel perspective for the future research and clinical management for DN.

Role of Borneol Induced Autophagy in Enhancing Radiosensitivity of Malignant Glioma.

Glioma is the common primary craniocerebral malignancy with unfavorable prognosis. It is currently treated by surgical resection supplemented by radiotherapy, although the resistance of glioma cells to radiation limits the therapeutic outcomes. The aim of the present study was to determine the potential radiosensitizing effects of borneol and the underlying mechanisms. We found that borneol administration along with radiotherapy significantly inhibited the growth of primary glioma cells in vitro and in vivo. Furthermore, borneol markedly increased the number of autophagosomes in the glioma cells, which coincided with increased expression of beclin-1 and LC3. And the combination of borneol and radiation exposure significantly decreased the expression levels of HIF-1α, mTORC1 and eIF4E. In addition, silencing mTORC1 and eIF4E upregulated Beclin-1 and LC3 and decreased the expression of HIF-1α, thereby inhibiting tumor cell proliferation. Our findings suggest that borneol sensitizes glioma cells to radiation by inducing autophagy via inhibition of the mTORC1/eIF4E/HIF-1α regulatory axis.