Regulatory mechanism of macrophage polarization based on Hippo pathway

Macrophages are found to infiltrate and migrate in a large number of Tumor-associated macrophages (TMEs) and other macrophages in the microenvironment of tumors and related diseases, and undergo phenotypic changes in response to a variety of cytokines, mainly including the primary phenotype M2 and the anti-tumor phenotype M1. The Hippo signaling pathway affects the development of cancer and other diseases through various biological processes, such as inhibition of cell growth. In this review, we focus on immune cells within the microenvironment of tumors and other diseases, and the role of the Hippo pathway in tumors on macrophage polarization in the tumor microenvironment (TME) and other diseases

The Mechanism of CD8+ T Cells for Reducing Myofibroblasts Accumulation during Renal Fibrosis

Renal fibrosis is a hallmark of chronic kidney disease (CKD) and a common manifestation of end-stage renal disease that is associated with multiple types of renal insults and functional loss of the kidney. Unresolved renal inflammation triggers fibrotic processes by promoting the activation and expansion of extracellular matrix-producing fibroblasts and myofibroblasts. Growing evidence now indicates that diverse T cells and macrophage subpopulations play central roles in the inflammatory microenvironment and fibrotic process. The present review aims to elucidate the role of CD8+ T cells in renal fibrosis, and identify its possible mechanisms in the inflammatory microenvironment

Shape Adaptive Neighborhood Information-Based Semi-Supervised Learning for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is an important research topic in detailed analysis of the Earth’s surface. However, the performance of the classification is often hampered by the high-dimensionality features and limited training samples of the HSIs which has fostered research about semi-supervised learning (SSL). In this paper, we propose a shape adaptive neighborhood information (SANI) based SSL (SANI-SSL) method that takes full advantage of the adaptive spatial information to select valuable unlabeled samples in order to improve the classification ability. The improvement of the classification mainly relies on two aspects: (1) the improvement of the feature discriminability, which is accomplished by exploiting spectral-spatial information, and (2) the improvement of the training samples’ representativeness which is accomplished by exploiting the SANI for both labeled and unlabeled samples. First, the SANI of labeled samples is extracted, and the breaking ties (BT) method is used in order to select valuable unlabeled samples from the labeled samples’ neighborhood. Second, the SANI of unlabeled samples are also used to find more valuable samples, with the classifier combination method being used as a strategy to ensure confidence and the adaptive interval method used as a strategy to ensure informativeness. The experimental comparison results tested on three benchmark HSI datasets have demonstrated the significantly superior performance of our proposed method