Objective To confirmthe protective effect of nerve growth factor (NGF)on cadiac adrenergie nerve in rats after acute myocardial infarction(AMI)and its mechanism.Methods 120 Wister rats were divided in-to sham-operated group.AMI group and NGF group.The samples were taken after6 h, d,4 d-7 d and 14 d sepa-lately.Immunohistochemistry method WaS used to show the distribution of adrenergic nerve fibers.The density of them were calculated by Medical Pathobgical Image Analysis Processing System.Results ①The densities of adrenergic nerve fibers in AMI group were obviously lower than that of sham-operated group greatly in 6 h and 2 d.4 d-7 d and14 d later.②The densities of adrenergic innervation in NGF group were obviously higher than that of AMI group 7 dand 14 d later.Conclusion NGF has protection effect 011 cardLac adrenergic nerve after AMI in the rat.

Ascites ; Humans ; Language ; Quality of Life ; Reproducibility of Results ; Surveys and Questionnaires ; Translations

Objective To clone the murine Na+-K+-2Cl-cotransporter (Nkcc2) gene promoter and analyze 20-HETE regulation of the murine Nkcc2 gene transcriptional activity. Methods A fragment of the murine Nkcc2 gene promoter was analyzed using bioinformatics software AliBaba and TRANSFAC TESS. The murine Nkcc2 gene promoter fragment (-1 462 bp-+40 bp) was amplified by PCR using murine genomic DNA as a template and then cloned into a pGL3-Basic vector to generate a luciferase reporter construct. The recombinant reporter construct was transiently transfected into HEK293 T cells using Lipofectamine 2000 for 24 h. The transfected HEK293 T cells were treated with 20-HETE for 2 h followed by measurement of the luciferase activity using the Dual-Luciferase Reporter Assay system. Results A luciferase reporter construct containing the murine Nkcc2 gene promoter was successfully generated. The results showed that 20-HETE significantly reduced the transcriptional activity of the construct. Conclusion 20-HETE may reduce the expression of the murine Nkcc2 gene through transcriptional regulation.

Objective:To develop and evaluate metal artifact removal systems (MARSs) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images.Methods:A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05).Results:Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0 mm, 1.5 mm, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively ( F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively ( F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 ( F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 ( F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 ( F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 ( F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARSs. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions:Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.

ObjectiveTo explore the relationship between schizotypal personality traits and creativity in college students and the mediating role of cognitive flexibility. MethodsSchizotypal Personality Questionnaire （SPQ）， Cognitive Flexibility Inventory （CFI） and Williams Creative Aptitude Test （WCAT） were used to assess 471 college students. Thereafter， Spearman correlation analysis was used to explore the relationship among the variables and the Bootstrap methodology was used to estimate the mediating role of cognitive flexibility. ResultsThe total SPQ， positive and disorganized schizotypal traits scores， and CFI score were all positively correlated with WCAT score （r=0.241~0.313， P<0.01）. The total SPQ， positive and disorganized schizotypal traits scores were also positively correlated with CFI score （r=0.111~0.128， P<0.05）. Cognitive flexibility mediated the relationship between positive schizotypal traits and creativity ［indirect effect=0.052 （95% CI： 0.016~0.112， P<0.01）， accounting for 11.93% of the total effect］. Cognitive flexibility mediated the relationship between disorganized schizotypal traits and creativity ［indirect effect=0.075 （95% CI： 0.020~0.161， P<0.01）， accounting for 11.50% of the total effect］. ConclusionSchizotypal personality has a direct impact on the creativity of medical students and also cause an indirect impact on their creativity through the mediating role of cognitive flexibility.

Objective:To develop and evaluate metal artifact removal systems (MARS) based on deep learning to assess their effectiveness in removing artifacts caused by different thicknesses of metals in cone-beam CT (CBCT) images.Methods:A full-mouth standard model (60 mm×75 mm×110 mm) was three-dimensional (3D) printed using photosensitive resin. The model included a removable and replaceable target tooth position where cobalt-chromium alloy crowns with varying thicknesses were inserted to generate matched CBCT images. The artifacts resulting from cobalt-chromium alloys with different thicknesses were evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). CNN-MARS and U-net-MARS were developed using a convolutional neural network and U-net architecture, respectively. The effectiveness of both MARSs were assessed through visualization and by measuring SSIM and PSNR values. The SSIM and PSNR values were statistically analyzed using one-way analysis of variance (α=0.05).Results:Significant differences were observed in the range of artifacts produced by different thicknesses of cobalt-chromium alloys (all P<0.05), with 1 mm resulting in the least artifacts. The SSIM values for specimens with thicknesses of 1.0, 1.5, and 2.0 mm were 0.916±0.019, 0.873±0.010, and 0.833±0.010, respectively ( F=447.89, P<0.001). The corresponding PSNR values were 20.834±1.176, 17.002±0.427, and 14.673±0.429, respectively ( F=796.51, P<0.001). After applying CNN-MARS and U-net-MARS to artifact removal, the SSIM and PSNR values significantly increased for images with the same thickness of metal (both P<0.05). When using the CNN-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.938±0.023, 0.930±0.029, and 0.928±0.020 ( F=2.22, P=0.112), while the PSNR values were 30.938±1.495, 30.578±2.154 and 30.553±2.355 ( F=0.54, P=0.585). When using the U-net-MARS for artifact removal, the SSIM values for 1.0, 1.5 and 2.0 mm were 0.930±0.024, 0.932±0.017 and 0.930±0.012 ( F=0.24, P=0.788), and the PSNR values were 30.291±0.934, 30.351±1.002 and 30.271±1.143 ( F=0.07, P=0.929). No significant differences were found in SSIM and PSNR values after artifact removal using CNN-MARS and U-net-MARS for different thicknesses of cobalt-chromium alloys (all P>0.05). Visualization demonstrated a high degree of similarity between the images before and after artifact removal using both MARS. However, CNN-MARS displayed clearer metal edges and preserved more tissue details when compared with U-net-MARS. Conclusions:Both the CNN-MARS and U-net-MARS models developed in this study effectively remove the metal artifacts and enhance the image quality. CNN-MARS exhibited an advantage in restoring tissue structure information around the artifacts compared to U-net-MARS.