Celastrol enhances the viability of random-pattern skin flaps by regulating autophagy through the AMPK-mTOR-TFEB axis.

In reconstructive and plastic surgery, random-pattern skin flaps (RPSF) are often used to correct defects. However, their clinical usefulness is limited due to their susceptibility to necrosis, especially on the distal side of the RPSF. This study validates the protective effect of celastrol (CEL) on flap viability and explores in terms of underlying mechanisms of action. The viability of different groups of RPSF was evaluated by survival zone analysis, laser doppler blood flow, and histological analysis. The effects of CEL on flap angiogenesis, apoptosis, oxidative stress, and autophagy were evaluated by Western blot, immunohistochemistry, and immunofluorescence assays. Finally, its mechanistic aspects were explored by autophagy inhibitor and Adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) inhibitor. On the seventh day after surgery, the survival area size, blood supply, and microvessel count of RPSF were augmented following the administration of CEL. Additionally, CEL stimulated angiogenesis, suppressed apoptosis, and lowered oxidative stress levels immediately after elevated autophagy in ischemic regions; These effects can be reversed using the autophagy inhibitor chloroquine (CQ). Specifically, CQ has been observed to counteract the protective impact of CEL on the RPSF. Moreover, it has also been discovered that CEL triggers the AMPK-mTOR-TFEB axis activation in the area affected by ischemia. In CEL-treated skin flaps, AMPK inhibitors were demonstrated to suppress the AMPK-mTOR-TFEB axis and reduce autophagy levels. This investigation suggests that CEL benefits the survival of RPSF by augmenting angiogenesis and impeding oxidative stress and apoptosis. The results are credited to increased autophagy, made possible by the AMPK-mTOR-TFEB axis activation.

Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution.

In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition.