Both zero-prolife devices and artificial cervical disks are generally effective and safe in the treatment of 2 noncontiguous levels of cervical spondylosis. However, in view of occurrence of the radiologic ASD and operative time, we prefer to artificial cervical disks if indications are well controlled.

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Recent studies suggest that dihydroartemisinin (DHA), a derivative of artemisinin isolated from the traditional Chinese herb Artemisia annua L., has anticancer properties. Due to poor water solubility, poor oral activity, and a short plasma half-life, large doses of DHA have to be injected to achieve the necessary bioavailability. This study examined increasing DHA bioavailability by encapsulating DHA within gelatin (GEL) or hyaluronan (HA) nanoparticles via an electrostatic field system. Observations from transmission electron microscopy show that DHA in GEL and HA nanoparticles formed GEL/DHA and HA/DHA aggregates that were approximately 30-40 nm in diameter. The entrapment efficiencies for DHA were approximately 13 and 35% for the GEL/DHA and HA/DHA aggregates, respectively. The proliferation of A549 cells was inhibited by the GEL/DHA and HA/DHA aggregates. Fluorescent annexin V-fluorescein isothiocyanate (FITC) and propidium iodide (PI) staining displayed low background staining with annexin V-FITC or PI on DHA-untreated cells. In contrast, annexin V-FITC and PI stains dramatically increased when the cells were incubated with GEL/DHA and HA/DHA aggregates. These results suggest that DHA-aggregated GEL and HA nanoparticles exhibit higher anticancer proliferation activities than DHA alone in A549 cells most likely due to the greater aqueous dispersion after hydrophilic GEL or HA nanoparticles aggregation. These results demonstrate that DHA can aggregate with nanoparticles in an electrostatic field environment to form DHA nanosized aggregates.

Circular RNA (CircRNA) plays an important role in tumorigenesis and progression of non-small cell lung cancer (NSCLC), but the pathogenesis of NSCLC caused by circRNA has not been fully elucidated. This study aimed to investigate differentially expressed circRNAs and identify the underlying pathogenesis hub genes of NSCLC by comprehensive bioinformatics analysis. Data of gene expression microarrays (GSE101586, GSE101684, and GSE112214) were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DECs) were obtained by the “limma” package of R programs and the overlapping operation was implemented of DECs. CircBase database and Cancer-Specific CircRNA database (CSCD) were used to find miRNAs binding to DECs. Target genes of the found miRNAs were identified utilizing Perl programs based on miRDB, miRTarBase, and TargetScan databases. Functional and enrichment analyses of selected target genes were performing using the “cluster profiler” package. Protein-protein interaction (PPI) network was constructed by the Search Tool for the STRING database and module analysis of selected hub genes was performed by Cytoscape 3.7.1. Survival analysis of hub genes were performed by Gene Expression Profiling Interactive Analysis (GEPIA). Respectively, 1 DEC, 249 DECs, and 101 DECs were identified in GSE101586, GSE101684, and GSE112214. A total of eight overlapped circRNAs, 43 miRNAs and 427 target genes were identified. Gene Ontology (GO) enrichment analysis showed these target genes were enriched in biological processes of regulation of histone methylation, Ras protein signal transduction and covalent chromatin modification etc. Pathway enrichment analysis showed these target genes are mainly involved in AMPK signaling pathway, signaling pathways regulating pluripotency of stem cells and insulin signaling pathway etc. A PPI network was constructed based on 427 target genes of the 43 miRNAs. Ten hub genes were found, of which the expression of MYLIP, GAN, and CDC27 were significantly related to NSCLC patient prognosis. Our study provide a deeper understanding the circRNAs-miRNAs-target genes by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of NSCLC. MYLIP, GAN, and CDC27 genes might serve as novel biomarker for precise treatment and prognosis of NSCLC in the future.

Background: Geriatric nutritional risk index (GNRI) and prognostic nutritional index (PNI) are associated with prognosis of various malignancies. Although GNRI and PNI indicates prognosis in some clinical settings, the values of GNRI and PNI on the prognosis of geriatric patients with Diffuse Large B‐Cell Lymphoma (DLBCL) is unclear. This retrospective analysis aimed to explore the prognostic values of GNRI and PNI in elderly DLBCL patients. Methods: A total of 133 geriatric patients with DLBCL were recruited from Affiliated Hospital of Xuzhou Medical University, and clinicopathological variables were analyzed. X-Tile program, restricted cubic spline (RCS) and time-dependent receiver operating characteristic (ROC) analysis were used to determine optimal cut-off points of GNRI, PNI and other continuous variables; univariate and multivariate Cox proportional hazards analyses were used for variables selection; Kaplan‐Meier curve was utilized to analyze the influence of variables on prognosis; log-rank test was performed for difference evaluation between groups. Results: The optimal cut-off points for GNRI and PNI were 106.26 and 47 by using RCS. Multivariate analysis showed that PNI, age, hemoglobin, liver invasion and central nervous system invasion were independent prognostic factors for elderly patients with DLBCL, and PNI was ( P = 0.001, HR = 0.413, 95% CI (0.240-0.710) a stronger predictor. Low PNI could predict worse prognosis independently of elderly patients of DLBCL and could re-stratify patients in GCB group, CD5 positive group BCL-2 positive group, and BCL-6 positive group. Conclusions: PNI was an independent adverse factor for elderly DLBCL and patients with low PNI in GCB group, CD5 positive group and BCL-6 positive group were with poor survival.