Serum Amyloid A Induces a Vascular Smooth Muscle Cell Phenotype Switch through the p38 MAPK Signaling Pathway

Atherosclerosis is an important pathological condition which is accompanied by a vascular smooth muscle cell (VSMC) phenotype switch toward a synthetic phenotype. As an acute-phase protein, Serum Amyloid A (SAA) is thought to have a close relationship to atherosclerosis development. However, no study has investigated the direct effect of SAA on the VSMC phenotype switch, as well as the underlying mechanisms. The purpose of our study was to explore the effect of SAA on the VSMC phenotype switch and the potential mechanisms involved. In our study, we found that SAA induced the VSMC phenotype switch which reduced expression of the smooth muscle cell (SMC) marker and enhanced expression of the matrix synthesis related marker. The proliferative ability of VSMCs was also increased by SAA treatment. Furthermore, our research found that SAA activated the ERK1/2 and p38 MAPK signaling pathways. Finally, by applying the ERK1/2 and p38 inhibitors, U0126 and SB203580, we demonstrated that the SAA-induced VSMC phenotype switch was p38-dependent. Taken together, these results indicated that SAA may play an important role in promoting the VSMC phenotype switch through the p38 MAPK signaling pathway

Representation Model of Topological Relations between Complex Planar Objects

In order to express the details of the topological relations between complex planar objects, the classic 9-intersection model is improved and the two extended 9-intersection models that based on decomposition ideas are proposed: the one 9-intersection model method that is decomposed into simple area has its advantage of simplification, but at the cost of complicated expressions; another 9-intersection model method that is decomposed into point-set, conforming well with the classical 9-intersection model, but has relatively complex calculations. Compared the expressive abilities between the two kinds of extened 9-intersection models and the classic 9-intersection model by examples. The results show that both the two extended 9-intersection models can give more accurately expression of the topological relations between details of the sub parts in complex planar objects, the expressive ability of 9-intersection model has been expanded and improved

Combining Sample Plot Stratification and Machine Learning Algorithms to Improve Forest Aboveground Carbon Density Estimation in Northeast China Using Airborne LiDAR Data

Timely, accurate estimates of forest aboveground carbon density (AGC) are essential for understanding the global carbon cycle and providing crucial reference information for climate-change-related policies. To date, airborne LiDAR has been considered as the most precise remote-sensing-based technology for forest AGC estimation, but it suffers great challenges from various uncertainty sources. Stratified estimation has the potential to reduce the uncertainty and improve the forest AGC estimation. However, the impact of stratification and how to effectively combine stratification and modeling algorithms have not been fully investigated in forest AGC estimation. In this study, we performed a comparative analysis of different stratification approaches (non-stratification, forest type stratification (FTS) and dominant species stratification (DSS)) and different modeling algorithms (stepwise regression, random forest (RF), Cubist, extreme gradient boosting (XGBoost) and categorical boosting (CatBoost)) to identify the optimal stratification approach and modeling algorithm for forest AGC estimation, using airborne LiDAR data. The analysis of variance (ANOVA) was used to quantify and determine the factors that had a significant effect on the estimation accuracy. The results revealed the superiority of stratified estimation models over the unstratified ones, with higher estimation accuracy achieved by the DSS models. Moreover, this improvement was more significant in coniferous species than broadleaf species. The ML algorithms outperformed stepwise regression and the CatBoost models based on DSS provided the highest estimation accuracy (R2 = 0.8232, RMSE = 5.2421, RRMSE = 20.5680, MAE = 4.0169 and Bias = 0.4493). The ANOVA of the prediction error indicated that the stratification method was a more important factor than the regression algorithm in forest AGC estimation. This study demonstrated the positive effect of stratification and how the combination of DSS and the CatBoost algorithm can effectively improve the estimation accuracy of forest AGC. Integrating this strategy with national forest inventory could help improve the monitoring of forest carbon stock over large areas

Effects of plies assembling on textile composite cellular structures

Cellular structures are generally assembled with face sheet plies in their application. It is necessary to understand the influence of assembled plies on deformation and energy absorption before using textile composite cellular structures in engineering design. In this paper, effects of ply assembly, outer ply material, outer ply thickness, and loading area on energy absorption and deformation of the applied structure including cellular structure and face sheet plies are investigated. Three-dimensional finite element analyses are carried out employing orthotropic mechanical properties of the applied materials, textile composite, wood, E-glass, aluminium alloy 2024-T3 and unidirectional fiber-epoxy composite T-300. The predicted results show that deformation and distributed strain energy density of both outer and inner surfaces of the applied structure are significantly affected by ply assembly, outer ply material, outer ply thickness, and loading area

Bisphosphoglycerate mutase predicts myocardial dysfunction and adverse outcome in sepsis: an observational cohort study

Abstract Background Sepsis not only causes inflammation, but also damages the heart and increases the risk of death. The glycolytic pathway plays a crucial role in the pathogenesis of sepsis-induced cardiac injury. This study aims to investigate the value of bisphosphoglycerate mutase (BPGM), an intermediate in the glycolytic pathway, in evaluating cardiac injury in septic patients and predicting poor prognosis in sepsis. Methods This prospective study included 85 patients with sepsis. Serum BPGM was measured at the time of enrollment, and the patients were divided into a BPGM-positive group (n = 35) and a BPGM-negative group (n = 50) according to their serum BPGM levels. Baseline clinical and echocardiographic parameters, and clinical outcomes were analyzed and compared between the two groups. Kaplan–Meier analysis was used to compare the 28-day survival rate between BPGM-negative and BPGM-positive patients. Multivariate logistic regression analysis was conducted to explore the independent risk factors for 28-day mortality in septic patients. The predictive value of serum BPGM for sepsis-induced myocardial injury and poor prognosis in sepsis was evaluated using receiver operating characteristic (ROC)curve analysis. Result The serum level of BPGM was significantly higher in patients who died within 28 days compared to survivors (p < 0.001). Kaplan–Meier analysis showed that serum BPGM-positive sepsis patients had a significantly shorter 28-day survival time (p < 0.001). Multivariate logistic regression analysis showed that serum BPGM (OR = 9.853, 95%CI 1.844–52.655, p = 0.007) and left ventricular ejection fraction-simpson(LVEF-S) (OR = 0.032, 95% CI 0.002–0.43, p = 0.009) were independent risk factors for 28-day mortality in sepsis patients. Furthermore, BPGM levels was negatively correlated with LVEF-S (p = 0.005) and positively correlated with the myocardial performance (Tei) index (p < 0.001) in sepsis patients. ROC curve analysis showed that serum BPGM was a good predictor of septic myocardial injury and 28-day mortality in sepsis patients. Conclusion The level of BPGM in the serum of sepsis patients can serve as a monitoring indicator for myocardial injury, with its high level indicating the occurrence of secondary myocardial injury events and adverse outcomes in sepsis patients