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Tumor-associated macrophages (TAMs) are pivotal in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC), influencing various stages from initiation to metastasis. Understanding the role of TAMs in HCC is crucial for developing novel therapeutic strategies. Macrophages exhibit plasticity, resulting in M1 and M2 phenotypes, with M1 macrophages displaying antitumor properties and M2 macrophages promoting tumor progression. Targeting TAMs to alter their polarization could offer new avenues for HCC treatment. β,β-dimethylacrylalkannin (DMAKN), a natural naphthoquinone, has gained attention for its antitumor properties. However, its impact on TAMs modulation remains unclear. This study investigates DMAKN's modulation of TAMs and its anti-HCC activity. Using an in vitro model with THP-1 cells, we induced M1 macrophages with LPS/IFN-γ and M2 macrophages with IL-4/IL-13, confirming polarization with specific markers. Co-culturing these macrophages with HCC cells showed that M1 cells inhibited HCC growth, while M2 cells promoted it. Screening for non-toxic DMAKN concentrations revealed its ability to induce M1 polarization and enhance LPS/IFN-γ-induced M1 macrophages, both showing anti-HCC effects. Conversely, DMAKN suppressed IL-4/IL-13-induced M2 polarization, inhibiting M2 macrophages' promotion of HCC cell viability. In summary, DMAKN induces and enhances M1 polarization while inhibiting M2 polarization of macrophages, thereby inhibiting HCC cell growth. These findings suggest that DMAKN has the potential to regulate TAMs in HCC, offering promise for future therapeutic development. [ABSTRACT FROM AUTHOR]

Dimensionality reduction is usually involved in the domains of artificial intelligence and machine learning. Linear projection of features is of particular interest for dimensionality reduction since it is simple to calculate and analytically analyze. In this paper, we propose an essentially linear projection technique, called locality-preserved maximum information projection (LPMIP), to identify the underlying manifold structure of a data set. LPMIP considers both the within-locality and the between-locality in the processing of manifold learning. Equivalently, the goal of LPMIP is to preserve the local structure while maximize the out-of-locality (global) information of the samples simultaneously. Different from principal component analysis (PCA) that aims to preserve the global information and locality-preserving projections (LPPs) that is in favor of preserving the local structure of the data set, LPMIP seeks a tradeoff between the global and local structures, which is adjusted by a parameter [alpha], so as to find a subspace that detects the intrinsic manifold structure for classification tasks. Computationally, by constructing the adjacency matrix, LPMIP is formulated as an eigenvalue problem. LPMIP yields orthogonal basis functions, and completely avoids the singularity problem as it exists in LPP. Further, we develop an efficient and stable LPMIP/QR algorithm for implementing LPMIP, especially, on high-dimensional data set. Theoretical analysis shows that conventional linear projection methods such as (weighted) PCA, maximum margin criterion (MMC), linear discriminant analysis (LDA), and LPP could be derived from the LPMIP framework by setting different graph models and constraints. Extensive experiments on face, digit, and facial expression recognition show the effectiveness of the proposed LPMIP method. Index Terms--Dimensionality reduction, image recognition, linear projection, manifold learning, undersampled problem.

Abstract We extracted and isolated three natural styryl lactones from Goniothalamus griffithii Hook f. Thoms and investigated their cytotoxicity on a panel of three hepatocyte cell lines, HepG2, drug resistant [...]