Objective To develop a new method to expose the stellate ganglion to increase the success rate of establishing a dog model of atrial fibrillation indinced by sympathetic stimulation .Methods A total of 28 adult dogs were randomly divided into traditional group and improvement group , 14 dogs in each group .The stellate ganglions were separated by the two different methods , respectively , to establish a sympathetic stimulation induced atrial fibrillation model in all the dogs .Changes of vital signs , survival rate of the dogs and the voltage required to stimulate the stellate ganglion were recorded intraoperatively .Changes of cardiac electrophysiology were recorded before and after electric stimulation . The levels of released neurotransmitters were detected by immunohistochemistry . Results The survival rate of the improvement group was 100%(14/14), significantly higher than the 64.3%(9/14) of the traditional group (P<0.05). The operation time of the improvement group was 122.71 ±3.62 min, significantly shorter than the 269.44 ±8.79 min of the traditional group (P<0.05).The threshold voltage of the improvement group was significantly lower than that of the traditional group ( P<0.05) .Conclusions Our modified surgical procedure can effectively reduce the mortality of dogs , significantly shorten the operation time , and reduce the intraoperative blood loss , keeping a more intact stellate ganglion , and maintains a more stable voltage of electric stimulation , Therefore, it is a new method more suitable for establishment of a sympathetic stimulation induced atrial fibrillation model in dogs .

Objective To investigate the effect of the multi-material artifact reduction (MMAR) algorithm of wide-detector CT system in reducing the beam hardening artifacts in brain CT imaging. Methods Nine tubes with various iodine concentrations (0.1-16.0 mgI/ml) were placed in a uniform phantom filled with soft-tissue equivalent material. The phantom was scanned using different combinations of the tube voltage and current as follows:80 kV/530 mA, 100 kV/295 mA, 120 kV/190 mA and 140 kV/135 mA. The scanning was performed using the GE Discovery 750 and GE Revolution CT scanners, respectively. The CT values and standard deviations of the uniform areas between tubes were measured. The artifact index (AI) was calculated by using the standard deviation value outside the tubes as background noise. The artifact index values under different kV/mA combinations with different scanners were compared. CT brain images of 36 patients (n=18 on Discovery CT and n=18 on Revolution CT) were randomly selected. CT values of normal brain tissue and dark bands areas in the posterior fossa were measured for each case. The AI was calculated for these cases as for the phantom study. Paired t test was performed for phantom data analysis, and independent t test was performed for the clinical cases data analysis. Results The average AI values with Revolution CT(4.96±1.39, 4.80±1.57, 4.56±1.45, 4.76±1.57) were smaller than those of Discovery 750 (11.90 ± 6.61, 11.17 ± 5.61, 8.85 ± 4.59, 8.77 ± 3.85) under different tube voltage settings(t=3.714, 4.186, 3.745, 4.634,P<0.001). The higher the iodine concentration difference between tube pairs was, the higher the artifact index;As for clinical data, the difference in AI values between Revolution CT(2.31 ± 0.95) and Discovery 750(3.91 ± 1.32) was found statistically significant(t=4.066,P<0.001). Conclusion The multi-material artifact reduction algorithm implemented on the wide-detector Revolution CT scanner can significantly reduce beam hardening artifacts.

Objective To investigate the effect of the multi-material artifact reduction (MMAR) algorithm of wide-detector CT system in reducing the beam hardening artifacts in brain CT imaging. Methods Nine tubes with various iodine concentrations (0.1-16.0 mgI/ml) were placed in a uniform phantom filled with soft-tissue equivalent material. The phantom was scanned using different combinations of the tube voltage and current as follows:80 kV/530 mA, 100 kV/295 mA, 120 kV/190 mA and 140 kV/135 mA. The scanning was performed using the GE Discovery 750 and GE Revolution CT scanners, respectively. The CT values and standard deviations of the uniform areas between tubes were measured. The artifact index (AI) was calculated by using the standard deviation value outside the tubes as background noise. The artifact index values under different kV/mA combinations with different scanners were compared. CT brain images of 36 patients (n=18 on Discovery CT and n=18 on Revolution CT) were randomly selected. CT values of normal brain tissue and dark bands areas in the posterior fossa were measured for each case. The AI was calculated for these cases as for the phantom study. Paired t test was performed for phantom data analysis, and independent t test was performed for the clinical cases data analysis. Results The average AI values with Revolution CT(4.96±1.39, 4.80±1.57, 4.56±1.45, 4.76±1.57) were smaller than those of Discovery 750 (11.90 ± 6.61, 11.17 ± 5.61, 8.85 ± 4.59, 8.77 ± 3.85) under different tube voltage settings(t=3.714, 4.186, 3.745, 4.634,P<0.001). The higher the iodine concentration difference between tube pairs was, the higher the artifact index;As for clinical data, the difference in AI values between Revolution CT(2.31 ± 0.95) and Discovery 750(3.91 ± 1.32) was found statistically significant(t=4.066,P<0.001). Conclusion The multi-material artifact reduction algorithm implemented on the wide-detector Revolution CT scanner can significantly reduce beam hardening artifacts.

Glioma, the most common intrinsic tumor of the central nervous system, is characterized by its high incidence and poor prognosis. The aim of this study was to identify differentially expressed genes (DEGs) between glioblastoma multiforme (GBM) and low-grade glioma (LGG) to explore prognostic factors of different grades of gliomas. Single-cell transcriptome sequencing data of gliomas were collected from the NCBI Gene Expression Omnibus (GEO), which included a total of 29 097 cell samples from three datasets. For the analysis of human gliomas of different grades, 21 071 cells were obtained by filtering, and 70 genes were screened from differentially expressed genes by gene ontology (GO) analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, from which the gene DLL3 was focused by reviewing the literature. The TCGA-based gene expression profiling interactive analysis (GEPIA) database was used to explore the survival curves of genes in LGG and GBM, and the gene expression profiling interactive analysis and tumor immune estimation resource (TIMER) database was used to study the expression of key genes in gliomas of different grades, predicting biomarkers that were closely related to immunotherapy. The cBioPortal database was used to explore the relationship between DLL3 expression and 25 immune checkpoints. Gene set enrichment analysis (GSEA) further identified pathways associated with central genes. Finally, the efficacy of biomarkers in prognosis and prediction was validated in the Chinese glioma genome atlas (CGGA). These results demonstrated that prognostic genes are associated with tumor proliferation and progression. Analysis of biological information and survival suggested that these genes might serve as a promising prognostic biomarker and as new targets for selecting therapeutic strategies.
Humans ; Biomarkers ; Brain Neoplasms/pathology* ; Gene Expression Profiling/methods* ; Glioblastoma/pathology* ; Glioma/pathology* ; Intracellular Signaling Peptides and Proteins ; Membrane Proteins/genetics* ; Prognosis ; Transcriptome ; Tumor Microenvironment/genetics* ; Biomarkers, Tumor

Retinoblastoma (RB) is the most common intraocular malignant tumor in infants and young children. The key causative factors in the progression of RB remain unclear. Therefore, identifying genes closely associated with RB progression may provide important clues for disease diagnosis and gene therapy. However, tumor tissues have strong cellular heterogeneity. There may be significant differences in cell function and gene expression among cells in different pathological states. In this study, we downloaded single-cell transcriptome sequencing data of RB tumors and adjacent tissues from the GEO public database. Subsequently, we analyzed RB tumor transcriptional profiles with different disease duration at the single-cell level and identified cell groups and gene sets potentially associated with RB progression. The results showed that the tumor tissue and the adjacent tissues had overall consistency in the single-cell transcriptional map, but there were obvious differences in the distribution proportions of G1 phase cells, G2 phase cells, and microglia cells of cone precursors in RB tumor and the adjacent tissues. Furthermore, the role of three cell populations in the progression of RB tumors was emphatically analyzed. We found that in the early stage of RB tumors, cone precursor cells proliferated abnormally in G1 phase. With the progression of RB tumors, the proportion of cone precursor cells in G2 phase increased significantly. Meanwhile, the results of differential analysis of microglial populations during RB progression showed that the key genes mainly involved in immune response include RPL23, B2M, and HLA superfamily genes. This study provides new perspectives and data resources for the research of RB pathogenesis and progress.
Child ; Infant ; Humans ; Child, Preschool ; Retinoblastoma/pathology* ; Transcriptome ; Retinal Neoplasms/pathology*