ABSTRACT
Femoral head necrosis (FHN), one of the most popular joint diseases in the musculoskeletal system, is usually attributed to local ischemia of the femoral head. Thus, regenerating the vascularization capacity and restoring the local perfusion of the femoral head becomes an efficient therapeutic approach for FHN. We investigated the function of autologous lipoaspirate cells (LPCs) in regenerating circulation in FHN animal models and human subjects in this study. We also explored the mechanisms of why LPCs show a superior effect than that of the bone marrow-derived stem cells (BMSCs) in vascularization. Thirty-four FHN patients were recruited for the randomized clinical trial. Harris Hip Score (HHS) and digital subtraction arteriography (DSA) and interventional technique were used to compare the efficacy of LPCs treatment and vehicle therapy in improving femoral head circulation and hip joint function. Cellular mechanism that underlies the beneficial effect of LPCs in restoring blood supply and rescuing bone architecture was further explored using canine and mouse FHN animal models. We found that LPCs perfusion through the medial circumflex artery will promote the femoral head vascularization and bone structure significantly in both FHN patients and animal models. The HHS in LPCs treated patients was significantly improved relative to vehicle group. The levels of angiogenesis factor secreted by LPCs such as VEGF, FGF2, VEC, TGF-ß, were significantly higher than that of BMSCs. As the result, LPCs showed a better effect in promoting the tube structure formation of human vascular endothelial cells (HUVEC) than that of BMSCs. Moreover, LPCs contains a unique CD44+CD34+CD31- population. The CD44+CD34+CD31- LPCs showed significantly higher angiogenesis potential as compared to that of BMSCs. Taken together, our results show that LPCs possess a superior vascularization capacity in both autonomous and paracrine manner, indicating that autologous LPCs perfusion via the medial circumflex artery is an effective therapy for FHN.
ABSTRACT
Improving the temperature prediction accuracy for subgrades in seasonally frozen regions will greatly help improve the understanding of subgrades' thermal states. Due to the nonlinearity and non-stationarity of the temperature time series of subgrades, it is difficult for a single general neural network to accurately capture these two characteristics. Many hybrid models have been proposed to more accurately forecast the temperature time series. Among these hybrid models, the CEEMDAN-LSTM model is promising, thanks to the advantages of the long short-term memory (LSTM) artificial neural network, which is good at handling complex time series data, and its combination with the broad applicability of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in the field of signal decomposition. In this study, by performing empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and CEEMDAN on temperature time series, respectively, a hybrid dataset is formed with the corresponding time series of volumetric water content and frost heave, and finally, the CEEMDAN-LSTM model is created for prediction purposes. The results of the performance comparisons between multiple models show that the CEEMDAN-LSTM model has the best prediction performance compared to other decomposed LSTM models because the composition of the hybrid dataset improves predictive ability, and thus, it can better handle the nonlinearity and non-stationarity of the temperature time series data.
