Neuroprotective effects of etanercept on diabetic retinopathy via regulation of the TNF-α/NF-κB signaling pathway

Purpose: To study the influence of etanercept on diabetic retinopathy in rats via tumor necrosis factor alpha (TNF-α)/nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway. Methods: Thirty-six Sprague-Dawley (SD) rats were randomly divided into normal, model and etanercept groups. The expression of Caspase-3 was determined by immunohistochemistry, while the relative protein and mRNA expression levels of TNF-α and NF-κB were determined by Western blotting and quantitative polymerase chain reaction, respectively. Besides, the contents of TNF-α and interleukin-1 beta (IL-1β) were evaluated using enzyme-linked immunosorbent assay (ELISA), while cell apoptosis was assessed by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL). Results: Immunohistochemical studies showed that the mean optical density of tissues positive for caspase-3 in both the model and etanercept groups were significantly higher than in the normal group (p < 0.05), while the mean optical density in the etanercept group was significantly lower than that in the model group (p < 0.05). The protein expression levels of TNF-α and NF-κB in the etanercept group were significantly lower than those in the model group (p < 0.05). Furthermore, mRNA expressions of TNF-α and NF-κB declined in the etanercept group (p < 0.05); in addition, TNF-α, and IL-1β levels in the etanercept group were lower than in the model group (p < 0.05). Cell apoptosis in the etanercept group was also lower than in the model group. Conclusion: Etanercept suppresses TNF-α/NF-κB signaling pathway thereby repressing inflammation and cell apoptosis in diabetic retinopathy rats. Therefore, etenercept’s neuroprotective effect may potentially be useful in developing a suitable therapy for diabetic neuropathy

Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China

This study evaluated causative factors in landslide susceptibility assessments and compared the performance of five landslide susceptibility models based on the certainty factor (CF), logistic regression (LR), analytic hierarchy process (AHP), coupled CF–analytic hierarchy process (CF-AHP), and CF–logistic regression (CF-LR). Kaiyang County, China, has complex geological conditions and frequent landslide disasters. Based on field observations, nine influencing factors, namely, altitude, slope, topographic relief, aspect, engineering geological rock group, slope structure, distance to faults, distance to rivers, and normalized difference vegetation index, were extracted using the raster data model. The precision of the five models was tested using the distribution of disaster points for each grade and receiver operating characteristic curve. The results showed that the landslide frequency ratios accounted for more than 75% within the high and very high susceptibility zones according to the model prediction, and the AUC evaluating precision was 0.853, 0.712, 0.871, 0.873, and 0.895, respectively. The accuracy sequencing of the five models was CF-LR > CF-AHP > LR > CF > AHP, indicating that the CF-AHP and CF-LR models are better than the others. This study provides a reliable method for landslide susceptibility mapping at the county-level resolution